We provide game developers the tools they need to protect and nurture their communities and create a more positive experience for their players

GGWP

ggwp

If you’re serious and competitive when it comes to gaming (like you should be), then it’s in your best interest to drop the antiquated quips and platitudes and get with the program. With proximity chat becoming ever more popular in some of the latest Battle Royale and First-Person Shooters, it has never been more important to know how to verbally thrash your opponent into oblivion after a good PK and teabag. “Are you saying they’re abdicating their duty to scrutinize me? Are you saying that they don’t have the guts to put the questions to me? Great supine protoplasmic invertebrate jellies!” Boris Johnson, Mayor of London Unlike Mr. Johnson, you don’t need to graduate at the top of your class from Oxford University to have a rich gaming vocabulary and to properly be able to articulate to your team that you’re responsible for the friendly-fire; due in-part to your nagging girlfriend. All you have to do is memorize this guide by heart and then you’ll never have to worry about another immature keyboard warrior giving you any more watercooler banter. The Complete List Gaming Slang Terms The most comprehensive and unadulterated list of gaming slang terms below is in alphabetical order, and applies to traditional games as much as it does to the more novel . Finding any particular phrase or word that you may be looking for is a complete non-issue. Web3 games The alphabetically ordered gaming terminology list is also segmented by genres so you don’t have to spend to much of your time and attention on thoroughly understanding how to deal with scrubs. General Gaming Terms – a PC gaming technology that aims to deliver better image quality without adding any additional strain on a graphics card. This results in higher frame rates and a better overall quality of game. Deep Learning Super Sampling (DLSS) – a setting that determines how much of your in-game surroundings you can actually see. Field of View (FOV) – a genre of games that feature a top-down player view and was originally popularized by DOTA. Multiplayer Online Battle Arena (MOBA) – the added immersion and ability to interact with in-game, cinematic cut-scenes. Quick Time Event (QTE) First-Person Shooter Gaming Terminology – when a highly skilled player goes berserk and annihilates all members of an opposing team all by themselves. ACE – players refer to a ranged weapon’s (typically a gun but could be a bow and arrow) ADS when discussing how long (in seconds or milliseconds) it takes to aim down the sight of a weapon when right clicking or pressing the left bumper on a controller. Aim Down Sights (ADS) – what players refer to characters as in the FPS VALORANT. Agent (VALORANT) – The AWP is a military spec sniper rifle that’s used by the Arctic Warfare Police in CS:GO. Those who use the AWP are known as AWPers and are feared due to their ability to quick-scope headshots. AWPer (CS:GO) – some FPS games have a game mode known as search and destroy. The bombsite is where one of the teams has to plant and detonate a bomb in order to successfully win the round. Bombsite – Non-Player Characters (NPCs) that are automata and typically act entirely based on a predetermined ruleset that is programmed into the game. Advanced bots can be governed by an artificial intelligence that is less predictable. Bots – the act of rapidly jumping and crouching in rapid succession so as to avoid getting hit by the enemy bullets. Bunny Hopping – someone who hides after spawning in-game instead of heading to the frontlines like all of the other players. In some instances, camping may be advantageous to those who provide sniper support fire. Camper – if rounds in an require the use of resources, some teams may agree to play a few practice rounds and minimize resource spend in order to save the remaining resources for future rounds. Economy Round FPS game – someone who aggressively attacks and kills an enemy member of the opposing forces. Frag – this is short for “get wrecked”. This is an insulting term that is acceptable to use upon defeating your opponent. Get Rekt – means “Good Game. Well Played.” GGWP – means “Good Luck, Have Fun.” GLHF – any player who hacks to gain an advantage and violates a game’s terms of service is deemed a haxor. Such individuals are less-than-adequate and will typically ruin the game for everyone else playing with them. Haxor – stands for elite and usually refers to players who are highly skilled and well geared. Leet – players who use alphanumeric and special characters in rapid succession to substitute the original grammar (i.e. l337, h4xor, etc.). L33t Speak – a player who thinks they’re L337 but in fact is rather horrendous in practice. A noob will usually be on the south-end of a leaderboard and may ragequit if triggered. Noobxor (noob) – usually means “please”, but can be used to annoy other players (“noobxor plox”). Plox – when a player flawlessly destroys another player without any loss of health. Pwned – players who are noobs and can’t handle the mental anguish of constantly losing to those better than they are may ragequit. Ragequitting is the act of vehemently turning off the console and throwing controllers through walls while profusely yelling unintelligible expletives at those who may be in the player’s vicinity. Ragequit – unlike a n00b, a scrub is a player who has been playing for a long time yet their skills haven’t improved in the slightest. Scrub – some rounds limit all of the players’ choice of weapons to only shotguns. Shotties Only – this refers to a round in a game that is stacked entirely to one-side. A “stomp” is preclusion to a round that is unbalanced and won’t prove to be a challenge to the team that has all of the best players. Stomp – someone who crouches on a deceased enemies face over and over again after a kill for the kill cam is known as a teabagger; for obvious reasons. Teabag MMORPG Gaming Terminology – In Player-versus-Environment (PvE), the additional enemy NPCs that spawn in large numbers during a main fight or battle; typically during a complex boss fight. Adds – stands for “Away From Keyboard”. Players who are idle in-game and are distracted with other unimportant activities are considered to be Away From Keyboard. In some MMORPGs, if you’re AFK for too long, the server will automatically boot you. AFK – also known as the “Aggro Radius”; this is the invisible radius around your in-game character that will provoke NPC mobs to engage you and begin attacking you in battle. Aggro – short for “Area of Effect”. This refers to skills, spells, abilities, and buffs that effect a certain area as opposed to a specific targeted NPC or player. AoE – short for “Damage per Second”. The amount of damage that a player can inflict on NPCs or other players in a single battle is represented as the DPS metric. There are two forms of DPS; Melee and ranged (guns and spells) DPS. DPS – often resembling bullying, this is when a group of players team up against another single player to take them out and collect their loot. Gank – players request heals in combat from other friendly players who have the ability to remedy the loss of health. Heals may take the form of spells and buffs or resources such as potions, bandages, and med-kits. Heals – the ability for a player to keep a boss or mob aggroed at a distance while moving around in a pattern that avoids taking substantial damage. Kite – stands for “Looking for Group”. In MMORPGs, players will look for or announce in chat that they are seeking to either start of join a group of other players to complete a task that they alone cannot do; such as a quest, a dungeon, or a raid. LFG – stands for “Looking for Party”. A common phrase that is used interchangeably with Looking for Group (LFG). LFP – these are standard PvE enemy NPCs that exist within MMORPG worlds. Mobs – buffs are passive and temporary spells that boost player stats and skill sets. When a player requests to stack buffs, they are requesting that another player adds additional buffs to their character in order to multiply the positive effects caused the buffs. Stack Buffs – a tank is an in-game character that takes all of the damage in a party. Tank – provides the ability for a player to change their avatar’s appearance while retaining stats of the original gear. Transmog – common in-game loot that has such a large supply that the marketplace has deemed the item or resource in question practically worthless. Vendor Trash – a common tactic used by n00bs who find power in numbers. These are typically brutes and are far from being skilled tacticians as the only way for them to find solace is to overpower an enemy with a large group of people. Zerg Gaming Terminology Recap You’re now equipped with the latest and most commonly used gaming slang terms that the pros use on the daily. Whether you’re playing a riveting and fast-paced First-Person Shooter or you’re slowly grinding for experience points in an MMORPG, you’ll know how to respond to a fellow gamer without appearing like a complete noob. Use these gamer slang terms in combination with IRL chatter for best results. Also published . here

A Comprehensive List of Gaming Slang Terms That You’ll Ever Need

This paper is available on arxiv under CC 4.0 license. Authors: (1) Md Masud Rana, Department of Mathematics, University of Kentucky; (2) Duc Duy Nguyen, Department of Mathematics, University of Kentucky & ducnguyen@uky.edu. Table of Links Abstract & Introduction Datasets and Results Methods Conclusion, Data and Software Availability, Competing interests, Acknowledgments & References Abstract Protein-protein interactions (PPIs) are critical for various biological processes, and understanding their dynamics is essential for decoding molecular mechanisms and advancing fields such as cancer research and drug discovery. Mutations in PPIs can disrupt protein binding affinity and lead to functional changes and disease. Predicting the impact of mutations on binding affinity is valuable but experimentally challenging. Computational methods, including physics-based and machine learning-based approaches, have been developed to address this challenge. Machine learning-based methods, fueled by extensive PPI datasets such as Ab-Bind, PINT, SKEMPI, and others, have shown promise in predicting binding affinity changes. However, accurate predictions and generalization of these models across different datasets remain challenging. Geometric graph learning has emerged as a powerful approach, combining graph theory and machine learning, to capture structural features of biomolecules. We present GGL-PPI, a novel method that integrates geometric graph learning and machine learning to predict mutation-induced binding free energy changes. GGL-PPI leverages atom-level graph coloring and multi-scale weighted colored geometric subgraphs to extract informative features, demonstrating superior performance on three validation datasets, namely AB-Bind, SKEMPI 1.0, and SKEMPI 2.0 datasets. Evaluation on a blind test set highlights the unbiased predictions of GGLPPI for both direct and reverse mutations. The findings underscore the potential of GGL-PPI in accurately predicting binding free energy changes, contributing to our understanding of PPIs and aiding drug design efforts. — geometric graph, machine learning, protein-protein interactions, mutation, binding free energy changes Keywords 1 Introduction Protein-protein interactions (PPIs) play a fundamental role in numerous biological processes, including cell signaling, metabolic pathways, and immune responses [1;2;3]. Understanding PPIs and their dynamics is crucial for unraveling the intricate mechanisms underlying these processes and holds significant implications for various fields, such as cancer research, drug discovery, and personalized medicine [3;4]. The effects of mutations on PPIs have drawn substantial attention due to their potential impact on protein function and cellular behavior [5;6;7;8]. Missense mutations, which involve single amino acid substitutions, can disrupt the binding affinity between proteins and their partners [9;10]. Such alterations can lead to malfunctioning PPI networks, resulting in diseases, drug resistance, or other molecular disorders [11;12;13;14;15;16]. Therefore, accurate prediction of the impact of mutations on binding affinity holds significant importance in understanding disease mechanisms, facilitating therapeutic interventions, and enabling the design of innovative biopharmaceuticals. One of the key parameters used to assess the impact of mutations on PPIs is the binding free energy change (∆∆G). This thermodynamic parameter quantifies the difference in binding affinity between the wild-type and mutant protein complexes. Experimental determination of ∆∆G values, while accurate, can be tedious and costly. Consequently, there has been a surge in the development of computational methods to predict these energy changes. Broadly, these computation approaches fall into two main categories: physics-based and machine learning-based methods. The former, rooted in biophysical principles, delves into protein conformations and offers a rigorous approach [17;18;19]. However, they often demand significant computational resources and are not always scalable. On the other hand, machine learning-based methods have gained popularity due to their scalability and rapid prediction capabilities. Leveraging the wealth of data from PPI datasets such as ASEdb [20], PINT[21], ProTherm[22], SKEMPI [23;24], and others [25;26;27], machine learning models like mCSM28, BindProf 6 , iSEE29, MutaBind [7] , and several others [30;31;32;33] have been developed. These models have shown significant potential in predicting ∆∆Gs. However, challenges such as imbalanced training datasets, generalization across different PPI datasets, and the intricacy of capturing complex sequence-structure-function relationships remain obstacles [34;35;36;37]. This underscores the need for further research to enhance machine learning methodologies, ensuring accurate and efficient ∆∆G predictions. In recent years, geometric graph learning has emerged as a promising approach for analyzing complex biomolecular systems [38;39;40]. By representing proteins and their interactions as graphs, this methodology leverages the power of graph theory and machine learning to capture essential structural and spatial features of the biomolecular complexes. Specifically, the use of geometric subgraphs, which encode local interactions between atoms and residues, offers a rich representation. This not only sheds light on intricate molecular details but also provides insights into their impact on binding affinity [39]. This work presents a novel method, called GGL-PPI (Geometric Graph Learning for Protein-Protein Interactions), which combines the principles of geometric graph learning and machine learning to predict mutation-induced binding free energy changes. The workflow of GGL-PPI is depicted in Figure 1. Central to its methodology, GGL-PPI utilizes atom-level graph coloring and multi-scale weighted colored geometric subgraphs, enabling the extraction of informative features from protein structures and their interactions. These features serve as inputs to a gradient-boosting tree model, which facilitates precise and consistent predictions of binding free energy change upon mutations. When compared with existing models, GGL-PPI consistently outperforms state-of-the-art approaches across all datasets. Further addressing its generalizability, GGL-PPI was evaluated on a blind test set, Ssym dataset [36]. This evaluation was conducted using a homology-reduced balanced training set to avert data leakage, showcasing GGL-PPI’s robust performance and ability to produce unbiased predictions for both direct and reverse mutations. This paper is available on under CC 4.0 license. Arxiv

GGL-PPI: Geometric Graph Learning to Predict Mutation-Induced Binding Free Energy Changes

Dedicated to The Tattered Cover

Welcome to my blog! As a seasoned software developer with extensive experience in Python, data science, and machine learning, I'm excited to share with you 20 essential libraries that will help elevate your skills in data mining, data visualization, and data processing. Let's dive right in! Python 1. Pandas is a powerful, open-source library that provides data manipulation and analysis tools for Python. It is particularly well-suited for handling structured data. Description: Pandas Pandas offers , a convenient way to manipulate and analyze tabular data, similar to tables in a spreadsheet. Feature: DataFrames From data cleaning and preprocessing to simple data exploration and analysis, Pandas is widely used in various data science projects. Application: Pros: Intuitive syntax Excellent performance Extensive documentation Cons: High memory usage for large datasets Limited scalability Link to a project to learn better: 10 Minutes to Pandas 2. NumPy is a fundamental library for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices and includes a collection of mathematical functions. Description: NumPy NumPy's core feature is the , which is a powerful and versatile n-dimensional array object. Feature: ndarray NumPy is used extensively in data science, machine learning, and scientific computing for linear algebra, Fourier analysis, and more. Application: Pros: Efficient array operations Widely supported by other libraries Large community and ecosystem Cons: Steeper learning curve for beginners Less intuitive than Pandas for data manipulation Link to a project to learn better: NumPy Quickstart Tutorial 3. Scikit-learn Scikit-learn is a widely-used, open-source machine learning library that provides simple and efficient tools for data mining and data analysis. Description: Scikit-learn offers a comprehensive collection of machine learning algorithms, including classification, regression, clustering, and dimensionality reduction. Feature: Scikit-learn is widely used in industry and academia for building machine learning models and developing data-driven applications. Application: Pros: Easy-to-use API Extensive documentation and examples Active development and community support Cons: Not optimized for deep learning Limited GPU support Link to a project to learn better: Scikit-learn Tutorial: Wine Classifier 4. Matplotlib Matplotlib is a popular Python library for creating static, interactive, and animated visualizations in a variety of formats. Description: Matplotlib provides a high-level interface for drawing attractive and informative graphs, plots, and charts. Feature: Matplotlib is used for creating visualizations in data exploration, data analysis, and presentation of results. Application: Pros: Highly customizable Wide range of supported plots and charts Active community and development Cons: Complex API for advanced use-cases Less interactive compared to other libraries Link to a project to learn better: Matplotlib Pyplot Tutorial 5. Seaborn is a Python data visualization library based on Matplotlib that provides a high-level interface for creating statistical graphics. Description: Seaborn Seaborn comes with built-in themes, color palettes, and advanced functions for creating beautiful, easy-to-read plots with fewer lines of code. Feature: Seaborn is ideal for exploring and understanding data through visualization, particularly for statistical analysis and visualizing complex datasets. Application: Pros: Easy to use and intuitive Integrates well with Pandas DataFrames Excellent support for categorical data visualization Cons: Less customizable than Matplotlib Slower performance for large datasets Link to a project to learn better: Seaborn Tutorial: Visualizing the Titanic Dataset 6. Plotly Plotly is a powerful, interactive graphing library for Python, R, and JavaScript, allowing users to create visually stunning, web-based data visualizations. Description: Plotly supports a wide range of chart types, such as scatter plots, bar charts, and heatmaps, with interactive features like zoom, pan, and hover tooltips. Feature: Plotly is ideal for creating interactive dashboards, web applications, and sharing visualizations online. Application: Pros: Highly interactive Wide range of supported chart types Cross-platform compatibility Cons: Requires an internet connection for some features Can be slow for large datasets Steeper learning curve compared to other libraries Link to a project to learn better: Plotly Getting Started Guide 7. TensorFlow is an open-source machine learning library developed by Google, designed for high-performance numerical computation and deep learning. Description: TensorFlow TensorFlow provides an extensive and flexible ecosystem of tools, libraries, and community resources for building and deploying machine learning models. Feature: TensorFlow is widely used in research and production for deep learning applications, such as image and speech recognition, natural language processing, and reinforcement learning. Application: Pros: Scalable and high-performance GPU support for faster computation Active development and large community Cons: Steeper learning curve for beginners Can be complex for simple use-cases Link to a project to learn better: TensorFlow Quickstart for Beginners 8. Keras Keras is a user-friendly, high-level neural networks API, written in Python and capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, and others. Description: Keras provides a simple, modular, and extensible interface for building and training deep learning models with minimal code. Feature: Keras is widely used for prototyping and building deep learning models for various applications, such as computer vision, natural language processing, and more. Application: Pros: Easy to use and intuitive Supports multiple backends Large community and support Cons: Less flexible than TensorFlow for advanced use-cases Performance may be slower than lower-level libraries Link to a project to learn better: Keras Getting Started Guide 9. PyTorch is an open-source machine learning library developed by Facebook, offering a flexible deep learning framework with strong GPU acceleration. Description: PyTorch PyTorch provides an intuitive and dynamic interface for building, training, and deploying deep learning models, along with extensive support for tensor computation. Feature: PyTorch is popular in research and industry for deep learning, computer vision, natural language processing, and reinforcement learning applications. Application: Pros: Dynamic computation graph Strong GPU support Active development and large community Cons: Steeper learning curve than Keras Less mature ecosystem compared to TensorFlow Link to a project to learn better: PyTorch 60 Minute Blitz 10. Dask Dask is a parallel computing library for Python that enables users to harness the power of multi-core processors and distributed computing clusters. Description: Dask provides a flexible and efficient way to parallelize operations on large datasets, offering parallelized versions of NumPy arrays, Pandas DataFrames, and more. Feature: Dask is well-suited for out-of-core and distributed computing tasks, such as big data processing, machine learning, and advanced analytics. Application: Pros: Scales seamlessly from single-core to multi-core and distributed systems Integrates well with existing Python libraries Highly customizable and extensible Cons: Steeper learning curve for advanced features Some limitations compared to native Pandas and NumPy functionality Link to a project to learn better: Dask Tutorial 11. Scrapy Scrapy is an open-source web crawling framework for Python that allows you to extract data from websites easily and efficiently. Description: Scrapy provides a robust and extensible architecture for building web spiders, with built-in support for handling common web scraping tasks like logging in and handling cookies. Feature: Scrapy is ideal for web scraping, data mining, and extracting structured data from websites for further processing and analysis. Application: Pros: Flexible and powerful Extensible through plugins Comprehensive documentation Cons: Steeper learning curve for beginners Overkill for small-scale scraping projects Link to a project to learn better: Scrapy Tutorial: Quotes Spider 12. Beautiful Soup is a Python library designed for web scraping purposes to pull data out of HTML and XML files. Description: Beautiful Soup Beautiful Soup provides an easy-to-use interface for parsing HTML and XML documents, making it simple to navigate, search, and modify the parse tree. Feature: Beautiful Soup is widely used for web scraping tasks, such as extracting information from websites, cleaning and preprocessing text data, and more. Application: Pros: Easy to use and beginner-friendly Robust parsing capabilities Handles malformed HTML gracefully Cons: Slower parsing compared to other libraries Limited support for advanced web scraping tasks Link to a project to learn better: Beautiful Soup Quickstart Guide 13. XGBoost XGBoost (eXtreme Gradient Boosting) is a highly efficient and scalable implementation of gradient-boosted decision trees, designed for speed and performance. Description: XGBoost provides a flexible and parallelized boosting library, with support for various loss functions, regularization, and early stopping. Feature: XGBoost is widely used in machine learning competitions and real-world applications for its exceptional performance in classification, regression, and ranking problems. Application: Pros: Fast and efficient Handles large datasets with ease Regularization and early stopping features Cons: Can be prone to overfitting Requires careful tuning of hyperparameters Link to a project to learn better: XGBoost Tutorial: Boston Housing Dataset 14. OpenCV OpenCV (Open Source Computer Vision Library) is a powerful and widely-used library for real-time computer vision, image processing, and machine learning. Description: OpenCV provides a rich set of functions and algorithms for image and video processing, feature extraction, object detection, and machine learning. Feature: OpenCV is extensively used in applications such as robotics, self-driving cars, augmented reality, facial recognition, and more. Application: Pros: Efficient and optimized performance Wide range of supported algorithms Active community and development Cons: Steeper learning curve for beginners Limited high-level API compared to other libraries Link to a project to learn better: OpenCV Python Tutorials 15. ggplot ggplot is a Python data visualization library based on R's ggplot2 and the Grammar of Graphics. It aims to provide a simple and concise way to create beautiful, customizable plots. Description: ggplot offers a declarative approach to visualization, allowing you to build complex plots by adding layers, scales, and themes to a base plot object. Feature: ggplot is ideal for creating static, publication-quality visualizations for data exploration, analysis, and presentation. Application: Pros: Elegant and expressive syntax Highly customizable Consistent and coherent plotting system Cons: Less mature than Matplotlib and Seaborn Limited interactivity compared to other libraries May have a learning curve for those unfamiliar with the Grammar of Graphics ggplot: Getting Started Link to a project to learn better: Conclusion These 15 essential Python libraries will help you tackle various tasks in data science and machine learning, from data mining and visualization to data processing. With a solid foundation in these tools, you'll be well on your way to becoming a successful data scientist or machine learning engineer. So, start exploring these libraries, work on the linked projects, and expand your skill set! Happy coding!

15 Essential Python Libraries for Data Science and Machine Learning

You’re been working on a project in RStudio for days and are knee-deep in code and loaded libraries. Things are going great…until they aren’t anymore 🙄. Suddenly, the dreaded error messages pop up when you try to utilize a library you’ve been using effortlessly so far. You decide to ignore it for now and worry about it later. But the next time you open your RStudio session, another problem arises. It’s time to update your RStudio software. You reason with yourself and decide to take care of it now because you know all too well, these issues and indicators ONLY KEEP COMING 😱. These problems can be scary and intimidating when you’re in the middle of a workflow, even for the most experienced programmers. For one, you have to disrupt the ‘ ’ that you have magically achieved. No one wants to stop what they’re doing so they can jump into troubleshooting mode. flow state Second, programming can be finicky and there are many moving pieces. Once you’ve found a working method, you just don’t want to mess with it. Updating your software leads to updating your libraries and it’s impossible to know if those updates may lead to bugs in your code. Table of Contents: What is R and RStudio? Why you should keep your software updated. How to check your current version of R and RStudio. Sidetrack - Updating R packages. How to update R. How to update RStudio. Troubleshooting. R and RStudio - What’s the Difference? R is the open-source programming language itself, and is the integrated development environment (IDE) that allows users to and have easy access to tools, data, and visualizations in a tidy console-like environment. Unfortunately, you have to update them independently. Although, you can get away with only updating RStudio most of the time and leaving R alone. RStudio code with R Why You Should Stay up to Date A common reaction to this scenario is to put off the updates and “think about it later”. That might work for a little while, but here’s why you should just deal with it right away and keep your software updated. Benefit from the latest new features of RStudio. Repair issues that you’re having. Remedy underlying bugs that RStudio programmers have updated, even if they haven’t affected your workflow (yet). Eliminate those pesky warning messages. Maintain R and RStudio in a happy state of being together. How To Check Your Current Version of R and RStudio There are a few methods to check your current software version(s). It’s good to take note of the version(s) you are using now, just in case you run into any issues and decide you want to revert backward. This is unlikely, but you might as well be cautious. RStudio - Method 1: Mostly, you’ll see a pop-up window like the one below when you open RStudio. This tells you your current version and which version is available. RStudio - Method 2: If you’re not receiving these update indications, you can still check for updates manually. First, you navigate to the and select (highlighted in yellow below). Help tab Check for Updates Clicking this will prompt the same window as before to pop up. R - Method 1: To check your current version of R, type into the console (seen in blue font below). Running the command will yield the following results. The results show that R version 4.2.1 is currently in use. sessionInfo() R - Method 2: Things are getting redundant now, but the easiest method to check your version of R (and the funniest) is to simply open RStudio, look to the console, and read the text displayed.You’ll notice the project team that developed this version gave it a humorous name. In this case, R version 4.2.1 is also known as “Funny-Looking-Kid” (highlighted in yellow below). 😭 A Sidetrack You might not be ready to update RStudio altogether, but you’re receiving error messages and bugs related to packages. Keep in mind you can try updating the troublesome package and see if that remedies the error. Navigate to your where you can see a list of RStudio packages. Click the (see image below). Packages tab Update button After clicking on the , a new window will appear like the image shown below. The new window informs you of the package version you have installed currently and which version is available to be installed. Update Packages tab You can also click on the for the package of interest to see what has been updated. Clicking this button will open a browser with a list of information. For example, clicking reveals numerous changes and bug fixes in the new version (see the image below). NEWS button ggplot2’s NEWS button Updating Packages: Method 1: Update Every Package Click at the bottom to select all packages. Select All Click . Install Updates Method 2: Update Only Select Packages Click the box next to the package(s) you wish to update. Select . Install Updates A warning prompt requesting to restart R will appear, click . Yes After the update is complete, you will notice the changes listed in the console. Your package version should be updated within the list of packages. How to Update R If you’re planning on updating both R and RStudio, it’s more logical to update R first but you can update them in either order. If you’re only planning on updating RStudio, proceed to the next section. Navigate to and look up the latest version of R. To download the latest version, you’ll have to choose your which is sorted regionally. CRAN is a network of servers that store the latest versions of R. They ask to choose the mirror “nearest to you to minimize network load”. Download and install the file. r-project CRAN mirror Confirm that you have successfully installed the latest version and move on to updating RStudio. The image below shows that R has been updated to version 4.2.3 “Shortstop Beagle”. How to Update RStudio Now you know what version you are running, what next? From the updater popup window, click on . This action will close your RStudio session and will open automatically in your browser. Quit and Download RStudio Desktop - Posit Download the latest version, open the installation file, and you will be greeted with the RStudio Setup window. Next, you can select your Start Menu Folder, and which version of R you would like to use. If you have installed the latest R, select the specific version. After updating, open a new session of RStudio and check for updates. You should see the following window. Troubleshooting Both R and RStudio should be updated now. But your console session in RStudio reads that you are still using the old version of R. What gives? You may have to do one last step to select the updated version of R. In RStudio, under the , select . Tools tab Global Options On the next window, under the section, select the correct directory for your updated R version by clicking the button. R General Change On the next window that appears, select the updated version of R. Finally, close your RStudio session, reopen it, and inspect the console. It should read the latest version of R installed. In my case, I am now using R 4.2.3 or “Shortstop Beagle”. Final Thoughts Now you have learned how to update R, RStudio, and your packages. Things should be smooth sailing now and you can get back into working in the zone. Note: This tutorial was performed on a Windows computer. Lead image source: stable diffusion v2.1 with prompt “Beagles playing baseball”.

Be a Shortstop Beagle: Learn How to Update R and RStudio to the Latest Version

শর্টস্টপ বিগল হও: সর্বশেষ সংস্করণে R এবং RStudio আপডেট করতে শিখুন

Seien Sie ein Shortstop Beagle: Erfahren Sie, wie Sie R und RStudio auf die neueste Version aktualisieren

Conviértase en un campocorto Beagle: aprenda cómo actualizar R y RStudio a la última versión

Soyez un Beagle d'arrêt-court : apprenez à mettre à jour R et RStudio vers la dernière version

शॉर्टस्टॉप बीगल बनें: R और RStudio को नवीनतम संस्करण में अपडेट करना सीखें

ショートストップ ビーグルになる: R と RStudio を最新バージョンに更新する方法を学ぶ

유격수 비글이 되어 보세요: R 및 RStudio를 최신 버전으로 업데이트하는 방법 알아보기

Seja um Shortstop Beagle: Aprenda como atualizar R e RStudio para a versão mais recente

Kısa Stoper Beagle Olun: R ve RStudio'yu En Son Sürüme Nasıl Güncelleyeceğinizi Öğrenin

Trở thành một Beagle Shortstop: Tìm hiểu cách cập nhật R và RStudio lên phiên bản mới nhất

成为游击手比格犬：了解如何将 R 和 RStudio 更新到最新版本

If you’re seeing this interview draft, it means you’ve recently published on HackerNoon . For this reason, we would like to help the community get to know you better as well as find out some writing tips from you. a story that the community found interesting and/or valuable While this template is automatic, our interest in the answers below is genuine and our human editors (and some cyborg wannabes) will review it before publishing. So let’s start! Tell us a bit about yourself. For example, name, profession, and personal interests. Hello techsters and netizens! I am the Editor of a tech blog, The Tech Panda, and a writer who accidentally fell into the tech world and now can’t stop gawping at all the new-fangled stuff that happens every day. Interesting! What was your latest Hackernoon Top story about? My was about ChatGPT breaking bad after it was released onto the playground that’s the Internet, reflecting humanity’s bias and deep-seated flaws. latest story Do you usually write on similar topics? If not, what do you usually write about? Well, ChatGPT is the hottest topic right now, and like all other techs, it was baffling enough to pique my interest. Usually, AI is one of my fav topics, along with crypto, robotics, esports, and any other tech that impacts society. I am also working on a sci-fi fantasy novel right now, fiction being my original passion. Great! What is your usual writing routine like (if you have one?) Copious amounts of reading news, books, and blogs help me keep up. While reading so much, thoughts are bound to rush in, and that’s when I put them into words. Being a writer in tech can be a challenge. It’s not often our main role, but an addition to another one. What is the biggest challenge you have when it comes to writing? Tech is indeed a challenge for me, coz I am from a literature background. My biggest challenge is making sense of the tech enough to be able to connect it with real life. At such times, an interview with an expert helps. What is the next thing you hope to achieve in your career? I am always aiming for that story that breaks the internet. Also, I, one day, want to be interviewed for having written the best book. Wow, that’s admirable. Now, something more casual: What is your guilty pleasure of choice? Cozying up with a book with cuppa Joe and a box of chocolates is my fav pass time. Other than that, I enjoy goofing around with my family a lot. My teenage son and I often read aloud books assuming different characters. I cherish those moments. Do you have a non-tech-related hobby? If yes, what is it? When it comes to hobbies, I am quite versatile; apart from writing stories and poetry, I love to sew, cook, craft, paint, and have a few strange collections. What can the Hacker Noon community expect to read from you next? There are definitely more ChatGPT stories coming up. Also, I am working on a piece on social media. What’s your opinion on HackerNoon as a platform for writers? HackerNoon is a boon for writers like me who aren’t that good with social media and spreading the word. The platform is easy to use, and their interactions are fun. Thanks for taking time to join our “ ” series. It was a pleasure. Do you have any closing words? Meet the writer This has been fun. I hope to keep audiences thrilled with my future writing.

Meet the Writer: HackerNoon's Contributor Navanwita Bora Sachdev, Editor

Python and R are two of the most popular programming languages used for statistical analysis, data analysis and machine learning. Both languages offer a rich collection of tools and libraries for working with data and are widely used in academia, research and industry. R is more focused on statistical analysis and has a simpler syntax, while Python has versatility, which enables it to have a broader range of applications and libraries. In this article, we will focus on the , including classification, regression analysis, clustering and time series analysis. Best Pre-Installed R Datasets Commonly Used for Statistical Analysis Pre-installed datasets are datasets that come with a piece of software or a platform. In R, these datasets provide a convenient way for users to get started with statistical analysis and machine learning without having to spend time searching for or creating their datasets. Ultimate List of Pre-Installed R Datasets 1. Mtcars This dataset includes information about various car models and their performance characteristics. The mtcars dataset is also derived from the 1974 Motor Trend US magazine and comprises 32 observations on 11 variables. The variables include: Mpg - The mpg variable represents miles per gallon (mpg) for the car model. Cyl - The cyl variable represents the number of cylinders in the engine of the car model. Disp - The disp variable represents the displacement of the car model, which is the volume of air and fuel mixture that the engine can compress and burn in one cycle, in cubic inches. Hp - The hp variable represents the gross horsepower of the car model. Drat - The drat variable represents the rear axle ratio of the car model. Wt - The wt variable represents the weight of the car model, in thousands of pounds. Qsec - The qsec variable represents the time taken to cover a quarter-mile distance from a standing start. Vs - The vs variable represents the type of engine (0 = V-shaped, 1 = straight). Am - The am variable represents the type of transmission (0 = automatic, 1 = manual). Gear - The gear represents the number of forward gears in the car model. Carb - The carb variable represents the number of carburettors in the engine of the car model. The can be loaded into R by typing data(mtcars), or it can be downloaded by clicking . Mtcars dataset here . 2 ChickWeight The ChickWeight, or chickwts, dataset includes information about the weight of chickens over time. The dataset also has 578 observations on 4 variables. The variables include: Weight - The weight variable represents the weight of the chicken in grams. Time - The time variable represents the time in days at which the weight was measured. Chick - The chick variable represents the ID of the chicken. Diet - The diet variable represents the type of diet that the chicken was fed during the experiment. The dataset can be downloaded into R by typing data(ChickWeight), or it can be downloaded by clicking . ChickWeight here 3. CO2 The CO2 dataset includes measurements of atmospheric carbon dioxide (CO2) concentrations at the Mauna Loa Observatory in Hawaii, taken from March 1958 to December 2001. The dataset also has 468 observations on 2 variables. The variables include: CO2 - This variable represents the atmospheric CO2 concentration in ppm, as measured at the Mauna Loa Observatory in Hawaii. Plant - This variable represents the type of plant on which the CO2 concentration was measured. There are two types of plants: "Quebec" and "Mississippi." The can be downloaded into R by typing data(CO2), or it can be downloaded by clicking . CO2 dataset here 4. Iris This dataset includes measurements of the sepal length, sepal width, petal length and petal width of 150 iris flowers, which belong to 3 different species: setosa, versicolor and virginica. The iris dataset has 150 rows and 5 columns, which are stored as a dataframe, including a column for the species of each flower. The variables include: Sepal.Length - The sepal.length represents the length of the sepal in centimetres. Sepal.Width - The sepal.width represents the width of the sepal in centimetres. Petal.Length - The petal.length represents the length of the petal in centimetres. Species - The species variable represents the species of the iris flower, with three possible values: setosa, versicolor and virginica. The can be loaded into R by typing data(iris), or it can be downloaded by clicking . Iris dataset here 5. Boston Housing The Boston Housing dataset includes housing prices and related factors in the Boston area. The dataset was obtained from information collected by the U.S. Census Service concerning housing in the area of Boston, Massachusetts. The dataset also comprises 506 observations on 14 variables. The variables include: Crim - The crim variable represents the per capita crime rate by town. Zn - The zn variable represents the proportion of residential land zoned for lots over 25,000 sq.ft. Indus - The indus variable represents the proportion of non-retail business acres per town. Chas - The chas variable represents whether the property is located along the Charles River or not. Nox - The nox variable represents the nitrogen oxides concentration (parts per 10 million) in the air. Rm -The rm variable represents the average number of rooms per dwelling. age: This variable represents the proportion of owner-occupied units built prior to 1940. Dis -The dis variable represents the weighted distances to five Boston employment centres. Rad - The rad variable represents the index of accessibility to radial highways. Tax - The tax variable represents the full-value property-tax rate per $10,000. Ptratio - The ptratio variable represents the pupil-teacher ratio by town. Black - The black variable represents the proportion of blacks by town. Lstat - The lstat variable represents the percentage of the lower status of the population. Medv - The medv variable represents the median value of owner-occupied homes in $1000s. The can be loaded into R by typing data(Boston), or it can be downloaded by clicking . Boston Housing dataset here 6. Airquality This dataset includes daily air quality measurements in New York, between May to September 1973. It also consists of 153 observations on 6 variables. The variables include: Ozone - The ozone variable represents the maximum daily ozone concentration in parts per billion (ppb) measured at a monitoring station in New York. Solar - R: The solar.R variable represents the daily solar radiation in Langleys (a measure of solar energy) measured at the same monitoring station. Wind - The wind variable represents the average daily wind speed in miles per hour (mph). Temp - The temp variable represents the average daily temperature in degrees Fahrenheit. Month - The month variable represents the month in which the observation was made (a value between 5 and 9). Day - The day variable represents the day of the month on which the observation was made (a value between 1 and 31). The can be loaded into R by typing data(air quality), or it can be downloaded by clicking . Airquality dataset here 7. Titanic The Titanic dataset includes information about the passengers aboard the Titanic, which includes whether they survived or not. The dataset also contains 891 rows and 12 variables and is also based on the passenger list of the ill-fated maiden voyage of the Titanic, which sank in the North Atlantic Ocean on April 15th,1912, after colliding with an iceberg. The variables include: PassengerId - This variable represents the unique identifier for each passenger on the Titanic. Survived - This variable represents whether the passenger survived or not. A value of 0 indicates that the passenger did not survive, while a value of 1 indicates that the passenger did survive. Pclass - This variable represents the passenger's class of travel. There were three classes on the Titanic: first, second, and third. Name - This variable represents the passenger's name. Sex - This variable represents the passenger's gender. Age - This variable represents the passenger's age. SibSp - This variable represents the number of siblings/spouses that the passenger had on board the Titanic. Parch - This variable represents the number of parents/children that the passenger had on board the Titanic. Ticket - This variable represents the ticket number of the passenger. Fare - This variable represents the fare that the passenger paid for their ticket. Cabin - This variable represents the cabin number of the passenger. Embarked - This variable represents the port of embarkation for the passenger. There were three ports of embarkation on the Titanic: Cherbourg, Queenstown, and Southampton. The can be loaded into R by typing data(titanic), or it can be downloaded by clicking . Titanic dataset here 8. Faithful This dataset includes measurements of the eruption and waiting times between eruptions for the Old Faithful geyser in Yellowstone National Park. The Faithful dataset also contains 272 observations on 2 variables. The variables include: Eruption time - The eruption time variable represents the duration of the current eruption in minutes. Waiting time - The waiting time variable represents the length of time between the current and the previous eruption in minutes. The can be loaded into R by typing data(faithful), or it can be downloaded by clicking . Faithful dataset here 9. Orange The Orange dataset includes growth measurements of orange trees. The dataset also contains 35 observations on 3 variables. The variables include: Tree - A numeric vector that represents the tree number (1-5). Age - A numeric vector that represents the age of the tree (in years). Circumference - A numeric vector that represents the trunk circumference of the tree (in mm). The can be loaded into R by typing data(Orange), or it can be downloaded by clicking . Orange dataset here 10. PlantGrowth This dataset includes the results of an experiment on the effect of fertilizer on plant growth. The dataset also contains 30 observations on 2 variables. The variables include: Weight - A numeric vector that represents the weight of plants (in grams). Group - A factor variable that represents the fertilizer treatment group (either "ctrl" for the control group or "trt" for the treated group). The can be loaded into R by typing data(PlantGrowth), or it can be downloaded by clicking . PlantGrowth dataset here 11. Swiss The Swiss dataset includes socio-economic data for 47 French-speaking provinces of Switzerland during the early 1880s. It also consists of 47 observations across 6 variables. The variables include: Fertility - This variable represents the fertility rate (number of live births per 1000 women) in each canton. Agriculture - This variable represents the percentage of males in the canton classified as farmers. Examination - This variable represents the percentage of males in the canton who took the military examination. Education - This variable represents the percentage of people in the canton who were educationally qualified to attend university. Catholic - This variable represents the percentage of the population in the canton who are Catholic. Infant.Mortality - This variable represents the infant mortality rate (number of deaths of children under 1 year of age per 1000 live births) in each canton. The can be loaded into R by typing data(swiss), or it can be downloaded by clicking . Swiss dataset here 12. Women This dataset includes the heights of mothers and their daughters for American women aged 30-39. The dataset also consists of 15 observations on 2 variables. The variables include: Mother - A numeric vector that represents the height of the mother (in inches). Daughter - A numeric vector that represents the height of the daughter (in inches). The can be loaded into R by typing data(women), or it can be downloaded by clicking . Women dataset here Common Use Cases for Pre-Installed R Datasets - The Mtcars dataset is used for regression analysis and exploratory data analysis to study the relationship between car specifications and fuel efficiency. Mtcars - The ChickWeight dataset is used for analyzing longitudinal growth data, such as the effects of diet on the growth of chicks. ChickWeight - The CO2 dataset is used for analyzing the relationship between atmospheric CO2 concentration and plant growth. CO2 - The Iris dataset is used for exploratory data analysis and classification analysis to study the relationship between iris flower species and their physical attributes. Iris - The Boston Housing dataset is used for regression analysis to study the relationship between housing prices and various factors, such as crime rate and accessibility to public transportation. Boston Housing - The Airquality dataset is used for exploratory data analysis and regression analysis to study the relationship between air pollution and various weather factors. Airquality - The Titanic dataset is used for classification analysis and survival analysis to study the factors that influenced the survival of passengers on the Titanic. Titanic - The Faithful dataset is used for exploratory data analysis and modelling to study the patterns of eruption time and waiting time of the Old Faithful geyser in Yellowstone National Park. Faithful - The Orange dataset is used for regression analysis and growth modelling to study the growth of orange trees. Orange - The PlantGrowth dataset is used for hypothesis testing and ANOVA analysis to study the effect of different types of fertilizer on plant growth. PlantGrowth - The Swiss dataset is used for exploratory data analysis and hypothesis testing to study the relationship between fertility and socio-economic indicators in the provinces of Switzerland. Swiss - The Women dataset is used for exploratory data analysis and hypothesis testing to study the relationship between the height and weight of women. Women Final Thoughts These datasets can also be accessed through R packages like "tidyverse", "ggplot2" and "data.table” They are also available in public repositories such as Kaggle, GitHub and the UCI Machine Learning Repository. The lead image of this article was generated via HackerNoon's AI Stable Diffusion model using the prompt 'R Programming'. More Dataset Listicles: Hugging Datasets PyTorch Datasets Power BI Datasets

12 Best Pre-Installed R Datasets Commonly Used for Statistical Analysis

Requesting and receiving data from internet sources is one of the basics of programming but isn’t taught  by most ‘Learn to Code <Programming Language>’ courses. For self-taught programmers, this is one of the skills that will get you moving further along your learning journey and make you better than 90% of the others. What is an API? Short for Application Programming Interface, API helps a program fetch information from various internet sources. Let’s say you’re building a basic currency conversion program in python. It takes an input amount of currency X and converts it into currency Y Sample Code for reference: def currency_converter ():

    currency_a = input ("Enter the currency you want to convert: ")

    currency_b = input ("Enter the currency you want to convert to: ")

    amount = float (input ("Enter the amount you want to convert: "))

    exchange_rate = float (input ("Enter the exchange rate: "))

    print(f"{amount} {currency_a} is equal to \
        {amount * exchange_rate} {currency_b}")
    
currency_converter () This program in its current form is no more than a glorified calculator - not of much use as a program. This is where APIs come in!! With a few lines of code, this program can be made so powerful that it does not need to ask for the exchange rate. Instead, with APIs, the code would be able to fetch the current exchange rate and provide it to the program. Need to make a program to give the country code for an input country? There’s an API for that. Inventory in your warehouse? Data in your spreadsheet? Payment confirmation? Email notification? All done with APIs! To put it simply, APIs enable communications between computing devices in a standardized manner. For example, if you go to Germany and use an interpreter to help translate for you, the interpreter is the API. Components of an API . An API has 3 elements - Request, Response, and Resource Request - You asking the interpreter to say ‘Good Day’ to a German is the request Response - What the German said back is the response Resource - The data within the response (‘Guten Tag’) is the resource In our program above, let’s see what the API needs to do: """

Request -- Our program requests for the currency_a -> currency_b exchange rate

Response -- API endpoint reads our request, fetches that information, and responds back

Resource -- The exchange rate data sent back is the resource

""" API Endpoint? What’s that? As we know by now (as self-taught programmers) that computers need very precise inputs in a very precise manner. Any deviation or any expectation from the computer to ‘understand things as we do’ is just going to lead to chaos! In that vein, we must understand that even API requests need to be structured in a very precise way. This is what the general structure of an API looks like: -- This is the URL that receives the API Endpoint Request. -- This is the type of API request and is of several types such as: Method GET -- indicates that the request is looking to fetch info from the server. POST -- indicates that the request is looking to send some info to a server. DELETE -- indicates that the request is looking to delete some info from the server. PUT/PATCH -- indicates that the request is looking to update existing info on the server. -- This provides some context about the Requested. Header Resource -- This is the main part of the request that triggers the Body Response. CRUD Responses CRUD stands for Create, Read, Update, and Delete and is performed by using the relevant API method in the API Request: API Method Response Description GET Read fetches existing info from the server POST Create adds info to the server DELETE Delete deletes info on the server PUT/PATCH Update updates info on the server Fetching Realtime Currency Rates into our Program Now we’re sufficiently ready to fetch the real-time currency rates into our sample program given earlier. We’ll be using the requests module for this exercise which you can get via pip3 install requests in your code editor’s (like VSCode) terminal. APIs that provide data come in Free and Paid forms - depending on the ease of use. I found a free source and shall be using it because it does not force me to sign up as long as I’m just testing here. ExchangeRateAPI API data usually comes in the form of a JSON (very similar to a dictionary in python) for easy interpretation by the programmers fetching that data. Here’s the code for fetching the current price of the US Dollar via the API. import requests
url =  "https://open.er-api.com/v6/latest/USD"
r = requests.get(url)
data = r.json()
print (data) Running this code will output the following: {'result': 'success', 
'provider': 'https://www.exchangerate-api.com', 
'documentation': 'https://www.exchangerate-api.com/docs/free', 
'terms_of_use': 'https://www.exchangerate-api.com/terms', 
'time_last_update_unix': 1666828952, 
'time_last_update_utc': 'Thu, 27 Oct 2022 00:02:32 +0000', 
'time_next_update_unix': 1666916222, 
'time_next_update_utc': 'Fri, 28 Oct 2022 00:17:02 +0000', 
'time_eol_unix': 0, 
'base_code': 'USD', 
'rates': {'USD': 1, 'AED': 3.6725, 'AFN': 87.825832, 'ALL': 118.121569, 
'AMD': 400.945057, 'ANG': 1.79, 'AOA': 469.341287, 'ARS': 155.541325, 'AUD': 1.541873,
 'AWG': 1.79, 'AZN': 1.703727, 'BAM': 1.945846, 'BBD': 2, 'BDT': 101.652423, 
'BGN': 1.946569, 'BHD': 0.376, 'BIF': 2061.89311, 'BMD': 1, 'BND': 1.40599, 
'BOB': 6.931975, 'BRL': 5.314395, 'BSD': 1, 'BTN': 81.988812, 'BWP': 13.376498, 
'BYN': 2.887549, 'BZD': 2, 'CAD': 1.355432, 'CDF': 2080.191892, 'CHF': 0.986916, 
'CLP': 967.707002, 'CNY': 7.181598, 'COP': 4992.818412, 'CRC': 624.374829, 'CUP': 24, 
'CVE': 109.702113, 'CZK': 24.428037, 'DJF': 177.721, 'DKK': 7.422296, 'DOP': 53.948303,
 'DZD': 140.691654, 'EGP': 19.734057, 'ERN': 15, 'ETB': 53.10939, 'EUR': 0.994984, 
'FJD': 2.280573, 'FKP': 0.862476, 'FOK': 7.422296, 'GBP': 0.862495, 'GEL': 2.780813, 
'GGP': 0.862476, 'GHS': 14.477856, 'GIP': 0.862476, 'GMD': 60.196663, 
'GNF': 8658.722933, 'GTQ': 7.851475, 'GYD': 210.222337, 'HKD': 7.848858, 
'HNL': 24.756699, 'HRK': 7.496037, 'HTG': 127.760311, 'HUF': 407.402111, 
'IDR': 15518.182729, 'ILS': 3.502567, 'IMP': 0.862476, 'INR': 81.991335, 
'IQD': 1466.589463, 'IRR': 42121.422024, 'ISK': 142.934149, 'JEP': 0.862476, 
'JMD': 153.171604, 'JOD': 0.709, 'JPY': 146.478104, 'KES': 121.814832, 
'KGS': 83.278965, 'KHR': 4159.804229, 'KID': 1.541816, 'KMF': 489.456326, 
'KRW': 1414.443423, 'KWD': 0.29963, 'KYD': 0.833333, 'KZT': 470.670656, 
'LAK': 17297.850805, 'LBP': 1507.5, 'LKR': 362.205766, 'LRD': 154.022883, 
'LSL': 17.964161, 'LYD': 5.043916, 'MAD': 10.860577, 'MDL': 19.373927, 
'MGA': 4262.390442, 'MKD': 61.999441, 'MMK': 2425.598271, 'MNT': 3414.010756, 
'MOP': 8.084315, 'MRU': 38.145372, 'MUR': 43.95057, 'MVR': 15.474793, 
'MWK': 1033.698319, 'MXN': 19.911183, 'MYR': 4.709804, 'MZN': 64.481751, 
'NAD': 17.964161, 'NGN': 437.991325, 'NIO': 36.062907, 'NOK': 10.27411, 
'NPR': 131.1821, 'NZD': 1.717982, 'OMR': 0.384497, 'PAB': 1, 'PEN': 3.994793, 
'PGK': 3.535979, 'PHP': 58.319524, 'PKR': 220.984121, 'PLN': 4.728217, 
'PYG': 7249.701869, 'QAR': 3.64, 'RON': 4.844272, 'RSD': 116.906933, 
'RUB': 61.346183, 'RWF': 1075.383068, 'SAR': 3.75, 'SBD': 8.07199, 'SCR': 13.126076, 
'SDG': 572.688874, 'SEK': 10.875131, 'SGD': 1.405948, 'SHP': 0.862476, 
'SLE': 17.636956, 'SLL': 17636.95595, 'SOS': 571.245973, 'SRD': 29.467417, 
'SSP': 611.916602, 'STN': 24.374931, 'SYP': 2514.92413, 'SZL': 17.964161, 
'THB': 37.747911, 'TJS': 10.200292, 'TMT': 3.504191, 'TND': 3.085688, 
'TOP': 2.388017, 'TRY': 18.607129, 'TTD': 6.77608, 'TVD': 1.541816, 
'TWD': 32.049926, 'TZS': 2335.14976, 'UAH': 37.234725, 'UGX': 3826.845217, 
'UYU': 41.283595, 'UZS': 11139.838897, 'VES': 8.4508, 'VND': 24821.263771, 
'VUV': 122.039898, 'WST': 2.787278, 'XAF': 652.608434, 'XCD': 2.7, 'XDR': 0.774448, 
'XOF': 652.608434, 'XPF': 118.722827, 'YER': 251.106608, 'ZAR': 17.964381, 
'ZMW': 16.127703, 'ZWL': 644.87339}} Looking at this JSON (Imma call it a dictionary from hereon for pythonic reasons), we see that the currency rates against that of the USD as base currency are given in the internal dictionary which can be accessed with the key. rates The actual conversion rate to a particular currency can be accessed by using the desired currency’s 3-letter currency code. For example, to get the conversion rate to INR (Indian Rupee), I will first need to use the key and then the key within the key. rates INR rates Case-sensitivity is important in most APIs so we’ll need to plan for that in our code as well. The code to get the USD to INR conversion rate, the code in python would look like this - inr_rate = data['rates'].get('INR', 'Not Found') Let’s now update our original code to become less of a calculator and more of a computer: import requests

def currency_converter ():
    currency_a = input ("Enter the currency you want to convert: ")
    currency_a = currency_a.upper() 
    #accepts user input for base currency name and converts it to uppercase
    
    currency_b = input ("Enter the currency you want to convert to: ")
    currency_b = currency_b.upper() 
    #accepts user input for final currency name and converts it to uppercase
    
    currency_converter_api = "https://open.er-api.com/v6/latest/" + currency_a
    #makes currency_a the base currency
    
    api_response = requests.get(currency_converter_api)
    #fetches the api response - the R from CRUD
    
    api_response_json = api_response.json()
    #Converts the response object into a JSON object
    
    amount_to_convert = float (input (f"Enter the amount of {currency_a} \
      you want to convert to {currency_b}: "))
    #accepts user input for the amount to convert as a float
    
    try:
        exchange_rate = api_response_json['rates'].get(currency_b, \
          'Currency Not Supported by API')

        if exchange_rate == 'Currency Not Supported by API':

            print ("Final Currency Not Supported by API")

        else:

            converted_amount = amount_to_convert * exchange_rate

            print (f"{amount_to_convert} {currency_a} is \
              equal to {converted_amount} {currency_b}")
    
    except KeyError:
        print ("Base Currency Not Supported by API")
    # for catching errors is the base currency name is not supported by the API
    
currency_converter () There you go - now it does a little better: Converting 5000 INR to EUR (successful conversion calculation): """
Enter the currency you want to convert: inr
Enter the currency you want to convert to: eur
Enter the amount of INR you want to convert to EUR: 5000
5000.0 INR is equal to 60.72 EUR
""" Converting unsupported currency (error-handling): """
Enter the currency you want to convert: utsav
Enter the currency you want to convert to: inr
Enter the amount of UTSAV you want to convert to INR: 45
Base Currency Not Supported by API
""" Converting TO Unsupported currency (error-handling): """
Enter the currency you want to convert: inr
Enter the currency you want to convert to: utsav
Enter the amount of INR you want to convert to UTSAV: 89
Final Currency Not Supported by API
""" Some Final Notes: While we've used print statements here for illustrative reasons, if you’re building an app, avoid print statements and use return instead Some premium APIs (paid) offer better sources, the ability to take full-name inputs (Indian Rupee) or just country as input (USA) and respond with the desired response Most APIs require the use of API keys for authentication and DDoS prevention features. Read more about them . here Resource Attributions: RapidAPI #API on HackerNoon Recommended Readings: Low Code APIs Error-Handling in React APIs Using Google Sheets API in Python Securing your API data GraphQL API

API for Beginners: Requests, Responses, Resources, and GET Explained with Code

Astounding Stories of Super-Science, March 1930), by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here . Brigands of the Moon: Chapter  III. Astounding Stories of Super-Science, March 1930: Brigands of the Moon - Chapter  III. In the Helio-room AT six A. M., earth Eastern time, which we were still carrying, Snap Dean and I were alone in his instrument room, perched in the network over the Planetara’s deck. The bulge of the dome enclosed us; it rounded like a great observatory window some twenty feet above the ceiling of this little metal cubby-hole. The Planetara was still in the earth’s shadow. The firmament––black interstellar space with its blazing white, red and yellow stars––lay spread around us. The moon, with nearly all its disc illumined, hung, a great silver ball, over 317our bow quarter. Behind it, to one side, Mars floated like the red tip of a smoldering cigarillo in the blackness. The earth, behind our stern, was dimly, redly visible––a giant sphere, etched with the configurations of its oceans and continents. Upon one limb a touch of the sunlight hung on the mountain-tops with a crescent red-yellow sheen. And then we plunged from the cone-shadow. The sun, with the leaping Corona, burst through the blackness behind us. The earth lighted into a huge, thin crescent with hooked cusps. To Snap and me, the glories of the heavens were too familiar to be remarked. And upon this voyage particularly we were in no mood to consider them. I had been in the helio-room several hours. When the Planetara started, and my few routine duties were over, I could think of nothing save Halsey’s and Carter’s admonition: “Be on your guard. And particularly––watch George Prince.” I had not seen George Prince. But I had seen his sister, whom Carter and Halsey had not bothered to mention. My heart was still pounding with the memory.... WHEN the passengers had retired and the ship quieted, I prowled through the passenger corridors. This was about the trinight hour. Hot as the corridors of hell, with our hull and the glassite dome seething with the friction of our atmospheric flight. But the refrigerators mitigated that; the ventilators blasted cold air from the renewers into every corner of the vessel. Within an hour or two, with the cold of space striking us, it was hot air that was needed. [3] Dr. Frank evidently was having little trouble with pressure-sick passengers ––the Planetara’s equalizers were fairly efficient. I did not encounter Dr. Frank. I prowled through the silent metal lounges and passages. I went to the door of A 22. It was on the deck-level, in a tiny transverse passage just off the main lounging room. Its name-grid glowed with the letters: “Anita Prince.” I stood in my short white trousers and white silk shirt, like a cabin steward gawping. Anita Prince! I had never heard the name until this night. But there was magic music in it now, as I murmured it to myself. Anita Prince.... [4] She was here, doubtless asleep, behind this small metal door. It seemed as though that little oval grid were the gateway to a fairyland of my dreams. I turned away. And thought of the Grantline Moon Expedition stabbed at me. George Prince––Anita’s brother––he whom I had been told to watch. This renegade––associate of dubious Martians, plotting God knows what. ISAW, upon the adjoining door, “A 20, George Prince.” I listened. In the humming stillness of the ship’s interior there was no sound from these cabins. A 20 was without windows, I knew. But Anita’s room had a window and a door which gave upon the deck. I went through the lounge, out its arch, and walked the deck length. The deck door and window of A 22 were closed and dark. The ten-foot-wide deck was dim with white starlight from the side ports. Chairs were here, but they were all empty. From the bow windows of the arching dome a flood of moonlight threw long, slanting shadows down the deck. At the corner where the superstructure ended, I thought I saw a figure lurking as though watching me. I went that way, but it vanished. I turned the corner, went the width of the ship to the other side. There was no one in sight save the observer on his spider bridge, high in the bow network, and the second officer, on duty on the turret balcony almost directly over me. As I stood and listened, I suddenly heard footsteps. From the direction of the bow a figure came. Purser Johnson. He greeted me. “Cooling off, Gregg?” “Yes,” I said. He went past me and turned into the smoking room door nearby. I stood a moment at one of the deck windows, gazing at the stars; and for no reason at all I realized I was tense. Johnson was a great one for his regular sleep––it was wholly unlike him to be roaming about the ship at such an hour. Had he been watching me? I told myself it was nonsense. I was suspicious of everyone, everything, this voyage. IHEARD another step. Captain Carter appeared from his chart-room which stood in the center of the narrowing open deck space near the bow. I joined him at once. “Who was that?” he half-whispered. “Johnson.” “Oh, yes.” He fumbled in his uniform; his gaze swept the moonlit deck. “Gregg––take this.” He handed me a small metal box. I stuffed it at once into my shirt. “An insulator,” he added, swiftly. “Snap is in his office. Take it to him, Gregg. Stay with him––you’ll have a measure of security––and you can help him to make the photographs.” He was barely whispering. “I won’t be with you––no use making it look as though we were doing anything unusual. If your graphs show anything––or if Snap picks up any message––bring it to me.” He added aloud, “Well, it will be cool enough presently, Gregg.” He sauntered away toward his chart-room. “By heavens, what a relief!” Snap murmured as the current went on. We had wired his cubby with the insulator; within its barrage we could at last talk with a degree of freedom. “You’ve seen George Prince, Gregg?” “No. He’s assigned A 20. But I saw his sister. Snap, no one ever mentioned––” Snap had heard of her, but he hadn’t known that she was listed for this voyage. “A real beauty, so I’ve heard. Accursed shame for a decent girl to have a brother like that.” I could agree with him there, but I made no comment. IT was now 6 A. M. Snap had been busy all night with routine cosmo-radios from the earth, following our departure. He had a pile of them beside him. Many were for the passengers; but anything that savored of a code was barred. “Nothing queer looking?” I suggested. “No. Not a thing.” We were at this time no more than some sixty-five thousand miles from the moon’s surface. The Planetara presently would swing upon her direct course for Mars. There was nothing which could cause passenger comment in this close passing of the moon; normally we used the satellite’s attraction to give us additional starting speed. It was now or never that a message would come from Grantline. He was supposed to be upon this earthward side of the moon. While Snap had rushed through with his routine, I had searched the moon surface with our glass, as I knew Carter was searching it––and also the observer in his tower, very possibly. But there was nothing. Copernicus and Kepler lay in full sunlight. The heights of the lunar mountains, the depths of the barren, empty seas were etched black and white, clear and clean. Grim, forbidding desolation, this unchanging moon! In romance, moonlight may shimmer and sparkle to light a lover’s smile; but the reality of the moon is cold and bleak. There was nothing to show my prying eyes where the intrepid Grantline might be. “Nothing at all, Snap.” And Snap’s helio mirrors, attuned for an hour now to pick up the faintest signal, were motionless. “If he has concentrated any appreciable 319amount of radio-active ore,” said Snap, “we should get an impulse from its Gamma rays.” BUT our receiving shield was dark, untouched. We tried taking hydrogen photographic impressions of the visible moon surface. A sequence of them, with stereoscopic lenses, forty-eight to the second. Our mirror-grid gave the magnified images; the spectro-heliograph, with its wave-length selection, pictured the mountain-levels, and slowly descended into the deepest seas. There was nothing. Yet in those moon caverns––a million million recesses amid the crags of that tumbled, barren surface––the pin-point of movement which might have been Grantline’s expedition could so easily be hiding! Could he have the ore insulated, fearing its Gamma rays would betray its presence to hostile watchers? Or might disaster have come to him? Or he might not be upon this hemisphere of the moon at all.... My imagination, sharpened by fancy of a lurking menace which seemed everywhere about the Planetara this voyage, ran rife with fears for Johnny Grantline. He had promised to communicate this voyage. It was now, or perhaps never. Six-thirty came and passed. We were well beyond the earth’s shadow now. The firmament blazed with its vivid glories; the sun behind us was a ball of yellow-red leaping flames. The earth hung, opened to a huge, dull-red half-sphere. WE were within some forty thousand miles of the moon. Giant white ball––all of its disc visible to the naked eye. It poised over the bow, and presently, as the Planetara swung upon her course for Mars, it shifted sidewise. The light of it glared white and dazzling in our tiny side windows. Snap, with his habitual red celluloid eyeshade shoved high on his forehead, worked over our instruments. “Gregg!” The receiving shield was glowing a trifle! Gamma rays were bombarding it! It glowed, gleamed phosphorescent, and the audible recorder began sounding its tiny tinkling murmurs. Gamma rays! Snap sprang to the dials. The direction and strength were soon obvious. A richly radio-active ore body, of considerable size, was concentrated upon this hemisphere of the moon! It was unmistakable. “He’s got it, Gregg! He’s––” The tiny helio mirrors began quivering. Snap exclaimed triumphantly, “Here he comes! By God, the message at last! Bar off that light!” IFLUNG on the absorbers. The moonlight bathing the little room went into them and darkness sprang around us. Snap fumbled at his instrument board. Actinic light showed dimly in the quivering, thumbnail mirrors. Two of them. They hung poised on their cobweb wires, infinitely sensitive to the infra-red light-rays Grantline was sending from the moon. The mirrors in a moment began swinging. On the scale across the room the actinic beams from them were magnified into sweeps of light. The message! Snap spelled it out, decoded it. “Success! Stop for ore on your return voyage. Will give you our location later. Success beyond wildest hopes––” The mirrors hung motionless. The shield, where the Gamma rays were bombarding, went suddenly dark. Snap murmured, “That’s all. He’s got the ore! ‘Success beyond wildest hopes.’ That must mean an enormous quantity of it available!” We were sitting in darkness, and abruptly I became aware that across our open window, where the insulation barrage was flung, the air was faintly hissing. An interference there! I saw a tiny swirl of purple sparks. Someone––some hostile ray from the deck beneath us, or from the spider bridge that 320led to our little room––someone out there trying to pry in! Snap impulsively reached for the absorbers to let in the outside light––it was all darkness to us outside. But I checked him. “Wait!” I cut off our barrage, opened our door and stepped to the narrow metal bridge. “Wait, Snap! You stay there.” I added aloud, “Well, Snap, I’m going to bed. Glad you’ve cleaned up that batch of work.” IBANGED the door upon him. The lacework of metal bridges and ladders seemed empty. I gazed up to the dome, and forward and aft. Twenty feet beneath me was the metal roof of the cabin superstructure. Below it, both sides of the deck showed. All patched with moonlight. No one visible down there. I descended a ladder. The deck was empty. But in the silence something was moving! Footsteps moving away from me down the deck! I followed; and suddenly I was running. Chasing something I could hear, but could not see. It turned into the smoking room. I burst in. And a real sound smothered the phantom. Johnson the purser was sitting here alone in the dimness. He was smoking. I noticed that his cigar held a long, frail ash. It could not have been him I was chasing. He was sitting there quite calmly. A thick-necked, heavy fellow, easily out of breath. But he was breathing calmly now. He sat up with amazement at my wild-eyed appearance, and the ash jarred from his cigar. “Gregg! What in the devil––” I tried to grin. “I’m on my way to bed––worked all night helping Snap with those damn Earth messages.” I went past him, out the door into the main interior corridor. It was the only way the invisible prowler could have gone. But I was too late now––I could hear nothing. I dashed forward into the main lounge. It was empty, dim and silent, a silence broken presently by a faint click––a stateroom door hastily closing. I swung and found myself in a tiny transverse passage. The twin doors of A 22 and A 20 were before me. The invisible eavesdropper had gone into one of these rooms! I listened at each of the panels, but there was only silence within. The interior of the ship was suddenly singing with the steward’s siren––the call to awaken the passengers. It startled me. I moved swiftly away. But as the siren shut off, in the silence I heard a soft, musical voice: “Wake up, Anita––I think that’s the breakfast call.” And her answer: “All right, George. I hear it.” About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various. 2009. Astounding Stories of Super-Science, March 1930. Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/files/29607/29607-h/29607-h.htm#BRIGANDS_OF_THE_MOON_THE_BOOK_OF_GREGG_HALJAN_BEGINNING_A_FOURPART_NOVEL This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

The Sun, With the Leaping Corona, Burst Through the Blackness Behind Us

Initially released in 1991, Python is a general-purpose programming language that was designed with a philosophy of optimizing the code readability. It is often referred to as a "batteries included" language because of its comprehensive standard library. Python being used for Machine Learning is a hot topic that has been doing the rounds all over the industry. According to , it’s also ranked 2nd among the list of best programming languages, right after JavaScript, and there are really good reasons for that. In recent web design stats What’s so special about Python? Many people don’t know this but Python was conceived as a successor to the ABC language. Guido van Rossum, the guy who designed Python, really liked a few features of the ABC language but also had a fair share of grievances with the language, the biggest issues being lack of extensibility, which gave birth to Python. Let’s dive deep into the specifics of why people love Python:- Free and open-source Even though several of them are, in fact, free and open-source, it’s still one of the features of Python that makes it stand out as a programming language. You can download Python for free, which means that Python developers are able to download its source code, make modifications to it and even distribute it. Python comes with an extensive collection of libraries that support you to carry out your tasks. Awesome Collection of Inbuilt Libraries Python offers a vast number of in-built libraries that the Python development  companies can use for data manipulation, data mining, and machine learning, such as:- Used for scientific calculation. NumPy - - For data mining and analysis which optimizes Python’s machine learning usability. Scikit-learn - offers developers with high-performance structures and data analysis tools that help them reduce the project implementation time. Panda Used for advanced computation. SciPy - Used for machine learning. Pybrain - Moderate Learning Curve Many people claim that Python is really simple to understand, and given the functionality and scalability it offers, Python as a programming language is easy to learn and use. It focuses on code readability and is a versatile and well-structured language. How hard Python is, depends on you. For instance, if a newbie is provided with good study material and a decent teacher, Python can easily be understood. Even decent Python developers can teach Python to a newbie. Also Read: Top Tips to Hire Python Developer in India at minimum cost General-purpose programming language What it means is that Python can be used to build just about anything. It is extremely useful for backend Web Development, Artificial Intelligence, Scientific Computing, and Data Analysis. Python is primarily be used for web development, system operations, Server & Administrative tools, scientific modeling and can also be used by several developers to build productivity tools, desktop apps, and games. Easy to integrate Python is being used as an integration language in many places, to stick the existing components together. Python is easy to integrate with other lower-level languages such as C, C++, or Java. Similarly, it is easy to incorporate a Python based-stack with data scientist’s work, which allows it to bring efficiency into production. Easy to create prototypes As we already know that Python is simple to learn and has the ability to develop websites quickly. Python requires less coding, which means that you are able to create prototypes and test your concepts quickly and easily in Python as compared to several other programming languages. Developing prototypes saves developer's time and decreases your company’s overall expenditure as well. Understanding Machine Learning A new suggests that the market size of Machine Learning is expected to grow from $1.41 Billion in 2017 to $ 8.81 Billion by 2023, at a Compound Annual Growth Rate (CAGR) of 44.1%. What it means for business owners is to tap right into the opportunity while it’s still in its infancy. But, to invest in it, you need to understand it well. Since it’s a very detailed subject and I’m not allowed to assume that you know everything about Machine Learning, I’m going to dive deep into it, from scratch. research report Machine learning is simply a subset of Artificial Intelligence (AI) that provides systems with the ability to automatically learn without human supervision or assistance and evolve their actions accordingly. Based on experiences and observations, it analyzes certain patterns within a given data set, draws conclusions and acts accordingly, without having to be explicitly programmed. Also Read: How to Hire iOS Developer in India? Let’s talk about the basic types of Machine Learning methods:- Supervised Machine Learning Source: Coders Daddy In supervised machine learning, we teach the model by training it with some labeled dataset. It’s important to note that the data is labeled. What sets it apart is the fact that we already have prior knowledge of what the output values for our samples should be. Note that the output is determined entirely from the training data, so as long as the data labels are correct, so will the output. If they’re incorrect, it will definitely reduce the effectiveness of your model. Unsupervised Machine Learning : Source Coders Daddy This model does exactly what it sounds like. Here we let Machine Learning algorithms to draw conclusions from Unlabeled data. It has more difficult algorithms than supervised learning as we don’t have almost zero information about the data set that it uses, and hence, we also don’t have any information about the outcome. Because of this, we also have fewer tests and models that can be used so as to test the accuracy of the data predicted by them. How Good is Python for Machine Learning? At this point, you’re already aware of how big corporations rely on AI and Machine Learning for numerous operations, which also calls for a huge demand for experts in these technologies. , from IBM’s Machine Learning Department, Python is the most popular language for Machine Learning, based on trending search results on indeed.com. According to Jean Francois Puget Based on several parameters, we bring you a comprehensive list of factors that make Python the most preferred programming language for Machine Learning:- An Amazing Collection of Libraries A great library ecosystem is one of the primary reasons why Python is preferred for Machine Learning. Machine Learning requires continuous data processing, and in order to make that effective, Python’s libraries can let you access, handle and transform data. Let’s look at these libraries:- - Used for handling basic Machine Learning algorithms like clustering, regression, linear and logistic regressions, classification, etc. Scikit-learn - Used for high-level data structures and analysis. Pandas - It works with Deep Learning by setting up, training, and employing artificial neural networks with large datasets. TensorFlow - Used for deep learning. It allows fast calculations and prototyping, as it also utilizes the GPU apart from the CPU of the computer. Keras - Used for creating 2D plots, histograms, charts, etc. Matplotlib - It handles image processing. Scikit-image - Works with language recognition, computational linguistics, and processing. NLTK - Used for neural networks, unsupervised and reinforcement learning. PyBrain - Used for deep learning and allows switching between CPU and GPU and processes 60+ million images a day using just the NVIDIA K40 GPU. Caffe - It performs statistical algorithms and data exploration. StatsModels Just like , learning Python is often termed as learning the English language. If you speak english already, well, good for you. All you need to do is install Python and start using it for Machine Learning development as it wouldn’t take much effort to learn the language. The syntax of the language is simple and it allows you to work with complex systems very conveniently, ensuring clear relations between the system elements. A Lower Barrier to Entry blockchain development trends Even though, saying that it has a simpler vocabulary would be the most apt description, it is a high-level language nonetheless. It does almost everything, and you are not stuck in the minutiae like you would with C++ or others near the machine code language. Python is a great choice for Machine Learning as it offers flexibility, that further enables the developers to choose the programming styles that they find easier to use. They can even combine these programming styles to solve various types of problems in the most productive way. Let’s have a look at these styles:- Flexible - You can define the sequence of computations that happen like a change in the program state. Imperative style It declares what operations should be performed without considering the program state. It declares statements in the form of mathematical equations. Functional style - - It is based on two parameters, namely, class and object. Similar objects form classes. But since this style can’t fully perform encapsulation, it is not fully supported by Python. However, developers can still use it to a degree. Object-oriented style - This is most common among beginners since it performs tasks in a step-by-step format. It’s used for sequencing, modularization, iteration, and selection. Procedural style Apart from being flexible and easy to use, it’s very well known for being versatile. What I mean is that any Python program written on a Windows PC can be run on any platform including MacOS, Linux, Unix, and 21 other platforms and vice versa.  Here’s the catch though - In order to transfer the program from one platform to another, the developers need to perform several micro-level changes and modify some codes so as to create an executable form of code for the chosen platform. Packages like PyInstaller can be really useful when it comes to preparing the code for running on different platforms. This doesn’t just save time and money for tests on various platforms but also makes the overall process more concise. Cross-Platform Compatibility For AI and Machine Learning developers, it’s important to realize that the data interpreted by these technologies is beyond human comprehension, unless it is represented in an organized manner, in a human-readable format. Python offers a variety of libraries, several of which are great visualization tools. Matplotlib, seaborn, ggplot are some of the many popular visualization tools that allow data scientists to build charts, histograms, and plots for better understanding of data. If you scroll through the Python Package Index, you'll be able to find libraries for all kinds of data visualization needs. Good visualization options Community Support Isn’t it really helpful when you have the support of the community striving to progress in the same direction? Not many programming languages offer that privilege but there’s a strong community support built around Python. It is an open-source language and is absolutely free along with a variety of useful libraries and tools. A lot of documentation is available online when it comes to Youtube videos and other informational content but specifically in Python, there are community forums where Python developers and Machine Learning developers discuss problems and try to eliminate them and help each other out. Growing popularity TIOBE, a popular software quality assessment company, have a community index for measuring the popularity of a programming language, in the list of which, Python continued to rise and made it to the top of the list in 2018, and was called the Language of the Year. It was only in January 2019 that it was taken over by Java and C programming languages and Python ranked 3rd, a position which it still stands in . The growing popularity entails the fact that Python developers will be easier to find and be hired. It is said that Python is the most frequently taught first language in Universities, which is what makes Python the most apt choice for Machine Learning. Machine Learning is growing at a fast pace and it’s about time companies adopted the technology, especially if you want to take your business to the internet by automating certain tasks. It isn’t even a hassle to find and as we’re already aware of the popularity of the programming language. These developers are easily available at , having the required expertise and talent. August 2019’s popularity index Conclusion hire Indian developers website development agencies in India

Python for Machine Learning: Benefits & Challenges

Astounding Stories of Super-Science, January 1930, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here . Phantoms of Reality - Chapter II: The Challenge of the Unknown Phantoms of Reality - CHAPTER II. The Challenge of the Unknown Incredible! Impossible! I did not say it, though my thoughts were written on my face, no doubt. Derek said quietly, "Difficult to believe, Charlie? Yes! But it happens to be true. The girl I love is not of this world, but she lives nevertheless. I have seen her, talked with her. A slim little thing—beautiful...." He sat staring. "This is nothing supernatural, Charlie. Only the ignorant savages of our past called the unknown—the unusual—supernatural. We know better now." I said, "This girl—" He gestured. "As I told you, I have for years been working on the theory that there is another world, existing here in this same space with us. The Fourth Dimension! Call it that it you like. I have found it, proved its existence! And this girl—her name is Hope—lives in it. Let me tell you about her and her people. Shall I?" My heart was pounding so that it almost smothered me. "Yes, Derek." "She lives here, in this Space we call New York City. She and her people use this same Space at the same time that we use it. A different world from ours, existing here now with us! Unseen by us. And we are unseen by them! "A different form of matter, Charlie. As tangible to the people of the other realm as we are to our own world. Humans like ourselves." He paused, but I could find no words to fill the gap. And presently he went on: "Hope's world, co-existing here with us, is dependent upon us. They speak what we call English. They shadow us." I murmured, "Phantoms of reality." "Yes. A world very like ours. But primitive, where ours is civilized." He paused again. His eyes were staring past me as though he could see through the walls of the cellar room into great reaches of the unknown. What a strange mixture was this Derek Mason! What a strange compound of the cold reality of the scientist and the fancy of the romantic dreamer! Yet I wonder if that is not what science is. There is no romantic lover gawping at the moon who could have more romance in his soul, or see in the moonlit eyes of his loved one more romance than the scientist finds in the wonders of his laboratory. Derek went on slowly: "A primitive world, primitive nation, primitive passions! As I see it now, Charlie—as I know it to be—it seems as though perhaps Hope's world is merely a replica of ours, stripped to the primitive. As though it might be the naked soul of our modern New York, ourselves as we really are, not as we pretend to be." He roused himself from his reverie. "Hope's nation is ruled by a king. An emperor, if you like. A monarch, beset with the evils of luxury and ease, and wine and women. He is surrounded by his nobles, the idle aristocracy, by virtue of their birth proclaiming themselves of too fine a clay to work. The crimson nobles, they are called. Because they affect crimson cloaks, and their beautiful women, voluptuous, sex-mad, are wont to bedeck themselves in veils and robes of crimson. "And there are workers, toilers they call them. Oppressed, down-trodden toilers, with hate for the nobles and the king smoldering within them. In France there was such a condition, and the bloody revolution came of it. It exists here now. Hope was born in the ranks of these toilers, but has risen by her grace and beauty to a position in the court of this graceless monarch." He leaped from his chair and began pacing the room. I sat silent, staring at him. So strange a thing! Impossible? I could not say that. I could only say, incredible to me. And as I framed the thought I knew its incredibility was the very measure of my limited intelligence, my lack of knowledge. The vast unknown of nature, so vast that everything which was real to me, understandable to me, was a mere drop in the ocean of the existing unknown. "Don't you understand me now?" Derek added vehemently. "I'm not talking fantasy. Cold reality! I've found a way to transport myself—and you—into this different state of matter, into this other world! I've already made a test. I went there and stayed just for a few moments, a night or so ago." It made my heart leap wildly. He went on:— "There is chaos there. Smoldering revolution which at any time—to-night perhaps—may burst into conflagration and destroy this wanton ruling class." He laughed harshly. "In Hope's world the workers are a primitive, ignorant people. Superstitious. Like the peons of Mexico, they're all primed and ready to shout for any leader who sets himself up. My chance—our chance—" He suddenly stopped his pacing and stood before me. "Don't you feel the lure of it? The open road? 'The road is straight before me and the Red Gods call for me!' I'm going, Charlie. Going to-night—and I want you to go with me! Will you?" Would I go? The thing leaped like a menacing shadow risen solidly to confront me. Would I go? Suddenly there was before me the face of a girl. White. Apprehensive. It seemed almost pleading. A face beautiful, with a mouth of parted red lips. A face framed in long, pale-golden hair with big staring blue eyes. Wistful eyes, wan with starlight—eyes that seemed to plead. I thought, "Why, this is madness!" I was not seeing this face with my eyes. There was nothing, no one here in the room with me but Derek. I knew it. The shadows about us were empty. I was conjuring the face only from Derek's words, making real that which existed only in my imagination. Yet I knew that in another realm, with my thoughts now bridging the gap, the girl was real. Would I go into the unknown? The quest of the unknown. The gauntlet of the unknown flung down now before me, as it was flung down before the ancient explorers who picked up its challenge and mounted the swaying decks of their little galleons and said, "We'll go and see what lies off there in the unknown." That same lure was on me now. I heard my voice saying, "Why yes, I guess I'll go, Derek." About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various. 2012. Astounding Stories of Super-Science, January 1930. Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/files/41481/41481-h/41481-h.htm#Phantoms_of_Reality This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

The Challenge of the Unknown

is a prize awarded annually to 20 – 30 citizens or residents of the United States, from any field based on their demonstrated talent, dedication and potential, and not necessarily on their past achievements. The complete list of recipients is . If you glance through it, you will notice the winners’ occupations are remarkably diverse: spider silk biologists, farmers, atmospheric chemists, painters, sculptors, tropical foresters, rare book preservationists, computer scientists, doctors, historians, etc. The MacArthur Fellows Program here As the current prize for the MacArthur Fellows is $625,000 paid over five years, this award is seen as a future investment to let the fellowship recipients to their own devices and work on projects they wouldn’t have the resources, time, or money. This grant doesn’t have any strings attached. As per : the programs’ page There are no restrictions on how the money can be spent, and we impose no reporting obligations. Like the Medicis, we’ll fund Michelangelo. If even one of them produces a great work of art, it will have been worth the risk — Roderick MacArthur And was it worth the risk indeed. Quickly scanning the list of recipients, we see the Nobel prize winners Barbara McClintock and Esther Duflo, historian Jared Diamond, researcher Robert Sapolsky, inventor of the World Wide Web Tim Berners-Lee, novelist Chimamanda Ngozi Adichie, artist Lin-Manuel Miranda, author Ibram X. Kendi, and so many others. In her book, Denise Shekerjian describes how great ideas are born based on her interviews with 40 MacArthur Fellows. Uncommon Genius One insight from this book: If you keep trying something and you keep failing, I think it’s very foolish to keep trying to do the same thing in the same way unless you’re sure that nothing else will work. I think you have to be more pragmatic. Instead of stupidly insisting and pushing in an area that hasn’t proven to be useful, you ought to put it aside and look at a different problem for a while and then return to it. The mind continues to work at it even when you’re not focused on it and sometimes discovers a new angle when the spotlight has been turned off. Then when you revisit the problem, you may be in a better position to re-conceptualize it and perhaps solve it. I’ve been very pragmatic in that way and haven’t let closed doors or defeats irritate me – very much — MacArthur Fellow and research psychologist Howard Gardner in Uncommon Genius Perhaps nowadays, when we know more about how the brain alternates between focused mode (concentrating on the task at hand) and diffuse mode (relaxing our concentration from that specific task) and how this switching proves to be an excellent method for handling knowledge roadblocks, Gardner’s quote doesn’t look like much. But consider that Gardner received the MacArthur grant in 1981 and probably used this technique for years or decades before that. This procedure works wonders, and so, we might need to remind ourselves when we feel stuck on one of our projects, as it will inevitably happen, what we could do: put the project on a shelf and let it simmer, do something else in the meantime, come back to the project, work, leave it again, and so on. What is this elusive “something else” we could do in the meantime? We could fall prey to the siren songs of procrastination and get trapped by neverending content scrolling. Not saying that this is wise, but we must acknowledge that it does happen. We could prioritize sleep, an eminently diffuse mode. We could let our minds wander and do something that relaxes us. Long walks, a hobby we enjoy, taking a bath and having a Eureka moment. Or … When I can’t bear to look at my poetry, I turn to my novel in progress. When the novel makes me ill, I draft the book review I promised someone. If the book review eludes me, I may sketch out an essay that I’ve been thinking of writing. There is always something on my desk that I can turn to, always something to work on — MacArthur Fellow and writer Brad Leithauser in Uncommon Genius Arranging our ideas into various projects is yet another way to recharge our creative powers. Why should we do that? Cross-pollination between projects. As Leithauser later explained, something in his essays could become a clue on how to give a voice to one of his struggling poems. The ever-surprising connections of handling multiple projects prove that . creativity feeds on creativity Two previously unrelated ideas come together and make something new under the sun. Your job isn’t to find these ideas but to recognize them when they show up. Stephen King – On Writing Then, as Tim Hartford remarked in : Messy A fresh context is exciting; having several projects may seem distracting, but instead, the variety grabs our attention like a tourist gawping at details that a local would find mundane. Targeting specifically only one endeavor has its drawbacks. An application might fail spectacularly. No publisher would be interested in our fantasy novels. No funding for our research. But having other projects in the backlog to focus on could serve as a reset moment, bringing exciting opportunities for a breakthrough. Note that this approach is not multitasking, as we are not doing multiple things at once, but dealing with numerous projects in different phases to which we switch when we are in an impasse with a specific project, focusing entirely on the task at hand. Still, handling many projects at once might trigger an anxiety-inducing work style. When to switch? What task to choose? Do I have too many projects? Too few? And more importantly, am I focusing on the right projects for me? The answer will need a customized trial and error as we are all unique individuals, with our own peculiarities, just like everyone else. But there are some ways to handle this process. Twyla Tharp, celebrated dancer, choreographer, and another MacArthur Fellow, calls her approach to work on different projects “the box”, where the box is a foldable file box. I start every dance with a box. I write the project name on the box, and as the piece progresses, I fill it up with every item that went into the making of the dance. This means notebooks, news clippings, CDs, videotapes of me working alone in my studio, videos of the dancers rehearsing, books and photographs and pieces of art that may have inspired me. There are separate boxes for everything I’ve ever done. If you want a glimpse into how I think and work, you could do worse than to start with my boxes. The box makes me feel organized, that I have my act together even when I don’t know where I’m going yet. It also represents a commitment. The simple act of writing a project name on the box means I’ve started work. Most important, though, the box means I never have to worry about forgetting. One of the biggest fears for a creative person is that some brilliant idea will get lost because you didn’t write it down and put it in a safe place. I don’t worry about that because I know where to find it. It’s all in the box. Twyla Tharp – The Creative Habit If Tharp runs out of space in a box, she adds a new box to that project. And if she gets stuck on a project, she simply moves to another box and digs through its contents. Or, consider this approach: I have a related solution [to Twyla Tharp’s boxes] myself, a steel sheet on the wall of my office full of magnets and 3×5 inch cards. Each card has a single project on it—something chunky that will take me at least a day to complete. As I write this, there are more than 15 projects up there, including my next weekly column, an imminent house move, a standup comedy routine I’ve promised to try to write, two separate ideas for a series of podcasts, a television proposal, a long magazine article, and this chapter. That would potentially be overwhelming, but the solution is simple: I’ve chosen three projects and placed them at the top. They’re active projects, and I allow myself to work on any of the three. All the others are on the back burner. I don’t fret that I will forget them, because they’re captured on the board. But neither do I feel compelled to start working on any of them. They won’t distract me, but if the right idea comes along they may well snag some creative thread in my subconscious. Tim Hartford – Messy In my case, I use a web application, . I created several boards dedicated to my projects. For example, I have a board for writing articles on this website. As Trello follows , I have several columns in the articles board: Trello the Kanban methodology Actions required (this column is for those drafts where I have a vague idea for an article but I need more investigation, like reading a particular book or watching a specific movie or video), Profiles (for drafts similar to ), these articles Parenting (parenting articles), Drafts (all other ideas), Candidates (a smaller list with profiles, parenting, or drafts cards from which I could choose which articles to publish) Doing (usually one, but if I get stuck, I add another card in this column, as I will explain later). In each column, I have cards for each draft where in time, I put comments, articles, quotes, insights, and anything that is somewhat relevant to the topic and comes to my attention. In due course, I will move a card to the shorter list of Candidates or straight to the Doing column. Then, when it is time to write an article based on the draft card from the Doing column, I create an outline based on the comments or my thoughts and write. Of course, not all comments end up in a published article, and I also add more comments as I write. Once I publish an article, I delete that draft card from the Doing column and move a card from the Candidates column to the Doing column. Perhaps this approach could be more streamlined, but it works for me as it lets me capture everything and see what drafts I have and which to pick (for example, the draft with the most comments from the Candidates column) if I am stuck with a specific article. I recently started playing with flash fiction and short stories (nowhere near done yet), so scribbling dialogues or thinking about a character’s motivation is a new project that provides a welcome relief and context switching from the heavy research I need for some of my articles. Still, this method of maintaining our creative powers by working on a set of diverse projects is not the only way to achieve our goals. We must discover an approach that is suited to our needs and keep at it. And lastly, remember what Amos Tversky, yet another MacArthur Fellowship grant winner and a late collaborator of Nobel prize winner Daniel Kahneman, once said: The secret to doing good research is always to be a little underemployed. You waste years by not being able to waste hours. Barbara Oakley names the thinking processes related to the (focusing) and (wandering thoughts) the focused mode and the diffuse mode, respectively, in her book and the wildly popular Coursera course, . I wrote more articles about focused / diffuse modes . Note: frontoparietal network default mode network A mind for numbers Learning How to Learn here Previously published . here

Sustaining Our Creativity through Managing Multiple Projects

Mantener nuestra creatividad a través de la gestión de múltiples proyectos

Soutenir notre créativité en gérant plusieurs projets

कई परियोजनाओं के प्रबंधन के माध्यम से हमारी रचनात्मकता को बनाए रखना

複数のプロジェクトを管理することで創造性を維持する

Sustentando nossa criatividade por meio do gerenciamento de vários projetos

Duy trì sự sáng tạo của chúng tôi thông qua việc quản lý nhiều dự án

通过管理多个项目来维持我们的创造力

Hello, Hackernoon community! After four years of coding and navigating the rough waters of bootstrapping a startup and finding the right path forward, our small team of Vizzu is at the best place it can be: We finally have our first product: an open-source Javascript library. (I admit, I might be biased here as an open-source enthusiast.) I’m happy to say a few words about why we think Vizzu is unique among the dataviz libraries and why we hope that you will love to use it as much as we do. About the project: Vizzu is a free, open-source Javascript/C++ library utilizing a generic dataviz engine that generates many types of charts and seamlessly animates between them. It is designed for building animated data stories, and interactive explorers as Vizzu enables showing different perspectives of the data that the viewers can easily follow due to the animation. Here is a small snippet from the readme to give a little taste of the API. After you set your data of three data series named ‘Foo’, ‘Bar’, ‘Baz’, creating a bar chart is as simple as this: chart.animate({
  x: 'Foo',
  y: 'Bar'
}); Then you can transform it into a scatterplot by moving the category to the color scale, adding a measure to the x-axis, and switching the geometry to circle. chart.animate({
  color: 'Foo',
  x: 'Baz', 
  geometry: 'circle' 
}); We created some animated examples and sample stories to show you how Vizzu works in more detail. There’s an Edit button on each of these examples’ page that will take you to a JSFiddle so that you can start playing with them right away. Examples: https://lib.vizzuhq.com/0.4/#examples-1.1 Stories: https://lib.vizzuhq.com/0.4/#examples-1.2 There’s also a step-by-step tutorial that helps you get acquainted with all the available options: https://lib.vizzuhq.com/0.4/#chapter-0.0 More details in the GitHub repo: https://github.com/vizzuhq/vizzu-lib So what is so special about Vizzu? If you built animated data visualizations in the past, you had a big decision to make: You could go for a low-level library, most likely D3, which you could use to create stunning, highly customized animated charts, for the price of putting a lot of code and effort into your project. Or you could choose one of the high-level charting libraries, create static charts with considerably less code, and then animate a growing number (pun intended) of aspects of your chart. High-level charting libraries are easier to use because they implement the rules of data visualization (axes, legends, etc.) and provide a high-level interface over this ruleset. Sometimes with a separate interface for each chart type, other times using the abstract interface of the by Leland Wilkinson, like the Vega library or the from Hadley Wickham used in ggplot2. Grammar of Graphics Layered grammar of graphics We set out to build a dataviz tool, which offers you a third path. We think that the missing link for making animation a first-class citizen is to extend the data visualization ruleset into the time dimension, by working towards the Grammar of Animation. We created our engine that provides a standard interface for several chart types as the first step. Then we looked at the parameter space of this engine and made each parameter interpolable, turning our engine into a generic chart morpher, which can continuously interpolate between any chart types described on its interface. The second step on this road was to analyze these transitions and develop rules that make them self-explanatory and easy to follow for the viewers. Finally, we built these rules into our engine to guide you through the scarcely charted sea of animated data visualizations. We still have a lot to do, but we believe we are at a significant milestone. We built this tool for the Javascript community to smoothly build animated data visualizations, create data stories, interactive reports, and the likes, and embed them in their websites and products. Check out the and the , and decide for yourself if Vizzu brings something new to the online charting world. We would also welcome contributors to create more examples and templates and further extend Vizzu’s capabilities. One of them already made the initial version for the , which you can use with Python! examples tutorial Jupyter notebook If you want to keep an eye on what’s going on with us, make sure to follow us on Twitter: . http://twitter.com/VizzuHQ This article was first published here

Vizzu: Open Source Library For Animated Data Visualizations and Data Stories

Random forest is one of the most popular algorithms for multiple machine learning tasks. This story looks into random forest regression in R, focusing on understanding the output and variable importance. If you prefer Python code, . here you go Decision Trees and Random Forest When decision trees came to the scene in , they were better than classic multiple regression. One of the reasons is that decision trees are easy on the eyes. People without a degree in statistics could easily interpret the results in the form of branches. 1984 Additionally, decision trees help you avoid the synergy effects of interdependent predictors in multiple regression. Synergy (interaction/moderation) effect is when one predictor depends on another predictor. is a nice example from a business context. Here On the other hand, regression trees are not very stable - a slight change in the training set could mean a great change in the structure of the whole tree. Simply put, they are not very accurate. But what if you combine multiple trees? Randomly created decision trees make up a , a type of based on , .i.e. . First, you create various decision trees on bootstrapped versions of your dataset, i.e. random sampling with replacement (see the image below). Next, you aggregate (e.g. average) the individual predictions over the decision trees into the final random forest prediction. random forest ensemble modeling bootstrap aggregating bagging Notice that we skipped some observations, namely Istanbul, Paris and Barcelona. These observations, i.e. rows, are called and used for prediction error estimation. out-of-bag Random Forest Regression in R Based on CRAN’s , 63 R libraries mention random forest. I recommend you go over the options as they range from bayesian-based random forest to clinical and omics specific libraries. You could potentially find random forest regression that fits your use-case better than the original version. Still, I wouldn’t use it if you can’t find the details of how exactly it improves on Breiman’s and Cutler’s implementation. If you have no idea, it’s safer to go with the original - . list of packages randomForest Code-wise, it’s pretty simple, so I will stick to the example from the documentation using . 1974 Motor Trend data ### Import libraries
library(randomForest)
library(ggplot2)

set.seed(4543)
data(mtcars)
rf.fit <- randomForest(mpg ~ ., data=mtcars, ntree=1000,
                       keep.forest=FALSE, importance=TRUE) I will specifically focus on understanding the performance and . So after we run the piece of code above, we can check out the results by simply running . variable importance rf.fit > rf.fit

Call:
 randomForest(formula = mpg ~ ., data = mtcars, ntree = 1000, 
              keep.forest = FALSE, importance = TRUE) 

               Type of random forest: regression
                     Number of trees: 1000
No. of variables tried at each split: 3

          Mean of squared residuals: 5.587022
                    % Var explained: 84.12 Notice that the function ran random forest regression, and we didn’t need to specify that. It will perform nonlinear multiple regression as long as the target variable is numeric (in this example, it is Miles per Gallon - ). But, if it makes you feel better, you can add mpg type= “regression”. The mean of squared residuals and % variance explained indicate how well the model fits the data. Residuals are a difference between prediction and the actual value. In our example, 5.6 means that we were wrong by 5.6 miles/gallon on average. If you want to have a deep understanding of how this is calculated per decision tree, watch . this video You can experiment with, i.e. increase or decrease, the number of trees ( ) or the number of variables tried at each split ( ) and see whether the residuals or % variance change. ntree mtry If you also want to understand what the model has learnt, make sure that you do as in the code above. importance = TRUE Random forest regression in R provides two outputs: decrease in mean square error (MSE) and node purity. Prediction error described as MSE is based on permuting out-of-bag sections of the data per individual tree and predictor, and the errors are then averaged. In the regression context, Node purity is the total decrease in residual sum of squares when splitting on a variable averaged over all trees (i.e. how well a predictor decreases variance). MSE is a more reliable measure of variable importance. If the two importance metrics show different results, listen to MSE. If all of your predictors are numerical, then it shouldn’t be too much of an issue - read more . here The built-in will visualize the results, but we can do better. Here, we combine both importance measures into one plot emphasizing MSE results. varImpPlot() ### Visualize variable importance ----------------------------------------------

# Get variable importance from the model fit
ImpData <- as.data.frame(importance(rf.fit))
ImpData$Var.Names <- row.names(ImpData)

ggplot(ImpData, aes(x=Var.Names, y=`%IncMSE`)) +
  geom_segment( aes(x=Var.Names, xend=Var.Names, y=0, yend=`%IncMSE`), color="skyblue") +
  geom_point(aes(size = IncNodePurity), color="blue", alpha=0.6) +
  theme_light() +
  coord_flip() +
  theme(
    legend.position="bottom",
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank()
  ) Conclusion In terms of assessment, it always comes down to some theory or logic behind the data. Do the top predictors make sense? If not, investigate why. “Rome was not built in one day, nor was any reliable model.” ( ) Good & Hardin, 2012 Modeling is an iterative process. You can get a better idea about the predictive error of your random forest regression when you save some data for performance testing only. You might also want to try out . other methods

Random Forest Regression in R: Code and Interpretation

Regresión de bosque aleatorio en R: código e interpretación

So you sort of know about NFTs. You may have read a couple of articles, grasped what non-fungible means (basically something that’s unique or very limited) and gawped at how much money some pretty cool NFT digital art has been selling for. But it’s becoming increasingly apparent there’s a lot more to the NFT hype than simply art dealing. Discussions ranging from the real-world potential of NFTs to their impact on the planet are happening across the internet, so what are you missing? Worry no more, here are the 5 things that perhaps you still don’t quite get about NFTs: 1.What do NFTs have to do with blockchain? Non-fungible tokens are often mentioned in the same breath as the mind-bending topic of blockchain, but how exactly are they related? NFTs are a decentralised technology, which means they work on a blockchain. So what’s a blockchain? It’s basically a shared database (often referred to as distributed but it pretty much means the same thing) with some special properties: Everyone can have a copy of the blockchain database and verify that their copy is true. Only people who are allowed to make changes to the database are able to make changes. This makes blockchain perfect for storing things of value, particularly information about valuable assets. NFTs are an example of something valuable that is stored on a blockchain. They are valuable because they contain the rights to something or the information about something in the digital or physical world that is valuable e.g. they prove that you own an expensive piece of digital art. The cool thing about having NFTs stored on a blockchain is that because of their shared but secure nature, there is no central authority or company that is in control. This is the special property of blockchain: we can have verifiable digital value stored in a way where there isn’t a central entity in control of it. 2. Aren’t NFTs really bad for the planet? One of the most emotive debates about NFTs right now relates to their energy impact and how this aids or damages our fight against the climate crisis. So are NFTs bad for the planet? The first thing you need to know is that NFTs in themselves don’t produce carbon. Some blockchains do, in the act of running the blockchain. Specifically, it is the chain’s method of producing blocks that can be very energy-intensive. It doesn’t matter what information is in those blocks, it’s the act of creating them that adds to the chain’s overall carbon footprint. Secondly, there are 2 kinds of blockchain technology: which first and some second-generation blockchain’s use (such as Bitcoin). This technology uses energy as a way to try and secure the network. So it needs a lot of energy if it stores a lot of value. - Proof of work , which pretty much all other major blockchain’s either use or are moving towards. Proof of Stake is around 99% more efficient. This means that creating and sending an NFT on a Proof of Stake blockchain will be roughly similar energy-wise to Tweeting. - Proof of stake Alongside shifts to less energy-demanding technology, the blockchain community is working hard to offset its carbon impact. For example, while the Ethereum network is moving its chain from proof of work to proof of stake, its team minted an NFT that generated enough revenue to offset the carbon cost of every NFT that has since been produced on the network. So even the dirty networks are working to offset their carbon contribution. Here at in New Zealand, we have built our network using , so any NFTs that are minted on our network are already very low energy usage. On top of that, we also work with local offsetting venture CarbonClick to offset our impact and achieve not just carbon neutral but carbon negative impact. CENNZnet Proof of Stake technology 3. How does NFT ownership work if people can just copy them? So what’s the point in an NFT if you can just screenshot the $69 million piece of digital art? The answer: blockchain’s make sure we can all tell what’s a screenshot and what’s the original. Blockchains are so useful because they provide the ability to verify and authenticate things in the digital world. If we look around us we can see that the world is becoming increasingly digital and as a result, things that we value are going to become more represented as digital assets rather than or as well as in the physical world. Even before NFTs, there were multi-billion dollar industries selling skins in games or stickers in messaging apps — so it’s not a new concept. But the blockchain provides a way to prove that your copy and paste version is not the real one. Now we can have easy verification in a digital setting and without a third party coming in to authenticate it. It’s worth also remembering that issues of authenticity also apply to non-digital art. You have always been able to buy a copy of the Mona Lisa or just photograph it. Even if you get that copy nicely framed on your wall — it’s not actually the Mona Lisa — and a busload of art dealers and experts would happily attest to that if they ever tried seriously to suggest that it was. 4. I don’t game or make art, so how are NFTs actually going to be useful to me? The real-world use cases for NFTs are just starting to be developed with cool projects in digital identity, and as . ticketing charity donation tools That being said, we predict that the biggest use cases will be in the finance world. We don’t often see it in our day to day lives, but pretty much all of the financial world runs on non-fungible instruments. For example, a bond has a unique maturity date, amount and interest rate. Your mortgage is non-fungible, as is the house itself and any land. All of these instruments which currently exist in the financial world can now be put into the digital sphere and connected to the world of smart money. In essence, all of the things which are possible with digital money can now be connected to NFTs and that opens up a whole world of new financial products that you can build in the blockchain and decentralised finance space. 5. Are NFTs easy to make? First off we call making an NFT ‘minting’. How easy minting your own NFTs’ is depends on the tools you use. Some platforms require you to have a bit of blockchain coding experience others are much more straightforward and require only a quick point and click UI. It's worth doing your research before selecting a platform to determine what the process is, and also how easy it is to sell your NFTs once they are minted. This article was first published at: https://medium.com/centrality/5-things-you-still-dont-know-about-nfts-9efba0abc019 This article is part of The hosted by HackerNoon in partnership with Gaming Metaverse Writing Contest The Sandbox . Submit your today for your chance to win up to $2000. #gaming-metaverse story

5 Things You Might Not Know About NFTs

According to a report from the International Data Corporation (IDC), the global big data and business analytics market has been expanding at a fast clip over the last several years, leaping from US$122 billion in global revenue in 2015 to US$189 billion in 2019 and driving towards a projected US$274 billion for 2022. As industries store and assess a huge amount of data online, they need professionals who can take care of managing, organizing, and assessing the information. Many companies are hiring data analysts to meet their data needs. Data analysts need certain skills to excel in their positions. For a rewarding career as a data analyst, it is essential to master the skills. These skills can also be learned by enrolling in a . Following are the important technical and non-technical skills which will help an individual to get hired in 2021: data science certification Key technical skills data analyst require An individual can master following technical skills by doing a big data certification: • Programming languages – This is a high-level, general-purpose programming language, which is mainly used in data science, it also offers a remarkable number of specialized libraries. A few libraries of Python are listed below: > Python a) Numpy b) Pandas c) Scikit Learn d) Matplotlib – This is among the highly used programming languages in data analytics. Its structure and syntax are developed to take care of the analytical tasks. It is one of the most appealing programming languages for businesses as it possesses the capacity to manage huge and complex quantities of data. It consists of packages that ensure to manage the analysis more easily. A few of the packages are listed below: >R a) Ggplot2 b) Dplyr – It is a programming language and multi-paradigm numerical computing environment that offers quick data cleaning, organization, visualization, matrix manipulations, algorithm implementation, and data plotting. It can also execute any machine learning models created in its environment across multiple platforms. >MATLAB - SQL or Structured Query Language is used for querying and handling huge sets of data and processing information in relational databases. The unstructured data is stored in a NoSQL database in the format of the document. The NoSQL database querying requires a proprietary language that varies from SQL. >SQL and NoSQL • Data visualization Data visualization is the representation of the data in an easy-to-digest manner, which includes charts, images, maps, and graphs. It offers better insights into data-driven details. The data analysts also facilitate the business’s decision-makers to identify patterns and understand complex ideas at a glance. • Data warehousing In this process, the creation of virtual storage and organization systems of an enterprise takes place. The role of a data analyst is to connect the databases from multiple sources to develop a data warehouse by using the querying languages to manage, monitor, and safeguard the data. • Data mining, cleaning, and munging A data analyst gathers information from one or more sources and prepares the data so it is helpful for numerical and categorical analysis. If the data is not stored properly in the database, the data analysts use certain tools to clean the unstructured data and process it through programming. In the munging process, the data is identified, extracted, and integrated to get a dataset, which is helpful for exploration and analysis. • Machine learning Machine learning offers computers to automatically learn and perform without the requirement of explicit programming.  There are many firms that use highly technical machines to process works. The data analysts should have knowledge on how to build, apply, and train the most appropriate models and algorithms to get solutions for certain problems. Key non-technical skills data analyst require Apart from technical skills, there are other non-technical skills that are important for data analysts to be successful. An individual could be a stellar data analyst by doing a big data certification, which assures to provide the soft skills. • Communication Communication is a key skill for data analysts. They are needed to explain their findings to various stakeholders, other data analysts, and IT representatives. • Problem-solving While performing their tasks, data analysts encounter many technical issues. They are required to effectively problem-solve and come up with better solutions. • Teamwork Data analysts should be able to perform tasks with various teams so that they can collaboratively achieve the business objectives. • Domain knowledge Domain knowledge is an understanding that is relevant to the particular organization that one works for. This domain knowledge allows resourceful insights into the data and gives data analysts useful information. Conclusion By putting an effort and time into a data science certification to learn the aforementioned skills can set up an individual for a successful career as a data analyst. If an individual wants to get hired in 2021 by a top firm then he/she must enhance their skills accordingly. Always list only those skills on your resume, which you can answer well in the process of the interview.

Top Data Analyst Skills in 2021

Introduction In Bioinformatics, all tasks can be done using one of 2 programming languages: R Python Python is a high-level programming language that is known for its format. Python can do tasks from automation, to data science and machine learning. “Easy to read and learn” On the other hand, R is a programming language that was made for statistics and data processing. Through-out the past few years, R was adopted by the bioinformatics community as the number one programming language for the release of new packages, partially because of Bioconductor (a collection of mature libraries for next-generation sequencing analysis) and the ggplot2 library for advanced plotting. But from personal experience with Bioinformatics, most of my data wrangling and manipulation is done in python and endpoint analysis, and plotting is done in R. But R can do almost anything that Python can in terms of statistics and even more. My only problem with R is the sometimes — unintuitive syntax , but there is a way around this using the rpy2 library in python. (<-, %>%, variable$attribute) In this post, I want to try to show an example of using this library for bioinformatics. Setting up the environment Download the Anaconda Package Download Python 3 and R Install rpy2 from here Open up Jupyter Notebook Downloading the Data For this demonstration, we are going to use data from the . To download the data for the project use the command below: 1000 genome project wget Linux: !wget https://raw.githubusercontent.com/VarunSendilraj/Bioinformatics/main/rpy2_tutorial/ -genomes_other_sample_info_sample_info.csv # Get the data 1000 If you are on windows or mac, the command won't work so use the library instead : wget urlib Windows/Mac: urllib.request
url = filename = urllib.request.urlretrieve(url, filename) import 'https://raw.githubusercontent.com/VarunSendilraj/Bioinformatics/main/rpy2_tutorial/1000-genomes_other_sample_info_sample_info.csv' 'genomes_other_sample_info_sample_info.csv' Converting Python DataFrame to R DataFrame Let’s start by importing the necessary libraries to open and convert the python DataFrame to an R DataFrame: pandas pd rpy2.robjects ro rpy2.robjects.packages importr rpy2.robjects pandas2ri rpy2.robjects.conversion localconverter #Import Libraries import as import as from import from import from import Now let's open the file that we have downloaded using pandas function: read_csv df = pd.read_csv( )
df.head() '1000-genomes_other_sample_info_sample_info.csv' Using the we can view the data of the first few rows. Lets Double check that this is a pandas DataFrame before we continue on: .head() method print(type(df)) #expected output: <class 'pandas.core.frame.DataFrame'> The conversion of the pandas DataFrame to R DataFrame has trouble encoding float and integer values so let’s start by converting the entire DataFrame into a string. df = df.applymap(str) print(type(df[ ][ ])) #converts values in df into string # Double check that values have changed 'In_Final_Phase_Variant_Calling' 0 #Expected output: <class 'str'> Next, let’s convert the Pandas DataFrame to an R DataFrame using from the rpy2 library: localcoverter localconverter(ro.default_converter + pandas2ri.converter):
  r_df = ro.conversion.py2rpy(df) print(type(r_df)) #conversion with #Check if conversion Worked #Expected output: rpy2.robjects.vectors.DataFrame Converting Datatypes using rpy2 Before we move forward, let’s get a better understanding of our data: print( )
print(r_df.colnames) f'This dataframe has columns and rows\n' {r_df.ncol} {r_df.nrow} Here we are just printing out the different columns in our DataFrame. The output should look like this: This dataframe has columns and rows

 [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] 62 3500 1 "Sample" "Family_ID" 3 "Population" "Population_Description" 5 "Gender" "Relationship" 7 "Unexpected_Parent_Child" "Non_Paternity" 9 "Siblings" "Grandparents" 11 "Avuncular" "Half_Siblings" 13 "Unknown_Second_Order" "Third_Order" 15 "In_Low_Coverage_Pilot" "LC_Pilot_Platforms" 17 "LC_Pilot_Centers" "In_High_Coverage_Pilot" 19 "HC_Pilot_Platforms" "HC_Pilot_Centers" 21 "In_Exon_Targetted_Pilot" "ET_Pilot_Platforms" 23 "ET_Pilot_Centers" "Has_Sequence_in_Phase1" 25 "Phase1_LC_Platform" "Phase1_LC_Centers" 27 "Phase1_E_Platform" "Phase1_E_Centers" 29 "In_Phase1_Integrated_Variant_Set" "Has_Phase1_chrY_SNPS" 31 "Has_phase1_chrY_Deletions" "Has_phase1_chrMT_SNPs" 33 "Main_project_LC_Centers" "Main_project_LC_platform" 35 "Total_LC_Sequence" "LC_Non_Duplicated_Aligned_Coverage" 37 "Main_Project_E_Centers" "Main_Project_E_Platform" 39 "Total_Exome_Sequence" "X_Targets_Covered_to_20x_or_greater" 41 "VerifyBam_E_Omni_Free" "VerifyBam_E_Affy_Free" 43 "VerifyBam_E_Omni_Chip" "VerifyBam_E_Affy_Chip" 45 "VerifyBam_LC_Omni_Free" "VerifyBam_LC_Affy_Free" 47 "VerifyBam_LC_Omni_Chip" "VerifyBam_LC_Affy_Chip" 49 "LC_Indel_Ratio" "E_Indel_Ratio" 51 "LC_Passed_QC" "E_Passed_QC" 53 "In_Final_Phase_Variant_Calling" "Has_Omni_Genotypes" 55 "Has_Axiom_Genotypes" "Has_Affy_6_0_Genotypes" 57 "Has_Exome_LOF_Genotypes" "EBV_Coverage" 59 "DNA_Source_from_Coriell" "Has_Sequence_from_Blood_in_Index" 61 "Super_Population" "Super_Population_Description" Now, we need to perform some data cleanup. For example, some columns should be interpreted as Integers or Floats, but they are read as strings: Let’s start by defining the and functions: as_numeric match as_numeric = ro.r( ) match = ro.r.match #define the as_numeric function 'as.numeric' #define the Match function Functions: : converts string to integer or float value as_numeric : works like python index function match Since there are a lot of columns that need to be converted into numbers, let's store those column names in a list and iterate through it: numCol = [ , , , , , , , , , , , , , , , , , , , , , , , , , , , ] col numCol:
    my_col = match(col, r_df.colnames)[ ] print( % r_df[my_col - ].rclass[ ])
    r_df[my_col - ] = as_numeric(r_df[my_col - ])
    print( % r_df[my_col - ].rclass[ ]) #columns 'Has_Sequence_in_Phase1' 'In_Phase1_Integrated_Variant_Set' 'Has_Phase1_chrY_SNPS' 'Has_phase1_chrY_Deletions' 'Has_phase1_chrMT_SNPs' 'LC_Passed_QC' 'E_Passed_QC' 'In_Final_Phase_Variant_Calling' 'Has_Omni_Genotypes' 'Has_Axiom_Genotypes' 'Has_Affy_6_0_Genotypes' 'Has_Exome_LOF_Genotypes' 'Has_Sequence_from_Blood_in_Index' 'Total_LC_Sequence' 'LC_Non_Duplicated_Aligned_Coverage' 'Total_Exome_Sequence' 'X_Targets_Covered_to_20x_or_greater' 'VerifyBam_E_Omni_Free' 'VerifyBam_E_Affy_Free' 'VerifyBam_E_Omni_Chip' 'VerifyBam_E_Affy_Chip' 'VerifyBam_LC_Omni_Free' 'VerifyBam_LC_Affy_Free' 'VerifyBam_LC_Omni_Chip' 'VerifyBam_LC_Affy_Chip' 'LC_Indel_Ratio' 'E_Indel_Ratio' 'EBV_Coverage' #loop for in 0 #returned as a vector 'Type of read count before as.numeric: %s' 1 0 1 1 'Type of read count after as.numeric: %s' 1 0 The results should look like this (make sure all columns are converted to numbers): Type of read count before as.numeric: character of read count after as.numeric: numeric of read count before as.numeric: character of read count after as.numeric: numeric of read count before as.numeric: character of read count after as.numeric: numeric of read count before as.numeric: character of read count after as.numeric: numeric of read count before as.numeric: character of read count after as.numeric: numeric . of read count before as.numeric: character of read count after as.numeric: numeric of read count before as.numeric: character of read count after as.numeric: numeric Type Type Type Type Type Type Type Type Type .. Type Type Type Type Using ggplot2 to plot Data Now let's use from the rpy2 library to plot our data. ggplot2 Let's start by making a bar graph plotting the count of people per country that participated in the : 1000 genome project rpy2.robjects.lib.ggplot2 ggplot2 rpy2.robjects.functions SignatureTranslatedFunction ggplot2.theme = SignatureTranslatedFunction(ggplot2.theme, init_prm_translate = { : }) bar = ggplot2.ggplot(r_df) + ggplot2.geom_bar() + ggplot2.aes_string(x= ) + ggplot2.theme(axis_text_x=ggplot2.element_text(angle= , hjust= )) ro.r.png( , type= )
bar.plot()
dev_off = ro.r( )
dev_off() #import ggplot2 import as from import #set theme 'axis_text_x' 'axis.text.x' #plot 'Population' 90 1 #save to img 'out.png' 'cairo-png' 'dev.off' This may look a little bit intimidating but its mainly just boilerplate Code: Code for Plotting: bar = ggplot2.ggplot(seq_data) + ggplot2.geom_bar() + ggplot2.aes_string(x=’CENTER_NAME’) + ggplot2.theme(axis_text_x=ggplot2.element_text(angle= , hjust= )) 90 1 Format: variableName = ggplot2.ggplot(**DATAFRAME**) + ggplot.**GRAPH_TYPE** + ggplot2.aes_string(x=’**X-AXIS**’) + ggplot2.theme(**ADJUST THE THEME HOWEVER NEEDED**) In the end, the graph will be stored in a pdf file, but you can also view the file in your Jupyter Notebook: IPython.display Image
Image(filename= ) from import 'out.png' Graph: Similarly, let's create a scatterplot to compare the and the with relation to the and Total_LC_Sequence LC_Non_Duplicated_Aligned_Coverage population gender: pp = ggplot2.ggplot(r_df) + ggplot2.aes_string(x= , y= , col= , shape= ) + ggplot2.geom_point() ro.r.png( , type= )
pp.plot()
dev_off = ro.r( )
dev_off() Image(filename= ) #plot 'Total_LC_Sequence' 'LC_Non_Duplicated_Aligned_Coverage' 'Population' 'Gender' #save img 'scatter.png' 'cairo-png' 'dev.off' #veiw img 'scatter.png' Graph: We can also plot a Box Plot using the same data above: bp = ggplot2.ggplot(r_df) + ggplot2.aes_string(x= , y= , fill= ) + ggplot2.geom_boxplot() ro.r.png( , type= )
bp.plot()
dev_off = ro.r( )
dev_off() Image(filename= ) #plot 'Total_LC_Sequence' 'LC_Non_Duplicated_Aligned_Coverage' 'Population' #save img 'box.png' 'cairo-png' 'dev.off' #veiw img 'box.png' Graph: Completed Jupyter Notebook: Bioinformatics/rpy2.ipynb at main · VarunSendilraj/Bioinformatics (github.com) Also published at https://varunsendilraj.medium.com/interfacing-r-using-python-for-bioinformatics-9387c17344bd

Stories, News & Mentions about GGWP

Ggwp’s Stories on HackerNoon