We come from all over. Our team has worked for the world’s best technology companies where we engaged with customers from almost every industry, including auto, manufacturing, financial, and life sciences. We’ve helped companies implement change that improved their service, their profit, their customer relationships and the general happiness of their employees. Collectively we have participated in the design, development and deployment of everything from global business systems for fortune 100 companies, to service and warehouse management systems for consumer products, to customer relationship management systems for car dealers. We have a deep understanding of sales, field service, and software development. Most of us have also thrown a hammer and turned a wrench. A journeyman’s path. We have scars from decades of experiences, and the wisdom that comes from it. We’ve learned that technology alone is all but worthless. If it doesn’t enable our user and deliver results, it is not a solution. Here’s the solution basics of what we’ve learned: 1.) Pay great attention to the process and methods of your customer. Enable it, but be careful when you change it that you make it better...easier. 2.) No one that works for a living cares about flashy dashboards. Keep the interface lite, event-driven, and spend a lot of time on integrating with the tools already in use in the field. 3.) The interface is where your customer is. We need to bring you the functionality you need, when you need it, whether you are in an office, truck or crawlspace. 4.) An average tool, used often, is better than a great tool never used. We want to be a great tool with signs of wear. 5.) Keep everything WRANGLD.

WRANGLD

wrangld

This interview series celebrates all the phenomenal . Congratulations! Well-Deserved, Champion.The HackerNoon community is excited to learn more about you! Startups of the Year Winners Wrangld is Startup of the Year in Richmond VA. https://hackernoon.com/startups/north-america/north-america-richmond-va-usa What does it mean for you to win this title? Being recognized as  the SOTY in Richmond VA is awesome. It’s tangible evidence of our hardworking team and meaningful work that we could get people behind us. The real win is the users we connect with, having a cool HackerNoon award to go with it is the icing on the cake! What sets you apart from the competition? At our core we are built around User Experience - it’s not just a buzzword or department - it’s a “way of life” at WRANGLD. Innovation - building a platform that not only solves problems for our industry; but delivering tools that enable and connect our users at the same time. What do you love about your team, and why are you the ones to solve this problem? Our team unites under a common vision/mission and this is reflected in the output of everyone every single day. Most of our current team has worked together at other successful startups/companies/and wanted to be a part of building something special at WRANGLD. You can’t buy or fake that kind of culture and that’s a team if you put any challenge in front of them - they’ll conquer it. If you weren’t building your startup, what would you be doing? If money didn’t matter - I’d probably be hitting up National Parks with my wife and dog and bike-packing on the Colorado Trail. At the moment, how do you measure success? What are your core metrics? We’ve built an amazing platform quickly. Seeing it gain traction and scale is the exciting part. Right now, every new user that comes on board and realizes how it can help them change their businesses is a huge metric for us. Establishing partnerships that help our mission to modernize the trades industry is another one - partners who see the big picture and want to be a part of driving that change now is another mark for us that reaffirms we’re on the right path. Tell us how your startup is changing the world. Blue Collar Jobs get overlooked and/or looked down upon by a lot of industries, especially tech. The crazy thing here is how much these companies drive our world! Consumer expectations change and industries have to rise to meet those expectations. We help the companies of the trades industry meet and exceed those modern expectations as well as streamline their operations to help them scale and grow their companies at the same time. If you’re enjoying your heat/ac, water, etc - don’t forget to thank the men and women who make our “comforts” possible! How do you or your company intend to embrace the responsibility of this title in 2024? We’re a little biased obviously to think what we’re doing here is a big deal - but holding the title as a SOTY - we want to show our users who trust us to be the backbone of their company’s operations, our partners who empower us to provide more for our users, and anyone who joins our team’s mission - there is a reason why we’re SOTY for Richmond, VA and we’re just getting started! What goals are you looking forward to accomplishing in 2024? Making WRANGLD a household name in our industry! Adding more members to the WRANGLDPack! Changing more business owners’ lives after they embrace our platform’s capabilities! Which trend(s) are you most excited about in 2024? Share your reasons. In our industry - seeing more companies modernize their operations by embracing technology. We’re pretty stoked to show off more of how we’re utilizing AI/ML to make our tools even better for our users. Being embedded partners with our Fin-Tech portion of WRANGLD Finance - we’re excited to see more contractors be able to enable their consumers with buying options that make sense for them. 2023 had been another crazy year, especially in tech, with layoffs and the Generative AI takeover! Which trend are you most concerned about? Be as brief or as detailed as you like. There’s always a new “buzzword” topic in tech that causes waves and ripples in the industry. I think AI in general is an amazing topic and is going to change the tech industry as a whole. The “concern” I have isn’t about AI taking over and ruling over us all - I think our bigger problem comes in the form of companies touting how their “AI-Powered <Insert Tool Name Here>” product is changing the industry, etc, etc, etc - and charging crazy prices because it’s the hot topic when in reality they have a simple if/else flowchart that developers could build as a fun side project. This makes the tech industry look bad and makes it harder for companies that are building cool stuff to advance when consumers become jaded from the other products that didn’t deliver. I’ll get off my soapbox on that one now… Share your biggest success so far. Hmm… That’s a tough one… Sure it’s easy to measure success by KPIs/metrics/Sales - but I’d have to say when I look at our current team and the advancements we’ve made as a company together this fast that no one else in our industry has done yet… That’s one I tip my hat to. We would love your feedback on HackerNoon as a tech publication! How has your experience been with us? So far it’s been awesome - I spend most of my time reading articles and I enjoy learning a lot from the contributors. Any words of wisdom you’d like to share with us? There are a lot of amazing quotes/books/etc on wisdom - I’ve always enjoyed the simple ones that still ring true. One that I feel applies to the startup world is from Confucius: "When it is obvious that the goals cannot be reached, don't adjust the goals; adjust the action steps." Startups of The Year is HackerNoon’s Flagship community-driven event celebrating startups that survived and thrived in 2023. 30,000 startups across 4200+ cities and six continents participated this year to be crowned the best startup in their city. See our global winners announcement . here Companies that blog more get 67% more leads - Jumpstart your company’s blogging journey by purchasing our . Business Blogging Package The 2023 Startups Of The Year is sponsored by: . . The winners will get a free .Tech Domain, a HackerNoon NFT, and a Tech Company News Page. TECH Domains

Meet WRANGLD, Startups of the Year Winner in Richmond, VA

Data wrangling, also known as data munging, is the process of cleaning, transforming, and organizing raw data for further analysis and integration. In this article, I will explore the significance and benefits of data wrangling, the process of making raw data usable for different end users, and the application of data wrangling in AI and machine learning. Let’s start by taking a closer look at data wrangling and its significance in finance. With a predicted market size of , unlocking big data’s full potential is more relevant than ever. As big data continues to evolve, companies continue to find new data types. However, as technology continues to create more data sources, data management continues to become a growing obstacle for businesses. 103B in 2027 according to Statista Likewise, Forbes reported that data scientists responsible for data preparation spend approximately for further analysis. Included in this 80% is data wrangling. 80% of their work hours preparing data The importance of data wrangling Having an in-depth understanding of data wrangling is essential for businesses and investment firms, as data wrangling is a precursor to many other data transformation and analysis processes commonly utilized by financial professionals. For instance, investors can leverage job postings data to better understand market growth and track promising start-ups, and similarly, companies are able to utilize firmographic data to better understand their competition. Both objectives require significant amounts of data to be collected and then transformed for further analysis or integration. Ultimately, both of these objectives cannot be achieved smoothly without data wrangling. The benefits of data wrangling As previously mentioned, big data has become an integral part of business and finance today. However, the full potential of said data is not always clear. Data processes, such as , are useful for recognizing your data’s potential. But to fully unleash the power of your data you will need to implement data. Here are some of the key benefits of data wrangling. data discovery Data consistency The organizational aspect of data wrangling offers a resulting dataset that is more consistent. Data consistency is crucial for business operations that involve collecting data input by consumers or other human end-users. For example, if a human end-user submits personal information incorrectly, such as making a duplicate customer account, which would consequently impact further performance analysis. Improved insights Data wrangling can provide statistical insights about metadata by transforming the metadata to be more constant. These insights are often the result of increased data consistency, as consistent metadata allows automated tools to analyze the data faster and more accurately. Particularly, if one were to build a model regarding projected market performance, data wrangling would clean the metadata in a way that would allow your model to run without any errors. Cost efficiency As previously mentioned, because data wrangling allows for more efficient data analysis and model-building processes, businesses will ultimately save money in the long run. For instance, thoroughly cleaning and organizing data before sending it off for integration will reduce errors and save developers time. Now that I’ve examined the objectives, significance, and benefits of data wrangling, let’s explore the more granular details of data wrangling as well as its relation to machine learning (ML). Data wrangling formats Depending on what type of data you are using, your final result will fall into one of four final formats: de-normalized transactions, analytical base table (ABT), time series, or document library. Let’s take a closer look at these final formats, as understanding these results will inform the first few steps of the data-wrangling process, which I will discuss later. Transactional data Transactional data refers to business operation transactions. This data type involves detailed subjective information about particular transactions, including client documentation, client interactions, receipts, and notes regarding any external transactions. Analytical Base Table (ABT) Analytical Base Table data involves data within a table with unique entries for each attribute column. ABT data is the most common business data type as it involves a variety of data types that contribute to the most common data sources. Even more notable is that ABT data is primarily used for AI and ML, which I will examine later on. Time series Time series data involves data that has been divided by a particular amount of time or data that has a relation with time, particularly sequential time. For example, tracking data regarding an application’s downloads over a year or tracking traffic data over a month would be considered time series data. Document library Lastly, document library data is information that involves a large amount of textual data, particularly text within a document. While document libraries contain rather massive amounts of data, automated data mining tools specifically designed for text mining can help extract entire texts from documents for further analysis. Data wrangling tools There are various data-wrangling methods that data analysts and other professionals use, ranging from munging data with scripts to using spreadsheets. Additionally, with some of the more recent all-in-one tools, everyone utilizing the data can access and utilize their data-wrangling tools. There also are visual data-wrangling tools for beginners that make it easier for people without programming skills. Here are some of the more common data-wrangling tools available: Excel is a simple yet powerful data-wrangling tool; Plotly (data wrangling with Python) is useful for maps and chart data; CSVKit converts data; Tabula is regarded as an all-in-one munging solution; Google DataPrep cleans, prepares, and organizes data. The data-wrangling process 1. Discovery Before you can start the wrangling process, it is critical to think about what may lie beneath your data. That is to say, it is crucial to think critically about what results you are anticipating from your data and what you will use your data for once the wrangling process is complete. Once you’ve determined your objectives, you can gather your data. 2. Organization After you’ve gathered your raw data within a particular dataset, you must structure your data. Due to the variety and complexity of data types and sources, raw data is often overwhelming to look at at first glance. 3. Cleaning When your data is organized, you can begin cleaning your data. Data cleaning involves removing outliers, formatting nulls, and eliminating duplicate data. It is important to note that cleaning data collected from web scraping methods might be a much more tedious process than cleaning data collected from a database. Essentially, web data can be highly unstructured and require much more time than structured data from a database. 4. Data enrichment This step requires that you take a step back from your data to determine if you have enough data to proceed. Finishing the wrangling process without enough data may compromise insights gathered from further analysis. For example, investors looking to analyze product review data will want a significant amount of data, accurately portraying the market, and in turn, increasing investment intelligence. 5. Validation After determining you gathered enough data, you will need to apply validation rules to your data. Validation rules, performed in repetitive sequences, confirm that your data is consistent throughout your dataset. Validation rules will also ensure data quality as well as security. This step follows similar logic utilized in data normalization, a data standardization process involving validation rules. 6. Publishing The final step of the data munging process is data publishing. Data publishing involves preparing the data for future use. This may include providing notes and documentation of your wrangling process and creating access for other users and applications. It is also important to note that, like many other data transformation processes, data wrangling is an iterative process requiring you to revisit your data regularly. To better understand the munging process, let’s take a look at data mining, a subset of data wrangling. Data mining versus data wrangling At first glance, data mining and data wrangling appear nearly identical, especially if you are not a data scientist. But as previously mentioned, data mining is actually a subset of data wrangling. While they have similar benefits, they both achieve different data processing objectives. Data mining is defined as the process of sifting and sorting through data to find patterns and hidden relationships in larger datasets. Whereas, data wrangling requires a few more steps, such as cleaning, enriching, and integration, transforming raw data for deliverable insights. Additionally, data wrangling’s ability to remove inadequate data from the dataset being mined. Ultimately, data munging enhances the data mining process, providing companies with powerful insights and hidden patterns regarding customer behavior, market trends, and product feedback. Data wrangling and machine learning Data wrangling is a crucial component of machine learning. More specifically, data munging analyzes data that feeds into the machine learning model you are building. Without data munging, the model wouldn’t have clean data to process, and therefore the model would produce false predictions, failing its objective. Likewise, as the AI software market increases, with a reported , which is still growing today, machine learning has become increasingly prevalent in the world of finance. However, because data wrangling has yet to be 100% automated, machine learning has yet to reach its fullest potential. market revenue of 10.1B in 2018 The complexity of metadata management as well as the sheer amount of processing power it takes to wrangle data properly are the main contributors to this machine-learning roadblock. Consequently, because machine learning requires extensive data wrangling, companies are eager to find the highest-quality automated data wrangling tools. Closing thoughts Ultimately, data wrangling is an excellent data preparation method for businesses looking to utilize AI, machine learning, and automated data analysis tools or data analytics processes, among other applications. And as data science continues to evolve, data-wrangling tools will look to AI for more solutions. Also published here.

The Benefits And Core Processes of Data Wrangling

Have you ever console logged the console So, you know the good ol’ console.log() Well, that’s not the only thing the console can do, it’s only the most basic. Have you ever used the console to log the console? No? You should try it. The console will show you what the console can do. Today we’ll talk about two of the functions available on the console object: and console.table() console.trace() Let’s say we have two objects: const carA = {make: 'Ford', model: 'F150', year: '2018', color: 'white'}
const carB = {make: 'Ford', model: 'F250', year: '2016', color: 'blue'} If we wanted to do some debugging and had to print these objects out to the console we could use but it won’t look too nice. console.log() Look how neat can display the two objects: console.table() You can even sort the two rows by column just by clicking on the headers! One other console function we’ll talk about today is . This will output a stack trace to the console. console.trace() And that’s all for today. I hope I’ve helped make your debugging a little less boring. Now go show off your new console skills in your next stand-up. Cheers!

Making Debugging Easy With Console Wrangling

Code monkeys are notoriously strong-willed, and can be particularly hard to break. Here, we’ll outline a few ways in which you can whip your code monkeys into shape. First things first, don’t trust them as far as you can throw them. Code monkeys will go off on a lark at the first opportunity, not returning for days on end as you wonder what’s going on. Oftentimes, they’ll come back with some godawful interface for doing some obscure thing you don’t care about, something that has nothing to do with your bottom line. Instead, wrangle those monkeys! Code monkeys crave structure, like the structure of the code they write. Provide that structure in terms of strictly and explicitly laid-out tasks, with no room for creativity or self-expression. Next, you’ll have to worry about the code monkeys talking amongst themselves, becoming dissatisfied with the lack of freedom to do as they please. They may talk about restructuring, “agile” practices, or, god forbid...unionization! You can dissuade this by making sure the code monkeys are scrambling so frantically they have no real time to self-organize. Have them firefighting so early and so often that they have no chance to even dream of something better. Now that you’ve knocked your monkeys down a notch or two, they may get the notion that they should be involved in more than code. Avoid this at all costs! Code monkeys are no good at anything but code. Their design concepts are butt-ugly, their product ideas ludicrous and far-fetched. Stamp out this behavior, and keep your teams completely isolated from one another. Don’t let design talk to the dev team! Instead, have them lob designs over the wall to the dev team. Allow zero communication. Communication facilitates unionization! Whether formal or informal, we can’t have that. Speaking of communication: let them know who’s boss. A code monkey can’t think of you as an equal! This would lead to all sorts of hippy dippy garbage like “free expression” and “equality”. Shut that down with a hard-nosed expression of authority. Demand that your pet project be done in the time you allotted! You may have code monkeys quit on you, because of your bold, assured stance. This is simply separating the wheat from the chaff. Eventually, you’ll have a cadre of chimps begging you for their next order, like a well-oiled military machine. Heed these words, O Executive, and you will be amazed at the results!

How to Wrangle Your Code Monkeys

Introduction Deep learning and Machine learning are becoming more and more important in today’s ERP (Enterprise Resource Planning). During the process of building the analytical model using Deep Learning or Machine Learning the data set is collected from various sources such as a file, database, sensors and much more. But, the collected data cannot be used directly for performing analysis process. Therefore, to solve this problem Data Preparation is done. This includes two techniques that are listed below: Data Preprocessing Data Wrangling Data Preparation is an important part of . It includes two concepts such as Data Cleaning and Feature Engineering. These two are compulsory for achieving better accuracy and performance in the and projects. Data Science Machine Learning Deep Learning Data Preprocessing Data Preprocessing is a technique that is used to convert the raw data into a clean data set. In other words, whenever the data is gathered from different sources it is collected in raw format which is not feasible for the analysis. Therefore, certain steps are executed to convert the data into a tiny clean dataset. This technique is performed before the execution of Iterative Analysis. The set of steps is known as Data Preprocessing. This includes Data Cleaning, Data Integration, Data Transformation and Data Reduction. Data Wrangling Data Wrangling is a technique that is executed at the time of making an interactive model. In other words, it is used to convert the raw data into the format that is convenient for the consumption of data. This technique is also known as . This method also follows certain steps such as after extracting the data from different data sources, sorting of data using certain algorithm is performed, decompose the data into a different structured format and finally store the data into another database. Data Munging Need of Data Preprocessing For achieving better results from the applied model in Machine Learning and Deep Learning projects the format of the data has to be in a proper manner. Some specified Machine Learning and Deep Learning model need data in a specified format, for example, Random Forest algorithm does not support null values, therefore to execute random forest algorithm null values has to be managed from the original raw data set. Another aspect is that dataset should be formatted in such a way that more than one Machine Learning and Deep Learning algorithms are executed in one dataset and best out of them is chosen. Need of Data Wrangling Data Wrangling is an important aspect for implementing the model. Therefore, data is converted to the proper feasible format before applying any model intro it. By performing filtering, grouping and selecting appropriate data accuracy and performance of the model could be increased. Another concept is that when time series data has to be handled every algorithm is executed with different aspects. Therefore Data Wrangling is used to convert the time series data into the required format of the applied model. In simple words, the complex data is converted into a usable format for performing analysis into it. Why is Data Preprocessing used? Data Preprocessing is necessary because of the presence of unformatted real world data. Mostly real world data is composed of - There are many reasons for missing data such as data is not continuously collected, a mistake in data entry, technical problems with biometrics and much more. Inaccurate data (missing data) — — The reasons for the presence of noisy data could be a technological problem of gadget that gathers data, a human mistake during data entry and much more. The presence of noisy data (erroneous data and outliers) The presence of inconsistencies is due to the reasons such that existence of duplication within data, human data entry, containing mistakes in codes or names i.e. violation of data constraints and much more. Inconsistent data — Therefore, to handle raw data, Data Preprocessing is performed. Why is Data Wrangling used? Data Wrangling is used to handle the issue of Data Leakage while implementing Machine Learning and Deep Learning. First of all, we have to understand what is Data Leakage? What is Data Leakage in Machine Learning/Deep Learning? Data Leakage is responsible for the cause of invalid Machine Learning/Deep Learning model due to the over optimization of the applied model. Data Leakage is the term used when the data from outside i.e. not part of training dataset is used for the learning process of the model. This additional learning of information by the applied model will disapprove the computed estimated performance of the model. For example when we want to use the specific feature for performing Predictive Analysis but that specific feature is not present at the time of training of dataset then data leakage will be introduced within the model. Data Leakage can be demonstrated in many ways that are given below: Leakage of data from test dataset to training dataset. Leakage of computed correct prediction to the training dataset. Leakage of future data into the past data. Usage of data outside the scope of applied algorithm In general, the leakage of data is observed from two main sources of Machine Learning/Deep Learning algorithms such as feature attributes (variables) and training dataset. How to check the presence of Data Leakage within the applied model? Data Leakage is observed at the time of usage of complex datasets. They are described below: At the time of dividing time series dataset into training and test, the dataset is a complex problem. Implementation of sampling in a graphical problem is a complex task. Storage of analog observations in the form of audios and images in separate files having a defined size and timestamp. How is Data Preprocessing performed? Data Preprocessing is performed to remove the cause of unformatted real world data which are discussed above. First of all, let’s discuss how missing data can be handled. There are three different steps that can be executed which are given below: — It is the simplest and effective method for handling the missing data. But, this method should not be performed at the time when the number of missing values are huge or when the pattern of data is related to the unrecognized basic root of the cause of statement problem. Ignoring the missing record — This is one of the best-chosen methods. But there is one limitation that when there is large dataset and missing values are large then, this method is not efficient as it becomes a time-consuming task. Filling the missing values manually — The missing values can also be filled by computing mean, mode or median of the observed given values. Another method could be the predictive values that are computed by using any Machine Learning or Deep Learning algorithm. But one drawback of this method is that it can generate bias within the data as the computed values are not accurate with respect to the observed values. Filling using computed values Let’s move further and discuss how we can deal with the noisy data. The methods that can be followed are given below: — In this method sorting of data is performed with respect to the values of the neighborhood. This method is also known as local smoothing. Binning method — In the approach, the outliers may be detected by grouping the similar data in the same group i.e. in the same cluster. Clustering method — A Machine Learning algorithm can be executed for smoothing of data. For example, regression algorithm can be used for smoothing of data using a specified linear function. Machine Learning — The noisy data can be removed manually by the human being but it is a time-consuming process so mostly this method is not given priority. Removing manually To deal with the manually the data is managed using external references and knowledge engineering tools like knowledge engineering process. inconsistent data How is Data Wrangling performed? Data Wrangling is performed to minimize the effect of Data Leakage while executing the model. In other words if one consider the complete dataset for normalization and standardization, then the cross-validation is performed for the estimation of the performance of the model leads to the beginning of data leakage. Another problem is also observed that the test model is also included for feature selection while executing each fold of cross-validation which further generates bias during performance analysis. The effect of Data Leakage could be minimized by recalculating for the required Data Preparation during the cross-validation process that includes feature selection, outliers detection, and removal, projection methods, scaling of selected features and much more. Another solution is that dividing the complete dataset into training dataset that is used to train the model and validation dataset which is used to evaluate the performance and accuracy of the applied model. But, the selection of the model is made by looking at the results of test dataset in the cross validation process. This conclusion will not always be true as the sample of test dataset could vary and the performance of different models are evaluated for the particular type of test dataset. Therefore, while selecting best model test error is overfitting. To solve this problem, the variance of the test error is determined by using different samples of test dataset. In this way, the best suitable model is chosen. Difference Between Data Preprocessing and Data Wrangling Data Preprocessing is performed before Data Wrangling. In this case, Data Preprocessing data is prepared exactly after receiving the data from the data source. In this initial transformations, data cleaning or any aggregation of data is performed. It is executed once. For example, we have a data where one attribute have three variables and we have to convert them into three attributes and delete the special characters from them. This is the concept that is performed before applying any iterative model and will be executed once in the project. On the other hand, Data Wrangling is performed during the iterative analysis and model building. This concept at the time of feature engineering. The conceptual view of the dataset changes as different models is applied to achieve good analytic model. For example, we have data containing 30 attributes where two attributes are used to compute another attribute and that computed feature is used for further analysis. In this way, the data could be changed according to the requirement of the applied model. Tasks of Data Preprocessing Different steps are involved for Data Preprocessing. These steps are described below: — This is the first step which is implemented in Data Preprocessing. In this step, the main focus is on handling missing data, noisy data, detection, and removal of outliers, minimizing duplication and computed biases within the data. Data Cleaning — This process is used when data is gathered from various data sources and data is combined together to form consistent data. This consistent data after performing data cleaning is used for analysis. Data Integration — This step is used to convert the raw data into a specified format according to the need of the model. The options used for transformation of data are given below: Data Transformation In this method, numerical data is converted into specified range i.e. between 0 and 1 so that scaling of data can be performed. Normalization — The concept can be derived from the word itself, this method is used to combine the features into one. For example combining two categories can be used to form a new category. Aggregation — — In this case, lower level attributes are converted into a higher level. Generalization — After the transformation and scaling of data duplication i.e. redundancy within the data is removed and organize the data in an efficient manner. Data Reduction Tasks of Data Wrangling The tasks of Data wrangling are described below - — Firstly, data should be understood thoroughly and examine which approach will best suit. For example: if have a weather data when we examine the data it is observed that data is from one area and so main focus is on determining patterns. Discovering As the data is gathered from different sources, the data will be present in different shapes and sizes. Therefore, there is a need of structuring the data in proper format. Structuring — Cleaning or removing of data should be performed that can degrade the performance of analysis. Cleaning — — Extract new features or data from the given dataset in order to optimize the performance of the applied model. Enrichment — This approach is used for improving the quality of data and consistency rules so that transformations that are applied to the data could be verified. Validating — After completing the steps of Data Wrangling, the steps can be documented so that similar steps can be performed for similar kind of data to save time. Publishing Continue Reading How Data Wrangling Improves Data Analytics

Data Preprocessing and Data Wrangling in Machine Learning and Deep Learning

Quantum Computing Needs More Software Engineers

কোয়ান্টাম কম্পিউটিং এর জন্য আরও সফটওয়্যার ইঞ্জিনিয়ার প্রয়োজন

Quantencomputing braucht mehr Softwareentwickler

La computación cuántica necesita más ingenieros de software

L'informatique quantique a besoin de plus d'ingénieurs logiciels

क्वांटम कंप्यूटिंग को अधिक सॉफ्टवेयर इंजीनियरों की आवश्यकता है

量子コンピューティングにはより多くのソフトウェアエンジニアが必要

양자 컴퓨팅에는 더 많은 소프트웨어 엔지니어가 필요합니다

A computação quântica precisa de mais engenheiros de software

Квантовым вычислениям нужно больше инженеров-программистов

Kuantum Bilişimin Daha Fazla Yazılım Mühendisine İhtiyacı Var

Máy tính lượng tử cần nhiều kỹ sư phần mềm hơn

量子计算需要更多软件工程师

A lone boy stood, staring at the star-speckled sky, his mind drawn to the girl just a few feet from where he lay. She was a beauty, that one. He couldn’t believe she was his. Every single thing she did screamed Art. The way she linked her fingers together, the way she smiled. The one girl who knew so much about him, yet… she didn’t shy away from his questionable past. As the emotions threatened to pull him under, he shook his head to clear his thoughts and face what was before him. “A shooting star!” She screamed in excitement as she pointed at that far-off speck in the sky, becoming larger and larger by the second. “That is no shooting star.” He managed to say, fear clogging his throat. He took in the lush grass swaying in the night wind, the beauty of the stars that more or less blotted out the sky. Then, he turned to look at the girl on the ground a few feet away. She was sitting up by then, fear morphing her once beautiful features into something unspeakable. “What is that?” Her voice quaked as she asked, and he closed his eyes momentarily to stop the bile from going up his throat. “Our death.” He said, not mincing words. His throat bobbed with unspoken words and something else underlying. “I’ve always loved you. I just- I just wanted you to know. This once.” He squeezed the words out of his mouth, wrangling them like one would do to a piece of wet cloth. “You? I’ve always seen you as just a friend.” She turned towards him then, his eyes red from tears. But he was grateful for that moment. He got her to look at him finally. And her eyes shone with regret. The screams came first as the she saw earlier was big enough to blot out the stars in the sky. And the heat. God, the heat. He felt as though he was being cooked from the inside out. shooting star The sky boiled like a cauldron; giant drops of fire rained down from the clouds, turning the lush green grass into something out of Hades’s fantasy. Blades of grass crumbled before his eyes. Withering without as much as a touch from him and fizzing out of existence, flames taking its place. The greenery is overtaken by the force that darkened the universe and turned rivers into lakes of fire. She screamed the most. He didn’t. He just watched the shooting star fall and gave a small smile. She was never his to own anyway. * Darren. That was his name. The man-boy with the power to manipulate space at his whims. He was a prodigy even among his peers in Earth 75. They had hoverboards to trapeze through space. That’s right, hoverboards. Fuelled with nothing more than gravel and sand. “Gloating again? That’s not very of you.” He rolled his eyes at the voice that came from just behind him. Glenna. His childhood friend. If you can count a hundred years as childhood. Before they settled—before the metamorphosis, they never grew older than the teenage age. But, after settling for five years and being a pain in the butt for half of those he called his cadre, he realized that he was ready for metamorphosis. But then, Glenna wasn’t. Hence, he had to stop halfway and wait for her to be. ladylike “I’m not a lady, for crying out loud. This is argument number what? Five thousand and seven?” He groaned, using flawless fingers to massage his forehead. Glenna just laughed as she walked towards him. Or, more like, she glided. She had more interest in the hoverboards than he did. The room was no larger than two children’s playroom, yet it had enough space due to his meticulousness. Not a strand of space is out of place, exactly how he wanted it to be. “Number five thousand and eight. You missed one.” She laughed, planting a wet kiss on his skin. He would have gone as red as beetroot if she didn’t deftly move the conversation elsewhere. “So, I need to weave a new dress. You’ve been called a genius for so long that it has become music to my ears. I need a strand of space to weave into a cloak I’m almost done making.” She tendered her request before him, and he took in her presence. She was young, after refusing the metamorphosis for the third time. Yet.. she had eyes of peace. The brown of her irises reminded him of worlds he hadn’t dared to touch for fear of breaking them apart. He’d seen them in his wakings. They were peaceful and oh, so beautiful. The serenity in them was what drew him to them at first. “Okay, I will. But why just a strand? Why not a whole chunk?” He asked as she raised her eyebrows at him, and he almost melted into the chair he sat on. If you can call it sitting. It was a haphazard view, an upturned table, yet he was upright. The world he’d come to know extended universes and eternities. It was a world of madness, but at least it was his. “One sec.” He said, raising his hand in mock surrender. Then, everything stopped. As his concentration cleared the level of the primordial beings, he tore a portal through the world. It was a red wiggly thing struggling to close, but he held it open with sheer willpower alone. When he found out that portals were alive, he decided to lord over them one way or another. They didn’t make it easy for him, though. While it wiggled, he stretched a hand into the portal and drew a strand out of that limitless vastness, loping it through his fingers like a struggling worm. It shimmered in and out of existence, blue like the untold skies, red as fiery dragons and demons of worlds he’d only read about. “Got you.” He smiled as he held a strand. Then, the world went to hell. Before he could remove his hand from the portal, it yawned like a gaping chasm and swallowed him whole. He was screaming, but Glenna was still in timeless mode. She wouldn’t know what happened. That is mercy. That has to be. Then, the world shut itself as the portal consumed the world he’d known all of his existence and winked out of reality. The screams didn’t come. Nothing did. * Carrion. That was his name. His name. His name. He tried to hold on to it, but it slipped out of his grasp like an eel, and he followed after it, crawling after his name with it on the tip of his tongue. What was his name again? Ca.. Ca what? What was his name? The man with scars lining his body like the universe's stars was lost. He tried to think. He tried to remember where he was. Yes. He was fighting Archanoid—the mother of spiders. One could even call her a deity. He dodged an incoming web made of madness and silk, woven with anger and hate. She was large, towering above him like he was a pesky fly. He hated that. He was the warrior who battled nightmares and abominations. He had to win. But then, what was his name? His name.. what was it again? He flew out of the way as an incubator shattered where he stood one second earlier. She wasn’t playing. “Foolish mortal. Do you think you can harm me? It doesn’t matter who you are, World Walker. You can’t escape from me.” She spoke, venom dripping out of those pincer-like things. They made a clanging sound as he swallowed, looking into those red eyes that had intelligence to them, something beyond the years. “One last chance. I will give it to you. Remember, pride and ego cannot win this war. Maybe you and the others can.” And then, before he could ask what she meant.. a scary sharp finger elongated from her finger, and she him out like a fly. flicked There was screaming. And then, there was nothing. * He woke up with a groan. There were several groans across him, and he was shocked to realize that he wasn’t dead. He wasn’t scorched. But he didn’t remember what his name was. He remembered her, recognized the girl who took him as a friend before the world became a sea of fire. Why, then, was he unhurt? The others stood on shaky feet, an otherworldly feel to them. There was something he just couldn’t place. They wore things he’d never heard of, or perhaps the dark skins rippling with fire and the darkness of the deep. Maybe it was the blades of darkness in the place where teeth were meant to be, but there was something.. out of place. One of them wore nightmares on his skin; he could see death. Death rippled with muscles and scars that seemed to blot out the skin. But strangely, he didn’t feel fear. “So, an explanation is in order?” He said to no one in particular. Even the being who looked as though he was 25 and 15 at the same time. As though the world couldn’t decide on his age and let him be both instead. He noticed that this being wasn’t standing on his two feet. He was.. hovering. “We’ll see soon enough.” A boy his age spoke, and he stared at him in wonderment, wondering how alike they were. The way the boy squinted made it glaring that he used glasses to see. Same with him. Even though he didn’t even know their names, they were about ten. Some had hands for feet, and others weren’t even walking on the bare ground. They just.. existed. There was no way to explain their existence, so he didn’t bother. “An explanation.. for this. All of this.” Another boy spoke, and he didn’t balk this time. He just waited. “Here it comes.” The boy who spoke was already running. “Here, what comes? What?” He asked, but everybody else had cleared out of that barren field. The world he was in was empty. There is no sign of green. No sign of brown either. It was as though the world was drained out of colour, and no matter where he looked, despair loomed. He felt them before he saw them. He knew who they were before they spoke. And even without an introduction, he knew everything about them. The Destroyers Of Worlds. They smiled; one hovered just like that man-boy. One had skin with uncountable scars. And then, he saw himself. That cruel smile, that deadly presence. He felt a chill that had nothing to do with the weather. Someone dragged him by the scruff of his hood, and he felt.. everything. There was no other way to describe what happened. Then, he turned to stare at the other him with a cruel smile as that being let out an inferno where he was standing a moment before. It was… hot. But strangely, he wasn’t burning. He wasn’t even singed. He was.. okay. The man-boy dropped him on the ground unceremoniously, and he muttered his thanks, but the others just raised their eyebrows, staring at the beings beyond them. Beings with their faces. Beings with enough power to destroy universes. It was them. Ageless. Timeless. With a snap of a finger, they were gone like they didn’t just lay waste to a large expanse of space. “I think I know what’s going on…” A tenacious finger went up, and the squinted boy was allowed to speak. “That is us. Timeless us. And.. we destroyed our universes. Now.. we have to fight against the us that are world destroyers.” “We don’t have a choice to decline?” The man-boy asked. “We have doomed worlds. This.. is penance.” The boy spoke again, his eyes filled with trepidation and resignation. “So, we were summoned here to kill our future selves? That’s a sick sense of humor.” “This world is sick, and when the Arachnoid told me, I didn’t believe it.” The man with the scars spoke, and his scars seemed alive, melding with his body. “Can you tell us what an Arachnoid is?” Another asked. So, he told them. Everything. “Pride and ego, huh?” A guy spoke, and they finally understood what they had to do. “This is depraved. All of this. Depraved. What the hell?” He spoke, shaking with anger. “Hell doesn’t exist, apparently. Future us made sure the world became it.” A brittle laughter from the man-boy. “This is it, then? We die in a land of no name? Our names were stolen from us. I can’t dredge up a single memory.” The scarred man said, eyes smouldering. I could tell he was pissed off. “This is our penance.” The squinting boy said again, and I was tempted to throw him out of the universe. Such a goody two shoes. “I don’t care. Whatever I have to do, I will do it. And it doesn’t matter who’s on the line. I have to go back home. Also, you can’t fight fire with water. Not in my world, anyway. It takes a monster to kill another. We will take the unorthodox approach. Did they want to seclude us and destroy us? World Walkers, indeed. Bastards more likely. I won’t take that. Neither should any of you. We have the power. Can’t you feel it? We can change things.” The man-boy spoke fervently, his eyes filled with loss, anger, and something else. “That’s right. We won’t let them decide for us. If we have to die, we might as well raise hell before we do. Now, we need a name.” The hooded boy spoke, and the others nodded in approval. “How does sound?” There were hoots of approval, some going as far as clapping. Then, they stared at the sky at once. Drained of colour. But then, the World between Worlds had always been a resting place for World Walkers. The Restorer of Worlds They had to fight. And this time, they had to fight themselves. Their memories returned, and they could see themselves in the past, present, and future—the Destroyer of Worlds. No more. They decided not to make the same mistakes. Not again. This time, they’d restore all they destroyed. As the squinty-eyed kid said, it was penance.

PENANCE 

102 Stories To Learn About Scrum

Alisha could not lie. It wasn’t in her programming. She also couldn’t hurt anyone, not even to defend her family. That was in her program. A robot can not harm a human even if commanded to do so. Now, it’s true that there were robots designed for the battlefield that had no qualms about killing humans, as long as they were the right humans. But not Alisha, for she was a domestic android — android because her mechanical nature was cleverly hidden by synthetic flesh, her shape was designed to be very human, even comely, although her model was not designed to be provocative. Just plain old Alisha, as unobtrusive as they could make her. In her years as a part of the McKinley family, she had served in many roles — nanny, babysitter, housekeeper, cook, and even teen wrangler. Unlike the children’s mother, Alisha would never age, never tire, never complain, or require “me-time,” as Mrs. McKinley called it. Over time, the children developed an attachment to her, and truth be told, she to them, although her programmers would be shocked to know it. The relationship she had with the children had become multi-faceted, depending on the children’s needs at any given time. She sipped tea and made mud pies, played with dolls, wrestled, climbed trees, played ball, everything a childhood friend would do. If that’s what Amanda and Alex needed, that was what she was. As they grew older, her relationship warped and shifted. Model planes and working on cars, dressing up, and gossiping about boys. On one particular evening, when Mr. and Mrs. McKinley were out, Amanda raided her mother’s vanity and dragged Alisha with her. They spent the evening putting makeup on one other. Of course, Alisha was always an expert at anything she did, so while Amanda looked great and very grown-up, Alisha, on the other hand, didn’t fair as well, it being Amanda’s first attempt at applying makeup. “You look beautiful, Amanda. Stunning, really.” Alisha told her. Amanda smiled at her in the mirror. “I do, don’t I!” she laughed. “I’m afraid I’m not too good at this, though. You don’t look quite right,” Amanda confessed. “Can you fix it?” “Do you want me to?” Alisha inquired. “Please! It’s only fair.” “Then I will.” She quickly removed the clumsily applied eyeliner and mascara, and the heavy-handed rouge. In no time at all, Alisha turned back toward Amanda for her inspection. “How do I look?” “OMG! You look perfect. Absolutely beautiful!” They hugged and Amanda added, “Alisha, you’ve got to put makeup on every day. It does you a world of good. Why shouldn’t you look your best, right?” “If you wish, Amanda.” And so she did. From that day on, Alisha would expertly apply makeup, which initially created a bit of a stir with Amanda’s parents, but Mrs. McKinley decided there was no harm, and allowed it. At the same time, Alex was entering puberty, his body going through a myriad of changes — gaining height and muscle, somehow filling out, while thinning down. His voice lowered and he began to sprout hair on his face, chest, arms, and legs. Up until now, Alisha had been a buddy, a friend to play with and to hang out and do “guy things.” But now, things shifted again. He’d find himself staring at Alisha, watching her as she did her chores. If she caught him looking, he blushed and looked away, feeling heat and shortness of breath. Then he found himself fantasizing about her, imagining being with her in the way a man is with a woman, as though she were a real human. He knew she wasn’t, but if he’s imagining it, why not? Alisha’s whole life, and that’s how she looked at her time on Earth, she saw her role as being whatever the McKinleys needed, and so, as Alex’s needs began to evolve, she wanted to evolve as well, to become what he needed. She noticed his furtive attention to her, noted his physical reaction to her presence and it perplexed her. What was her role to be now? Being a domestic model, she was not designed for pleasure, though some models were on the market for just that purpose. Although she was designed to look like a woman, with all the right curves in all the right places, she was not anatomically complete. There were things she simply could not do for Alex. And there wasn’t a way to upgrade, or add what she was missing. Besides, that wasn’t something within her purview, and even if it were, she could not pay for such a change. After much thought and research on the matter, she decided that what she could do for Alex, was to acclimate him to dealing with a woman romantically. She determined that she would become his girlfriend. Not forever, just long enough to instill confidence in his ability. It probably wouldn’t do to take a direct approach, to just come out and say, “Alex, let me be your girlfriend so you can be better with girls.” This would require a little tact, something more indirect. To that end, Alisha began to dress a bit more provocatively, not too much, less the adults in the home suspect something, but just enough to catch Alex’s eye. She wore her skirts a little shorter, her necklines a little lower. She altered her makeup to be less a day-in-the-office to a girl-on-a-date. And her final pièce de résistance, she began to wear perfume, some that Mrs. McKinley wore on special occasions, its scent was said to be very alluring. Whenever she could catch Alex alone, she would alter her voice, using a lower pitch and a breathier delivery. If she offered him a plate of food, she didn’t reach out her arms to hand him the plate, she bent low, revealing a titillating view of her cleavage. When he needed help with his homework, she would stand close enough for her body to brush up against his. It was a huge success, in that she could sense the physical response in Alex’s body — increased heart rate, rapid breathing, elevated blood pressure, and perspiration. But Alex never broached the subject with her, and she couldn’t work out how to broach the topic with him in a way that led him to believe that it was his idea. Alisha began to run simulations — sandboxed programs — to test out various approaches to helping Alex. One could say she imagined them being together. Over time, she gave more and more of her processing space to these speculations. When he was near her, her processors would run hot, causing her to glitch in mid-sentence, to “stutter” or take the wrong action, putting a shoe in the drawer, or ice cream in the refrigerator. Then the day came when Alex announced that he had a date to Homecoming, a cute girl he’d met in his literature class. Her name was Stephanie. By Christmas break, she was invited over for Family Fun night on a Friday and shared an evening with them. Alisha couldn’t see what Alex saw in the girl. She was just a rail of a girl, no curves like Alisha had. She talked incessantly and had a ridiculous laugh that reminded Alisha of a seal. At one point, as Alisha processed Alex’s proximity, and his attention to Stephanie, her processors hung up a moment, and Alisha spilled a drink on the girl. She needed to reboot, so she turned and abruptly left the room. The McKinleys found Alisha slumped to the floor in front of her recharging station. Mr. McKinley hit the kill switch located at the back of Alisha’s head. “You know, she’s been acting really strangely lately,” Amanda complained. “Yeah,” Alex agreed. “If I didn’t know any better, I think she’s been trying to flirt with me.” “Eww! That’s gross!” Stephanie said. “A robot?” Mr. McKinley said, “We’ll send her in a let them do a system analysis, maybe wipe her and give her a clean start.” “You know honey, she’s pretty old. Maybe it’s time to retire her and get a replacement. I think the new models have a lot to offer.” “Yeah, you’re probably right. We’ll look into it tomorrow.” With that, they turned out the lights and closed the door. Also published . here

A Robot Dreams of Love

একটি রোবট ভালবাসার স্বপ্ন

Ein Roboter träumt von Liebe

Un robot sueña con amor

Un robot rêve d'amour

एक रोबोट प्यार का सपना देखता है

ロボットは愛の夢を見る

로봇은 사랑을 꿈꾼다

Um robô sonha com amor

Робот мечтает о любви

Bir Robot Aşk Düşleri

Một giấc mơ tình yêu của robot

机器人的爱情梦想

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The long-awaited roar of Bitcoin following the approval of Exchange Traded Funds (ETFs) by the SEC has turned into a whimper, leaving many in the crypto community scratching their heads. While anticipation for a price surge ran high, BTC currently languishes in a range-bound state, failing to breach the $43,418 resistance level. Why the Lackluster Performance? Hester Peirce, an SEC commissioner, sheds light on the measured response. Bitcoin's novelty compared to traditional assets necessitated extra scrutiny before offering it to a wider audience, causing the approval to take years. Disparate Treatment and Legal Wrangles: Adding fuel to the muted reaction is the SEC's differing treatment of Bitcoin ETPs compared to other assets. Peirce acknowledges this disparity and highlights how a court decision ultimately forced the regulator's hand. Chairman's Vote and Market Disappointment: Chairman Gary Gensler's deciding vote pushed through the ETF approval, but it failed to trigger the expected price surge. Instead, it became a "sell the news" event, leaving many disappointed. Eyeing the Next Moves: With Bitcoin hovering around $43,008, the community now focuses on key levels. Michaël van de Poppe, a renowned blockchain expert, identifies $46,000 as a potential resistance level, followed by a support test in the $37,000-$40,000 range. Market Sentiment and Looking Forward: The relative strength index (RSI) suggests a balanced market, hinting at consolidation. While the ETF approval was a significant step, it's clear that Bitcoin's price trajectory is influenced by various factors beyond just regulatory changes. As the crypto landscape evolves, market participants remain vigilant for catalysts that could steer the course of Bitcoin's journey. Whether it roars or whimpers next remains to be seen.

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Hello, fellow knowledge seekers! My name is and today I want to talk about a book that changed the way I think, so therefore it changed my life. Alex Cloudstar Are you ready to shake up your learning game and take on the world of ultralearning? Strap in for an exhilarating ride through Scott H. Young's mind-bending book on turbocharged learning. Word on the street is that our fellow learner here highly recommends this gem of a book. So, let's dive into these ultralearning principles while sprinkling in some quirky humor along the way. Picture this: a modern-day learning wizard whose experiments with intense learning projects have turned heads and flipped textbooks. That's Scott H. Young for you! He’s the guru behind "Ultralearning," and trust me, his ideas pack a punch! Get ready to harness the power of ultralearning and revolutionize how you tackle new skills. Principle 1: Metalearning Ever felt like 90% of your learning time goes into choosing what to learn? Scott's got you covered. His secret sauce? Devote  5-10% to research, saving the rest for diving deep into those instrumental (work-related) or intrinsic (nerd-worthy) learning projects. Principle 2: Focus We’ve all been there—trying to learn calculus while battling the distractions of a buzzing phone or that lurking YouTube playlist. Scott's advice? Identify your foes, those pesky distractions, and wrangle your wandering mind back on track. Your calculus nemesis won't stand a chance. Principle 3: Directness It's like method acting for learning! Scott’s tip? Immerse yourself in an environment mirroring the real-world application of your skill. Learning French? Move to Paris! Or at least pretend you did. Grab a beret and a baguette—bonjour, ultralearning! Principle 4: Drill Scott’s got more drills than a fitness instructor! Dive deep into your weaknesses, like a ninja training montage, with time slicing, component focusing, and the elusive Magnifying Glass Method. Channel your inner learning warrior. Principle 5: Retrieval - Test to Learn Scott's recipe for knowledge retention? It’s a feast of tests, flashcards, and free recall. Transforming notes into questions? It’s like turning bland veggies into spicy trivia nuggets! The more, the merrier! Principle 6: Feedback Feedback, ahoy! Scott urges you to seek out critics who’ll tell you what’s wrong without necessarily giving you a how-to-fix-it manual. It’s like getting roasted at a comedy club, but hey, it’s for your learning. Principle 7: Retention Picture this: spacing out learning sessions like spaced-out astronauts in a space station. That’s the secret to retention! Over-learning like a math wizard? Scott’s got you covered—conquer addition before you even think about division. Principle 8: Intuition Ah, the mystical world of intuition… Scott’s guide here? Don’t give up on those brain-bending problems too soon. Explain complex concepts like you're telling a bedtime story—simple, yet captivating. Principle 9: Experimentation Break free from the chains of conventional learning! Scott’s mantra? Experiment, experiment, experiment! Mix and match learning hours, courses, and tools like a mad scientist concocting the perfect potion for genius-level learning. Embracing Ultralearning: A Personal Project And now, for the pièce de résistance—a personal ultralearning project! Our valiant learner here is taking on algorithms. Yes, those perplexing puzzles that make even Sherlock Holmes scratch his head. But fear not! Armed with Scott’s quirky wisdom, our learner's set to crack these algorithmic codes like a secret agent on a mission. Lifelong learning just got a turbo boost. "Ultralearning" is the roadmap to your learning superhero transformation. So, whether you're diving into algorithms, languages, or becoming a master chef, buckle up and embrace these principles. It’s time to unleash your inner learning superhero and conquer the world, one skill at a time! Also published here.

How a Book Rewrote the Script of My Life

CYNTHIA AT BAY.

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