Skio

skio

Authors:
(1) Ruochu Yang;
(2) Chad Lembke;
(3) Fumin Zhang;
(4) Catherine Edwards. Table of Links Abstract and Intro Anomaly Detection Algorithm Experimental Evaluation Conclusion and References Abstract— Underwater gliders have been widely used in oceanography for a range of applications. However, unpredictable events like shark strikes or remora attachments can lead to abnormal glider behavior or even loss of the instrument. This paper employs an anomaly detection algorithm to assess operational conditions of underwater gliders in the real-world ocean environment. Prompt alerts are provided to glider pilots upon detecting any anomaly, so that they can take control of the glider to prevent further harm. The detection algorithm is applied to multiple datasets collected in real glider deployments led by the University of Georgia’s Skidaway Institute of Oceanography (SkIO) and the University of South Florida (USF). In order to demonstrate the algorithm generality, the experimental evaluation is applied to four glider deployment datasets, each highlighting various anomalies happening in different scenes. Specifically, we utilize high resolution datasets only available post-recovery to perform detailed analysis of the anomaly and compare it with pilot logs. Additionally, we simulate the online detection based on the real-time subsets of data transmitted from the glider at the surfacing events. While the real-time data may not contain as much rich information as the post-recovery one, the online detection is of great importance as it allows glider pilots to monitor potential abnormal conditions in real time. I. INTRODUCTION Underwater gliders are extensively used in ocean research for activities such as ocean sampling, surveillance, and other purposes [1]–[5]. However, given the complexity of the ocean environment and the long-duration of glider missions, unexpected events such as shark attacks, wing loss, or attachment of marine species can cause gliders to operate abnormally or even totally fail [6]–[8]. In such cases, the gliders may drift to unexpected areas, making localization and rescue operations challenging. Furthermore, it can be difficult to detect the abnormal behavior of gliders, particularly when external disturbances arise, due to the lack of monitoring devices [9]–[12]. The deployment of monitoring devices for gliders or the addition of self-monitoring of performance would increase mission costs and pilot complexity. Typically, glider pilots can only rely on heavily subsetted data transmitted by the glider in real time to form hypotheses about potential anomalies. Sometimes, they just resort to climb and dive ballast data to assess if the glider is surfacing or diving as expected. However, this empirical detection can never be conclusively confirmed as the mission is going on. To address this challenge, we develop an anomaly detection algorithm that systematically utilizes simple glider data such as glider speed, heading, and trajectory. This algorithm is feasible for theoretical validation on numerous real-world glider datasets, and runs autonomously in real-time, as opposed to manual detection by human pilots. By monitoring gliders in realtime, the algorithm allows glider pilots to take appropriate actions promptly to ensure the safety and success of missions. Different strategies have been in the field of underwater robotics to identify abnormal behavior of underwater gliders. Some anomaly detection algorithms focus on changes in robot motion, such as roll angle or pitch angle, to detect possible motion deviation or a foreign object attached to the glider [13], [14]. Some algorithms monitor the power consumption or motor performance of the glider, as variations in these parameters can indicate degeneration of individual components, such as propellers and rotors [15]–[17]. Other algorithms utilize machine learning techniques to identify anomalous behavior by analyzing sensor data collected by the glider over time, such as changes in the speed, roll, pitch, or depth [18]–[20]. However, most of the existing research relies on shore-based manual implementations and does not resolve issues like inability to perform online detection on the gliders or lack of real-time experimental verification. In addition, it is essential to determine whether the detected anomaly is false positive [21], [22]. When the ocean current speed is significantly greater than the maximum speed of the marine robot, it can lead to a considerable performance degradation. Under such circumstances, false alarms should be avoided since the anomaly caused by an unexpected ocean current is unrelated to the glider itself. In practice, it is challenging to separate flow speed and glider speed due to hardware limitations, but leveraging the Controlled Lagrangian Particle Tracking (CLPT) framework [23], the anomaly detection algorithm in [24] generates real-time estimates of the glider speed and flow speed from the trajectory and heading angles. The estimated glider speed is compared with the normal speed range to detect anomalies, while the algorithm-estimated flow speed is compared with the gliderestimated flow speed to avoid false alarms. We initially validate the anomaly detection algorithm by using two real-life deployment datasets [25]. Building upon this previous work, we aim to extend the algorithm to largescale datasets, thus effectively handling various anomalies in diverse missions. We also plan to simulate online implementation of the detection to enable real-time interaction with glider pilots. This objectives constitute primary motivation of this paper, and our main contribution are summarized as follows. • We demonstrate generality of the anomaly detection algorithm based on four glider datasets collected in real deployments featuring diverse anomalies. • We simulate online mode implementation of the algorithm to a real glider deployment with limited data streams in real time for the first time. The SouthEast Coastal Ocean Observing Regional Association (SECOORA) glider Franklin, operated by Skidaway Institute of Oceanography (SkIO), and the University of South Florida (USF) gliders USF-Sam, USF-Gansett, and USF-Stella provide numerous examples of valuable experimental data in which anomalies may be associated with marine bio-hazards. Promising anomaly detection results of these datasets are shown to well match glider pilots’ hindcast analysis. Building off its efficacy, the real-time anomaly detection algorithm is incorporated into the autonomous glider navigation software GENIoS Python [26] to better assist human pilots as an add-on warning functionality. This paper is organized as follows. Section II illustrates the framework of the anomaly detection algorithm. Section III describes the experimental setup of glider deployments, verifies the algorithm by detecting anomalies in large-scale real experiments, and simulates the online implementation on subsetted glider datasets. Section IV provides conclusions and future work. This paper is available on arxiv under CC 4.0 license. Authors: (1) Ruochu Yang; (2) Chad Lembke; (3) Fumin Zhang; (4) Catherine Edwards. Authors: Authors: (1) Ruochu Yang; (2) Chad Lembke; (3) Fumin Zhang; (4) Catherine Edwards. Table of Links Abstract and Intro Abstract and Intro Anomaly Detection Algorithm Anomaly Detection Algorithm Experimental Evaluation Experimental Evaluation Conclusion and References Conclusion and References Abstract— Underwater gliders have been widely used in oceanography for a range of applications. However, unpredictable events like shark strikes or remora attachments can lead to abnormal glider behavior or even loss of the instrument. This paper employs an anomaly detection algorithm to assess operational conditions of underwater gliders in the real-world ocean environment. Prompt alerts are provided to glider pilots upon detecting any anomaly, so that they can take control of the glider to prevent further harm. The detection algorithm is applied to multiple datasets collected in real glider deployments led by the University of Georgia’s Skidaway Institute of Oceanography (SkIO) and the University of South Florida (USF). In order to demonstrate the algorithm generality, the experimental evaluation is applied to four glider deployment datasets, each highlighting various anomalies happening in different scenes. Specifically, we utilize high resolution datasets only available post-recovery to perform detailed analysis of the anomaly and compare it with pilot logs. Additionally, we simulate the online detection based on the real-time subsets of data transmitted from the glider at the surfacing events. While the real-time data may not contain as much rich information as the post-recovery one, the online detection is of great importance as it allows glider pilots to monitor potential abnormal conditions in real time. Abstract — Underwater gliders have been widely used in oceanography for a range of applications. However, unpredictable events like shark strikes or remora attachments can lead to abnormal glider behavior or even loss of the instrument. This paper employs an anomaly detection algorithm to assess operational conditions of underwater gliders in the real-world ocean environment. Prompt alerts are provided to glider pilots upon detecting any anomaly, so that they can take control of the glider to prevent further harm. The detection algorithm is applied to multiple datasets collected in real glider deployments led by the University of Georgia’s Skidaway Institute of Oceanography (SkIO) and the University of South Florida (USF). In order to demonstrate the algorithm generality, the experimental evaluation is applied to four glider deployment datasets, each highlighting various anomalies happening in different scenes. Specifically, we utilize high resolution datasets only available post-recovery to perform detailed analysis of the anomaly and compare it with pilot logs. Additionally, we simulate the online detection based on the real-time subsets of data transmitted from the glider at the surfacing events. While the real-time data may not contain as much rich information as the post-recovery one, the online detection is of great importance as it allows glider pilots to monitor potential abnormal conditions in real time. Abstract I. INTRODUCTION Underwater gliders are extensively used in ocean research for activities such as ocean sampling, surveillance, and other purposes [1]–[5]. However, given the complexity of the ocean environment and the long-duration of glider missions, unexpected events such as shark attacks, wing loss, or attachment of marine species can cause gliders to operate abnormally or even totally fail [6]–[8]. In such cases, the gliders may drift to unexpected areas, making localization and rescue operations challenging. Furthermore, it can be difficult to detect the abnormal behavior of gliders, particularly when external disturbances arise, due to the lack of monitoring devices [9]–[12]. The deployment of monitoring devices for gliders or the addition of self-monitoring of performance would increase mission costs and pilot complexity. Typically, glider pilots can only rely on heavily subsetted data transmitted by the glider in real time to form hypotheses about potential anomalies. Sometimes, they just resort to climb and dive ballast data to assess if the glider is surfacing or diving as expected. However, this empirical detection can never be conclusively confirmed as the mission is going on. To address this challenge, we develop an anomaly detection algorithm that systematically utilizes simple glider data such as glider speed, heading, and trajectory. This algorithm is feasible for theoretical validation on numerous real-world glider datasets, and runs autonomously in real-time, as opposed to manual detection by human pilots. By monitoring gliders in realtime, the algorithm allows glider pilots to take appropriate actions promptly to ensure the safety and success of missions. Different strategies have been in the field of underwater robotics to identify abnormal behavior of underwater gliders. Some anomaly detection algorithms focus on changes in robot motion, such as roll angle or pitch angle, to detect possible motion deviation or a foreign object attached to the glider [13], [14]. Some algorithms monitor the power consumption or motor performance of the glider, as variations in these parameters can indicate degeneration of individual components, such as propellers and rotors [15]–[17]. Other algorithms utilize machine learning techniques to identify anomalous behavior by analyzing sensor data collected by the glider over time, such as changes in the speed, roll, pitch, or depth [18]–[20]. However, most of the existing research relies on shore-based manual implementations and does not resolve issues like inability to perform online detection on the gliders or lack of real-time experimental verification. In addition, it is essential to determine whether the detected anomaly is false positive [21], [22]. When the ocean current speed is significantly greater than the maximum speed of the marine robot, it can lead to a considerable performance degradation. Under such circumstances, false alarms should be avoided since the anomaly caused by an unexpected ocean current is unrelated to the glider itself. In practice, it is challenging to separate flow speed and glider speed due to hardware limitations, but leveraging the Controlled Lagrangian Particle Tracking (CLPT) framework [23], the anomaly detection algorithm in [24] generates real-time estimates of the glider speed and flow speed from the trajectory and heading angles. The estimated glider speed is compared with the normal speed range to detect anomalies, while the algorithm-estimated flow speed is compared with the gliderestimated flow speed to avoid false alarms. We initially validate the anomaly detection algorithm by using two real-life deployment datasets [25]. Building upon this previous work, we aim to extend the algorithm to largescale datasets, thus effectively handling various anomalies in diverse missions. We also plan to simulate online implementation of the detection to enable real-time interaction with glider pilots. This objectives constitute primary motivation of this paper, and our main contribution are summarized as follows. • We demonstrate generality of the anomaly detection algorithm based on four glider datasets collected in real deployments featuring diverse anomalies. • We simulate online mode implementation of the algorithm to a real glider deployment with limited data streams in real time for the first time. The SouthEast Coastal Ocean Observing Regional Association (SECOORA) glider Franklin, operated by Skidaway Institute of Oceanography (SkIO), and the University of South Florida (USF) gliders USF-Sam, USF-Gansett, and USF-Stella provide numerous examples of valuable experimental data in which anomalies may be associated with marine bio-hazards. Promising anomaly detection results of these datasets are shown to well match glider pilots’ hindcast analysis. Building off its efficacy, the real-time anomaly detection algorithm is incorporated into the autonomous glider navigation software GENIoS Python [26] to better assist human pilots as an add-on warning functionality. This paper is organized as follows. Section II illustrates the framework of the anomaly detection algorithm. Section III describes the experimental setup of glider deployments, verifies the algorithm by detecting anomalies in large-scale real experiments, and simulates the online implementation on subsetted glider datasets. Section IV provides conclusions and future work. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Real-Time Anomaly Detection in Underwater Gliders: Abstract and Intro

The Useless Skills You're Still Learning 

Here's the thing about HBO documentaries. The best thing about them is - the production value. The worst thing is - the production value. What's that supposed to mean? Well, you know what they say. After a series of award-winning and jaw-dropping HBO documentaries, such as The Inventor, Leaving Neverland, Going Clear, Beware the Slenderman, and At the Heart of Gold, we were supposed to be blown away with - Money Electric: The Bitcoin Mystery. It didn't happen. Yeah, I've spent more time watching HBO than reading about blockchain. That's why my articles about Bitcoin always play it safe. I share true stories of how Bitcoin impacted the lives of people I know personally or the challenges of Bitcoin acceptance, and similar stuff the MAXIs won't tear me apart for. After spending just one day reading about what blockchain is and how it works from "Cryptocurrency Investing For Dummies" by Kiana Danial, which I couldn't recommend more, I realized that it wasn't rocket science. However, you can't just show a few cubes, which looked as if they were taken out of the movie "Cube," and call it a blockchain day. Back to the production value because it is important. Look, I don't know what happened to the author, Cullen Hoback, after Terms and Conditions May Apply, but at least he didn't stretch Money Electric which was the case with Q: Into the Storm. So, of all people, and after all these years, he was supposed to reveal the biggest Bitcoin Mystery in less than a hundred minutes, give it or take. Who is Miyamoto Musashi? Yeah, I'm fraking (Battlestar Galactica) with you, but for educational purposes only. Miyamoto Musashi was a legendary Japanese samurai "who became renowned through stories of his unique double-bladed swordsmanship and undefeated record in his 62 duels." More importantly, he wrote "The Book of Five Rings," and was "eternalized" as one of the most well-known Joe Rogan's tattoos. By default, the creators of revolutionary things have to be larger-than-life characters themselves. Can you imagine the iPhone without Steve Jobs or SpaceX without Elon Musk? Thus, Satoshi Nakamoto and Bitcoin. There's a scene in the documentary where Cullen Hoback is on the phone with someone, a producer, or whoever is calling the shots, trying to sell his WOW moment—we got him! It was obvious that Adam Back and especially Peter Todd were tossing him around mercilessly. After some time, it stopped being fun and became painful to watch this who-is-Satoshi-Nakamoto game. All that time and money could've been put to much better use in explaining why the block size in blockchain is so damn important. This way we were left with more trivial questions than important answers. Some Mysteries Are Better Left Unsolved Whether Satoshi Nakamoto is one person or a team of people is irrelevant. The myth about Satoshi's 1M BTC is an old and boring story. If you haven't created anything in your life no matter how small and insignificant it may seem to you or enjoyed some magnificent creation first-hand, then you're going to have a problem accepting and realizing why the creations become more important to the creators themselves. Sometimes, I wanted to be published under a pen name or I had to as a ghostwriter. It's both fun and exciting. Gamers care more about their nicks and avatars than anything in the world. I'm no different: Let's wrap this Bitcoin story with the best reply on Reddit. You see, some people have to write one line only and be remembered, while others can write books and be forgotten. And, that's OK. We may never find out who created Bitcoin, but we hopefully know why. And, that's OK too. Till the next HBO documentary and crypto revolutionary, in tech we trust, Bitcoin is a blast! Here's the thing about HBO documentaries. The best thing about them is - the production value. The worst thing is - the production value. What's that supposed to mean? Well, you know what they say. After a series of award-winning and jaw-dropping HBO documentaries, such as The Inventor, Leaving Neverland, Going Clear, Beware the Slenderman, and At the Heart of Gold, we were supposed to be blown away with - Money Electric: The Bitcoin Mystery. It didn't happen. Yeah, I've spent more time watching HBO than reading about blockchain. That's why my articles about Bitcoin always play it safe. I share true stories of how Bitcoin impacted the lives of people I know personally or the challenges of Bitcoin acceptance, and similar stuff the MAXIs won't tear me apart for. After spending just one day reading about what blockchain is and how it works from "Cryptocurrency Investing For Dummies" by Kiana Danial, which I couldn't recommend more, I realized that it wasn't rocket science. However, you can't just show a few cubes, which looked as if they were taken out of the movie "Cube," and call it a blockchain day. Back to the production value because it is important. Look, I don't know what happened to the author, Cullen Hoback, after Terms and Conditions May Apply, but at least he didn't stretch Money Electric which was the case with Q: Into the Storm. So, of all people, and after all these years, he was supposed to reveal the biggest Bitcoin Mystery in less than a hundred minutes, give it or take. Who is Miyamoto Musashi? Yeah, I'm fraking (Battlestar Galactica) with you, but for educational purposes only. Miyamoto Musashi was a legendary Japanese samurai "who became renowned through stories of his unique double-bladed swordsmanship and undefeated record in his 62 duels." More importantly, he wrote "The Book of Five Rings," and was "eternalized" as one of the most well-known Joe Rogan's tattoos. double-bladed swordsmanship and undefeated record By default, the creators of revolutionary things have to be larger-than-life characters themselves. Can you imagine the iPhone without Steve Jobs or SpaceX without Elon Musk? Thus, Satoshi Nakamoto and Bitcoin. There's a scene in the documentary where Cullen Hoback is on the phone with someone, a producer, or whoever is calling the shots, trying to sell his WOW moment—we got him! It was obvious that Adam Back and especially Peter Todd were tossing him around mercilessly. After some time, it stopped being fun and became painful to watch this who-is-Satoshi-Nakamoto game. All that time and money could've been put to much better use in explaining why the block size in blockchain is so damn important. This way we were left with more trivial questions than important answers. Some Mysteries Are Better Left Unsolved Whether Satoshi Nakamoto is one person or a team of people is irrelevant. The myth about Satoshi's 1M BTC is an old and boring story. If you haven't created anything in your life no matter how small and insignificant it may seem to you or enjoyed some magnificent creation first-hand, then you're going to have a problem accepting and realizing why the creations become more important to the creators themselves. Sometimes, I wanted to be published under a pen name or I had to as a ghostwriter. It's both fun and exciting. Gamers care more about their nicks and avatars than anything in the world. I'm no different: Let's wrap this Bitcoin story with the best reply on Reddit . You see, some people have to write one line only and be remembered, while others can write books and be forgotten. And, that's OK. We may never find out who created Bitcoin, but we hopefully know why. And, that's OK too. Reddit Till the next HBO documentary and crypto revolutionary, in tech we trust, Bitcoin is a blast!

I Watched HBO’s ‘Money Electric’ on Bitcoin: Here’s Why You Might Want To Skip It (or Not)

Gledao sam HBO-ov 'Money Electric' o Bitcoinu: Evo zašto biste ga mogli preskočiti (ili ne)

Sledoval jsem „Money Electric“ HBO o bitcoinech: Zde je důvod, proč byste to možná chtěli přeskočit (nebo ne)

Παρακολούθησα το «Money Electric» του HBO στο Bitcoin: Εδώ είναι γιατί μπορεί να θέλετε να το παραλείψετε (ή όχι)

Vi “Money Electric” de HBO sobre Bitcoin: esta es la razón por la que quizás quieras saltártelo (o no)

Ho guardato "Money Electric" della HBO su Bitcoin: ecco perché potresti volerlo saltare (o no)

ビットコインに関する HBO の「Money Electric」を視聴しました。視聴しないほうがよい理由 (またはしないほうがよい理由)

ຂ້ອຍໄດ້ເບິ່ງ 'Money Electric' ຂອງ HBO ໃນ Bitcoin: ນີ້ແມ່ນເຫດຜົນທີ່ເຈົ້າອາດຈະຕ້ອງການຂ້າມມັນ (ຫຼືບໍ່)

Ik heb HBO's 'Money Electric' op Bitcoin bekeken: dit is waarom je het misschien wilt overslaan (of niet)

Я посмотрел сериал HBO «Money Electric» на биткоины: вот почему вы можете захотеть его пропустить (или нет)

මම Bitcoin මත HBO හි 'Money Electric' නැරඹුවෙමි: මෙන්න ඔබට එය මඟ හැරීමට අවශ්‍ය විය හැක්කේ (නැතහොත්)

Men HBO-ning Bitcoin-da "Money Electric" filmini tomosha qildim: Mana nima uchun uni o'tkazib yuborishni xohlashingiz mumkin (yoki yo'q)

Ngibuke i-'Money Electric' ye-HBO ku-Bitcoin: Nakhu Kungani Ungase Ufune Ukuyeqa (noma Cha)

The HackerNoon Newsletter: How to Improve Your Data Literacy Skills (10/19/2024)

Are you data literate? An easy way to tell is to ask yourself whether you know how to source, analyze, and share quantitative information. If you don’t, you should learn how. The good news is the process is easy, even if you’re not a numbers person. Here’s what you need to do to improve your data literacy skills. What Is Data Literacy? Data literacy is the ability to interpret and interact with information meaningfully. You work with, perform operations on, and extract insights from it. Like regular literacy, there are multiple levels of competency, ranging from basic to adept. In other words, you don’t need to be an expert in the field to demonstrate an adequate understanding. What is poor data literacy? It is the inability to interpret, interact with, and understand information. If you work in an office or administrative setting, this skills gap might manifest as needing help deciphering graphs and reports. You may have difficulty distinguishing valuable insights from the noise. At the very least, you might struggle to understand analytics. For the record, you shouldn’t confuse data literacy with analytics. While analysis technically falls under the umbrella of being data literate, it’s only a tiny part of what you need to know. Arguably, uncovering patterns or correlations is the most useful thing you can do with a dataset, but it’s not the only thing. You shouldn’t feel discouraged if you’re not data literate yet. After all, even today’s high-ranking professionals aren’t. Only half of C-suite executives feel confident in their data literacy skills, with 45% admitting they make decisions based on gut feelings instead of facts. What Are Data Literacy Skills? While dozens of data literacy skills exist, you only need a handful to start your educational journey. Mastering the basics helps you build a solid knowledge foundation. 1. Critical Thinking Critical thinking is arguably one of the most important skills because there’s simply too much data floating around, much of which is inaccurate, outdated, or irrelevant. Knowing what questions to ask can help you determine whether you want to use a source, reference a website, or perform operations on a dataset. 2. Problem-Solving Sometimes, data is incomplete or messy — it doesn’t always fit into spreadsheets neatly. Problem-solving skills can help you make those metrics workable. While you’ll have to rely on mathematics often, you don’t need to excel in the subject. You don’t necessarily even need to know how to perform calculations. More often than not, knowing how to get to the answer is enough. Say you’re trying to find how much one figure increased over time to determine growth. Instead of memorizing a formula, you’d simply use a percentage change calculator. Creatively approaching problems can help you interact with data meaningfully. 3. Communication Communication is one of the most essential skills to learn because all worthwhile findings are reviewed and replicated. These processes can help you identify mistakes and uncover opportunities. At work, viewing information as a shared resource can help foster a culture of collaboration and communication. 4. Visualization Visualization is similar to communication in that it helps you get your point across. The key difference is you prioritize storytelling. Graphs, charts, and tables make plain metrics more interesting. They also make complex topics easier to comprehend — numbers seem more real when accompanied by informative illustrations. Why Data Literacy Matters Data literacy isn’t a term you come across daily, so it might not seem important. In reality, you could consider it a life skill since you interact with information all day, every day. Utility bills, news reports, ingredient labels, medication dosages, online polls, and product recommendations are good examples. You may even read reports and process spreadsheets at work. These skills can help you better understand the world because you live in the digital age. Even if you don’t realize it, you absorb quantitative data daily. Knowing how to interpret, analyze, and explain it can help you turn seemingly meaningless numbers into meaningful insights. You learn how to develop arguments, follow along at work, and spot misinformation better. Interestingly, possessing data literacy skills also makes it easier to keep up with the latest technology trends. You need a solid understanding to get involved in trends like artificial intelligence and the Internet of Things. Recognizing the importance of sourcing, dataset cleaning and analytics goes a long way. Frankly, becoming data literate is important because there’s too much information. Experts estimate the total amount created, copied and consumed will go from 64.2 zettabytes in 2020 to 180 zettabytes in 2025 — a 180% increase in just five years. Knowing what makes metrics relevant, accurate, and reliable is essential. What You Need to Understand About Data Before you can become data literate, there are a few fundamentals you must understand. How It Works There are multiple data types, including datasets, physical documents, studies, databases and records. At its most basic, it is broken into two categories — qualitative or quantitative. Whether you leverage numbers or descriptions, you’re working with information. In data science, data is often either described as structured or unstructured. The first fits neatly into spreadsheet tables, often containing numbers, dates or words. The latter is messier. It can be social media posts, audio clips, emails, videos or documents. Where It Goes You often have to collect data to extract any meaningful results. While tons of information is available online, it may not be recent, accurate or specific enough to be useful to you. Common sources are surveys, records, studies and analytics. A few decades ago, information was usually physical — people stored records in filing cabinets and documents in briefcases. Now, almost everything is digitally stored on hard drives or kept in the cloud for remote access. What It Does You can do a lot to a dataset, including processing, analysis and insight extraction. These use cases are probably the main reason your employer collects information, unless they’re a financial or health care institution and are required to do so by law. Before you can do anything with data, you need to clean it, which involves eliminating outliers, filling in missing fields and transforming any mistyped figures. It’s a lengthy process. According to one survey, data scientists spend 39% of their time on preparation and cleaning. How to Become Data Literate Where should you go to improve your data literacy? Since just under half of employees say their employer has offered data training courses, there may not be many opportunities at work. Thankfully, you don’t need predefined courses to develop the core skills. The best way to learn is to practice — even if you’re inexperienced. Consider getting a dataset online to practice on. If you don’t know what to use, try the weather. It makes for good practice because there are always recent, accurate forecasts. At first, your goal should be to evaluate the raw numbers to ensure you understand what’s in front of you. Then, you can transition to cleaning, performing analysis and extracting insights. Figure out what questions you want to ask. Is the change from the first day to the last significant? What percentage of days were overcast? If you feel like taking things a step further, try analyzing two datasets at once — one on weather and another on beachgoers. Does the ultraviolet index affect how busy the beach is? While that example may seem overly simplistic, analysis often is. Whether you’re trying to extract insights on employee retention, construction material cost, return fraud or the housing market, you can uncover patterns by asking straightforward questions and plugging numbers into the correct formulas. Your practice isn’t finished when you produce your findings. At that point, you proceed to visualization. The best way to get better at reading graphs and tables is to make your own. This way, you’ll get used to the fundamentals, allowing you to instantly recognize a visual’s shared characteristics when someone else presents information. Making Yourself Data Literate You won’t improve your data literacy skills overnight. However, even though it will likely be time-consuming, it doesn’t have to be complicated. To make the process more enjoyable, work with information that interests you. There are patterns, trends and insights to uncover in every industry, and there is a wealth of information online for free. Are you data literate? An easy way to tell is to ask yourself whether you know how to source, analyze, and share quantitative information. If you don’t, you should learn how. Are you data literate? An easy way to tell is to ask yourself whether you know how to source, analyze, and share quantitative information. If you don’t, you should learn how. The good news is the process is easy, even if you’re not a numbers person. Here’s what you need to do to improve your data literacy skills. What Is Data Literacy? Data literacy is the ability to interpret and interact with information meaningfully. You work with, perform operations on, and extract insights from it. Like regular literacy, there are multiple levels of competency, ranging from basic to adept. In other words, you don’t need to be an expert in the field to demonstrate an adequate understanding. What is poor data literacy? It is the inability to interpret, interact with, and understand information. If you work in an office or administrative setting, this skills gap might manifest as needing help deciphering graphs and reports. You may have difficulty distinguishing valuable insights from the noise. At the very least, you might struggle to understand analytics. For the record, you shouldn’t confuse data literacy with analytics. While analysis technically falls under the umbrella of being data literate, it’s only a tiny part of what you need to know. Arguably, uncovering patterns or correlations is the most useful thing you can do with a dataset, but it’s not the only thing. You shouldn’t feel discouraged if you’re not data literate yet. After all, even today’s high-ranking professionals aren’t. Only half of C-suite executives feel confident in their data literacy skills, with 45% admitting they make decisions based on gut feelings instead of facts. half of C-suite executives half of C-suite executives What Are Data Literacy Skills? While dozens of data literacy skills exist, you only need a handful to start your educational journey. Mastering the basics helps you build a solid knowledge foundation. 1. Critical Thinking Critical thinking is arguably one of the most important skills because there’s simply too much data floating around, much of which is inaccurate, outdated, or irrelevant. Knowing what questions to ask can help you determine whether you want to use a source, reference a website, or perform operations on a dataset. 2. Problem-Solving Sometimes, data is incomplete or messy — it doesn’t always fit into spreadsheets neatly. Problem-solving skills can help you make those metrics workable. While you’ll have to rely on mathematics often, you don’t need to excel in the subject. You don’t necessarily even need to know how to perform calculations. More often than not, knowing how to get to the answer is enough. Say you’re trying to find how much one figure increased over time to determine growth. Instead of memorizing a formula, you’d simply use a percentage change calculator. Creatively approaching problems can help you interact with data meaningfully. 3. Communication Communication is one of the most essential skills to learn because all worthwhile findings are reviewed and replicated. These processes can help you identify mistakes and uncover opportunities. At work, viewing information as a shared resource can help foster a culture of collaboration and communication. can help foster a culture can help foster a culture 4. Visualization Visualization is similar to communication in that it helps you get your point across. The key difference is you prioritize storytelling. Graphs, charts, and tables make plain metrics more interesting. They also make complex topics easier to comprehend — numbers seem more real when accompanied by informative illustrations. Why Data Literacy Matters Data literacy isn’t a term you come across daily, so it might not seem important. In reality, you could consider it a life skill since you interact with information all day, every day. Utility bills, news reports, ingredient labels, medication dosages, online polls, and product recommendations are good examples. You may even read reports and process spreadsheets at work. These skills can help you better understand the world because you live in the digital age. Even if you don’t realize it, you absorb quantitative data daily. Knowing how to interpret, analyze, and explain it can help you turn seemingly meaningless numbers into meaningful insights. You learn how to develop arguments, follow along at work, and spot misinformation better. help you better understand the world help you better understand the world Interestingly, possessing data literacy skills also makes it easier to keep up with the latest technology trends. You need a solid understanding to get involved in trends like artificial intelligence and the Internet of Things. Recognizing the importance of sourcing, dataset cleaning and analytics goes a long way. Frankly, becoming data literate is important because there’s too much information. Experts estimate the total amount created, copied and consumed will go from 64.2 zettabytes in 2020 to 180 zettabytes in 2025 — a 180% increase in just five years. Knowing what makes metrics relevant, accurate, and reliable is essential. from 64.2 zettabytes in 2020 from 64.2 zettabytes in 2020 What You Need to Understand About Data Before you can become data literate, there are a few fundamentals you must understand. How It Works There are multiple data types, including datasets, physical documents, studies, databases and records. At its most basic, it is broken into two categories — qualitative or quantitative. Whether you leverage numbers or descriptions, you’re working with information. In data science, data is often either described as structured or unstructured. The first fits neatly into spreadsheet tables, often containing numbers, dates or words. The latter is messier. It can be social media posts, audio clips, emails, videos or documents. Where It Goes You often have to collect data to extract any meaningful results. While tons of information is available online, it may not be recent, accurate or specific enough to be useful to you. Common sources are surveys, records, studies and analytics. A few decades ago, information was usually physical — people stored records in filing cabinets and documents in briefcases. Now, almost everything is digitally stored on hard drives or kept in the cloud for remote access. What It Does You can do a lot to a dataset, including processing, analysis and insight extraction. These use cases are probably the main reason your employer collects information, unless they’re a financial or health care institution and are required to do so by law. Before you can do anything with data, you need to clean it, which involves eliminating outliers, filling in missing fields and transforming any mistyped figures. It’s a lengthy process. According to one survey, data scientists spend 39% of their time on preparation and cleaning. spend 39% of their time spend 39% of their time How to Become Data Literate Where should you go to improve your data literacy? Since just under half of employees say their employer has offered data training courses, there may not be many opportunities at work. Thankfully, you don’t need predefined courses to develop the core skills. The best way to learn is to practice — even if you’re inexperienced. just under half of employees just under half of employees Consider getting a dataset online to practice on. If you don’t know what to use, try the weather. It makes for good practice because there are always recent, accurate forecasts. At first, your goal should be to evaluate the raw numbers to ensure you understand what’s in front of you. Then, you can transition to cleaning, performing analysis and extracting insights. Figure out what questions you want to ask. Is the change from the first day to the last significant? What percentage of days were overcast? If you feel like taking things a step further, try analyzing two datasets at once — one on weather and another on beachgoers. Does the ultraviolet index affect how busy the beach is? While that example may seem overly simplistic, analysis often is. Whether you’re trying to extract insights on employee retention, construction material cost, return fraud or the housing market, you can uncover patterns by asking straightforward questions and plugging numbers into the correct formulas. Your practice isn’t finished when you produce your findings. At that point, you proceed to visualization. The best way to get better at reading graphs and tables is to make your own. This way, you’ll get used to the fundamentals, allowing you to instantly recognize a visual’s shared characteristics when someone else presents information. Making Yourself Data Literate You won’t improve your data literacy skills overnight. However, even though it will likely be time-consuming, it doesn’t have to be complicated. To make the process more enjoyable, work with information that interests you. There are patterns, trends and insights to uncover in every industry, and there is a wealth of information online for free.

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