Learn Data
> Student or parent? Teach yourself.
A number of resources (many free) exist online to learn data literacy on your own. While these resources are no replacement for learning data in the classroom with a qualified educator, they can help build a foundational knowledge for students to pursue career opportunities and engage in our increasingly data-driven world with confidence. Below is a list of introductory content meant to introduce data literacy. Many sites (DataCamp, Khan Academy) have options for exploring more advanced, code-dependent techniques in data analysis, data visualization, and machine learning. Many schools may also offer elective credit for completing such programs; make sure to check with your teacher or administrator to see if such an option is available.
A few guiding thoughts for approaching your data education:
-
Building data sense—start here, and build an understanding of the "what, when, where, and why" of data.
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Learn some statistics—a few concepts are necessary for analyzing data, whether your own work or others.
-
Learn some tools—whether spreadsheets (Excel, Google Sheets) or code (R, Python, Tableau), all are useful
(Have a resource to add?)
Learn Data
> Student or parent? Teach and train yourself.
A number of resources (many free) exist online to learn data literacy on your own. While these resources are no replacement for learning data in the classroom with a qualified educator, they can help build a foundational knowledge for students to pursue career opportunities and engage in our increasingly data-driven world with confidence. Below is a list of introductory content meant to introduce data literacy. Many sites (DataCamp, Khan Academy) have options for exploring more advanced, code-dependent techniques in data analysis, data visualization, and machine learning. Many schools may also offer elective credit for completing such programs; make sure to check with your teacher or administrator to see if such an option is available.
A few guiding thoughts for approaching your data education:
1. Building data sense—start here, and build an understanding of the "what, when, where, and why" of data.
2. Learn some statistics—a few concepts are necessary for analyzing data, whether your own work or others'.
3. Learn some tools—whether spreadsheets (Excel, Google Sheets) or code (R, Python, Tableau), all are useful
What tools are right for me? (The short answer is, "all of them!", but in case you're confused):
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Spreadsheets (Excel, Google Sheets): the long-standing workhorses of the business world, good for smaller amounts of data where showing your observations and visually manipulating them is useful. Some examples include budgets, lists of contacts, qualitative observations, etc.
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General Programming (R, Python): these softwares are ideal for larger datasets, with the ability to complete a wider range of tasks. Both are open-source, free softwares that can both do the basics of data analysis (data cleaning, statistical analysis, and visualization) in addition to other programming functions (web-scraping, web design, app construction, to name a few)—making them the most popular softwares in data analysis.
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Visualization Software (Tableau): Tableau and other like softwares are focused on user-friendly data visualization, while still being able to handle larger amounts of data than spreadsheets. Tableau in particular offers an especially wide-range of pre-set data visualization functions, while minimizing the code needed to do so for the user.
These are just some mainstream examples of the many softwares that can be used for data analysis. See this article (or really any other on Google) for some help. Ultimately, you can't go wrong in choosing a starting point—once you know one, it's easier to learn the others.
Looking for a good software for younger students? Try CODAP—an intuitive drag-and-drop program that blends some of the key features of each of the above—for teaching students in K-5 the intuition of data.
(Have a resource to add?)
Crash Course Data Literacy Study Hall
Data is everywhere. We expect it on our computers and in science labs, but it’s also in things like the food we eat, the websites we surf, and the cars we drive. We use it at work, school, or even just scrolling through YouTube. In this 15 episode playlist, your host Jessica Pucci will help us learn to think about the data we're presented with.
6-8, 9-12
Full Course
Grade:
Type:
Software:
N/A
Cost:
Free
Data for Early School (Khan Academy)
Learn how to measure length, tell time, count money, and make graphs.
K-5
Unit
Grade:
Type:
Software:
N/A
Cost:
Free
Data for 2nd Grade (Khan Academy)
Practice solving problems involving picture graphs, bar graphs, and line plots.
K-5
Unit
Grade:
Type:
Software:
N/A
Cost:
Free
Data for 3rd Grade (Khan Academy)
Learn how to represent and interpret data.
K-5
Unit
Grade:
Type:
Software:
N/A
Cost:
Free
Data and Statistics for 6th Grade (Khan Academy)
In statistics, we try to make sense of the world by collecting, organizing, analyzing, and presenting large amounts of data. This unit is a discussion of statistics and data science, including box and whisker plots, bar charts, pictographs, line graphs, and dot plots.
6-8
Unit
Grade:
Type:
Software:
N/A
Cost:
Free
Data and Probability for 7th Grade (Khan Academy)
This introduction to probability and statistics explores probability models, sample spaces, compound events, random samples, and a whole lot more—a key theoretical underpinning for proper data analysis.
6-8
Unit
Grade:
Type:
Software:
N/A
Cost:
Free
Data and Modeling for 8th Grade (Khan Academy)
In this topic, we will learn about scatter plots, lines of best fit, and two-way tables.
6-8
Unit
Grade:
Type:
Software:
N/A
Cost:
Free
High School Statistics (Khan Academy)
Khan Academy's full statistics course for high school students. While not exclusively focused on data science, it introduces the theoretical foundation needed to analyze data and think about how it is used most effectively, with applications to more practical data work.
9-12
Full Course
Grade:
Type:
Software:
N/A
Cost:
Free
Data Science for Everyone
What is data science, why is it so popular, and why did the Harvard Business Review hail it as the “sexiest job of the 21st century”? In this non-technical course, you’ll be introduced to everything you were ever too afraid to ask about this fast-growing and exciting field, without needing to write a single line of code. Through hands-on exercises, you’ll learn about the different data scientist roles, foundational topics like A/B testing, time series analysis, and machine learning, and how data scientists extract knowledge and insights from real-world data. So don’t be put off by the buzzwords. Start learning, gain skills in this hugely in-demand field, and discover why data science is for everyone!
9-12
Mini Course
Grade:
Type:
Software:
N/A
Cost:
$25 / month
Spreadsheet Fundamentals
Join millions of people using Google Sheets and Microsoft Excel on a daily basis and learn the fundamental skills necessary to analyze data in spreadsheets! The skills you learn in these courses will empower you to join tables, summarize data, and answer your data analysis and data science questions.
9-12
Mini Course
Grade:
Type:
Software:
Excel or GSheets
Cost:
$25 / month
Data Visualization for Everyone
Visualizing data using charts, graphs, and maps is one of the most impactful ways to communicate complex data. In this course, you’ll learn how to choose the best visualization for your dataset, and how to interpret common plot types like histograms, scatter plots, line plots and bar plots. You'll also learn about best practices for using colors and shapes in your plots, and how to avoid common pitfalls. Through hands-on exercises, you'll visually explore over 20 datasets including global life expectancies, Los Angeles home prices, ESPN's 100 most famous athletes, and the greatest hip-hop songs of all time.
9-12
Mini Course
Grade:
Type:
Software:
N/A
Cost:
$25 / month
Machine Learning for Everyone
What's behind the machine learning hype? In this non-technical course, you’ll learn everything you’ve been too afraid to ask about machine learning. There’s no coding required. Hands-on exercises will help you get past the jargon and learn how this exciting technology powers everything from self-driving cars to your personal Amazon shopping suggestions. How does machine learning work, when can you use it, and what is the difference between AI and machine learning? They’re all covered. Gain skills in this hugely in-demand and influential field, and discover why machine learning is for everyone!
9-12
Mini Course
Grade:
Type:
Software:
N/A
Cost:
$25 / month
Introduction to Python
Python is a general-purpose programming language that is becoming ever more popular for data science. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike other Python tutorials, this course focuses on Python specifically for data science. In our Introduction to Python course, you’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses.
9-12
Full Course
Grade:
Type:
Software:
Python
Cost:
$25 / month
Introduction to R
In Introduction to R, you will master the basics of this widely used open source language, including factors, lists, and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis. Oracle estimated over 2 million R users worldwide in 2012, cementing R as a leading programming language in statistics and data science.
9-12
Full Course
Grade:
Type:
Software:
R
Cost:
$25 / month
Introduction to Tableau
Tableau is a widely used business intelligence (BI) and analytics software trusted by companies like Amazon, Experian, and Unilever to explore, visualize, and securely share data in the form of Workbooks and Dashboards. With its user-friendly drag-and-drop functionality it can be used by everyone to quickly clean, analyze, and visualize your team’s data. You’ll learn how to navigate Tableau’s interface and connect and present data using easy-to-understand visualizations.
9-12
Full Course
Grade:
Type:
Software:
Tableau
Cost:
$25 / month
Importing & Cleaning Data with R
Understanding how to prep your data is an essential skill when working in R. It’s what you have to do before you can reveal the insights that matter. In this track, you’ll learn how to import your data from a variety of sources, including .csv, .xls, text files, and more. You'll then gain the skills you'll need to prepare your data for analysis, including converting data types, filling in missing values, and using fuzzy string matching.
9-12
Full Course
Grade:
Type:
Software:
R (others available)
Cost:
$25 / month
Statistics Fundamentals with R
Statistics is the study of how best to collect, analyze, and draw conclusions from data. A strong foundation will serve you well, no matter what industry you work in. In this beginner’s track, you'll learn the concepts, topics, and techniques used by data scientists and statisticians every day—including observational studies and experiments, correlation and regression, exploratory data analysis, and inference. You’ll also develop your stats skills by working with real-world data from National Health surveys from the CDC, instructor evaluations, and Italian restaurant reviews in NYC.
9-12
Full Course
Grade:
Type:
Software:
R (others available)
Cost:
$25 / month
Data Visualization with R
Bring your data into focus. Develop the invaluable skills you need to analyze and display data using R—essential when communicating insights and discoveries to non-technical stakeholders. In this track, you'll learn how to create and modify plots using ggplot2, optimizing the aesthetics and geoms to help you build beautiful and accurate visualizations. From here you’ll progress your ggplot2 skills and learn how to calculate statistics and use facets and coordinates. You'll also learn some best practices for creating visualizations, including three plot types you should avoid.
9-12
Full Course
Grade:
Type:
Software:
R (others available)
Cost:
$25 / month