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:
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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.
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Learn some tools—whether spreadsheets (Excel, Google Sheets) or code (R, Python, Tableau), all are useful
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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.
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