Key Concepts

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Data Gaps

The ability to separate good, mediocre, and poor quality data is a crucial data literacy skill. Data-driven conclusions are only as strong, robust, and well-supported as the data behind them. This is also often referred to with the phrase “garbage in, garbage out.”

Case Studies in Data Literacy
Lesson 1 of 2
  1. 1
    Welcome to this course on data literacy! First things first, let’s answer a crucial question: Why is data literacy important? In other words, why should anyone aim to be data literate? There are a…
  2. 2
    Garbage in, garbage out is a data-world phrase that means “our data-driven conclusions are only as strong, robust, and well-supported as the data behind them.” For example: we have a lot of da…
  3. 3
    One question the data on heart attacks might prompt is “why did the trials have only 38% female participation?” In part, for historical reasons: in the 1950s, pregnant women in Europe and Canada w…
  4. 4
    Now let’s check out a case study that showcases the value of data literacy in the legal system. Big, amorphous injustices like hiring discrimination are hard to prove in court. Hiring discriminati…
  5. 5
    So how did Elaine Shoben show that discrimination was at play in hiring decisions? It’s a bit heavy on the legal jargon, but we can break it down to see how it works. 1. First, she said that we ca…
  6. 6
    Okay, we’ve walked through recognizing data quality and bias in healthcare and using statistics to answer big legal questions. Where else does data literacy come into play? Data visualization is o…
  7. 7
    Before we pick apart these visualizations, it’s worth saying that hindsight is 20/20. If it were as simple as “obviously, the O-rings were going to fail,” then the Challenger would never have been …
  8. 8
    Wow, we’ve made it through a lot of content! Let’s kick off the final section: analysis, or turning data into useful information. The key question of analysis is, “what’s the takeaway?” Let’s sta…
  9. 9
    In the world of data, we’ll hear time and time again that “correlation does not equal causation.” In other words, while two events might be connected or related, that doesn’t mean they’re in a caus…
  10. 10
    Dr. John Snow’s causal analysis breakthrough started with how he visualized his data: he organized cholera death records by location rather than by time, which was more common. He made a map, and d…
  11. 11
    Wowie, we’ve covered a lot! The road to confident data literacy is full of fascinating examples, and knowledge that helps us make sense of one of the most powerful tools of our age: data. In this …

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