Introduction to Data
Explore the world of data through case studies, and learn about collection methods and data quality.
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Key Concepts
Review core concepts you need to learn to master this subject
Data Gaps
Addressing Bias
What is Statistics?
Statistics at work
Garbage In, Garbage Out
Binary Categorical Variables
Categorical Variables
Quantitative Vs. Categorical Variables
Data Gaps
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
- 1Welcome 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…
- 3One 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…
- 4Now 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…
- 5So 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…
- 6Okay, 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…
- 7Before 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 …
- 9In 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…
- 10Dr. 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…
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