There is also no need to question the data.
If the data should be questioned, then the problem will have no single correct answer, which doesn’t fit well with traditional academic STEM training.
(By contrast, social science graduates are better trained to handle complexity and incomplete data.
)In a recent blog post, I showed how a data analyst can use critical thinking to question the data and avoid making embarrassing erroneous conclusions.
Analysts at the National Highway Traffic Safety Administration (NHTSA) failed to notice gaping holes in the data submitted by Tesla when they endorsed Tesla’s claim that the auto-pilot feature would reduce crash rates by 40 percent.
An independent consultant succeeded in getting the data released, and noticed a large number of blank entries.
When the missing values were imputed using a standard method (mean imputation), the reported benefit of auto-pilot vanished entirely.
Bridging this skill gap is a key goal of mine when I started Principal Analytics Prep.
We accomplish this by fostering in-class, hands-on learning with practitioners who have years of practical work experience, and finding students with diverse backgrounds to consider both science and social science approaches to problem solving.
In Part 2, I provide materials to help you prepare for case interviews that hiring managers use to test critical thinking.
This post was originally published at Kaiser Fung’s blog (https://www.
principalanalyticsprep.
com/news/critical-thinking-the-most-desired-skill-in-data-science) and is slightly modified.
Bio: Kaiser Fung is the founder of Principal Analytics Prep, a leading data analytics bootcamp; best-selling author of Numbers Rule Your World; and the author of Junk Charts (https://www.
junkcharts.
com), the popular data visualization blog.
Twitter: @junkchartsYoutube: bitly.
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