How Much Statistics Do You REALLY Need for Data Science?

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In my last video I discussed the fact that statistics is a must-know component of the broad, multidisciplinary data science skill set. If you missed that video you can find it here: https://www.youtube.com/watch?v=DLjPPNhU1aI

A book I highly recommend, especially for the non-mathematical reader, is "How Not to be Wrong" by Jordan Ellenberg. Find it here: https://amzn.to/2U1FjpQ

However, not everyone going into data science necessarily has a statistics background. It begs an obvious follow-up question: how much statistics do you REALLY need for data science? Education is valuable but not every single thing you learn in a traditional statistics degree is a hard and fast requirement. Here are, from my perspective, the core skills you need:

Fundamentals
- Probability calculations including conditional probability/Bayes rule and the Central Limit Theorem
- Basic understanding of distributions including properties of random variables such as expected value and variance
- Full confidence interval framework
- Full hypothesis testing framework including p-values, conclusions, Type I and Type II error

Tools
- Linear models (how to setup, interpret, iterate)
- Machine learning models including setting them up in a programming language from pre-processing to outputting results, also understanding the bias-variance tradeoff and how to address over (and under) fitting
- Survival analysis

Reasoning
- Assumptions of tests and models used
- How bias affects results
- Confounding variables and Simpson's Paradox

#statistics #datascience #StatisticsForDataScience

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Видео How Much Statistics Do You REALLY Need for Data Science? канала RichardOnData