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Subscribe to RichardOnData here: https://www.youtube.com/channel/UCKPyg5gsnt6h0aA8EBw3i6A?sub_confirmation=1 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://www.amazon.com/gp/product/0143127535?ie=UTF8&camp=1789&creative=9325&creativeASIN=0143127535 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 PayPal: [email protected] Patreon: https://www.patreon.com/richardondata BTC: 3LM5d1vibhp1F7pcxAFX8Ys1DM6XLUoNVL ETH: 0x3CfC599C4c1040963B644780a0E62d45999bE9D8 LTC: MH8yPjvSmKvpmRRmufofjRB9hnRAFHfx32
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