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Subscribe to RichardOnData here: / @richardondata 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: • What Is a Data Scientist Exactly? 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 PayPal: [email protected] Patreon: / richardondata BTC: 3LM5d1vibhp1F7pcxAFX8Ys1DM6XLUoNVL ETH: 0x3CfC599C4c1040963B644780a0E62d45999bE9D8 LTC: MH8yPjvSmKvpmRRmufofjRB9hnRAFHfx32