Overview

Welcome to the course on Statistics For Data Scientists!Learn about the key concepts in statistics, and how to apply them to your data analysis.A highly practical and hands-on approach.A focus on building an intuitive understanding of each topic.Learn to use Python code to simulate various scenarios in a plug-and-play manner.What is included in the course:Detailed Course Notes (100 page textbook with 50+ illustrative figures)Deck of 360 slides Lectures with 10h+ content spread over 40+ videosAll of the code in Jupyter Notebooks (7 notebooks, 2000+ lines of code)Bonus Chapter: Introduction to Machine LearningTopics that the course covers:The HistogramGenerating artificial Data setsThe central tenet of StatisticsThe Central Limit TheoremDistribution functionsPercentilesData RangesCumulative Distribution FunctionDifferent Distribution types:Normal DistributionUniform DistributionExponential DistributionPoisson DistributionBernoulli DistributionRayleigh DistributionStatistical TestingReasoning behind statistical testingP-valueStatistical SignificanceDifferent Statistical Tests:Shapiro-Wilk testLevene's testStudent T-test/ Welsh T-testANOVA testKolmogorov Smirnov testNon-parametric testsTwo real-life examplesDetect a biased coin with 95% certaintyReal-life A/B testingCorrelationLinear correlation - Pearson correlation coefficient + alternativesCategorical correlation - Chi-Squared test + contingency tablesEXTRA: Regression and intro to Machine LearningLinear RegressionLogistic Regression + ML pipelineWho is this course for:Students on a data science track, or any other technical field.Professionals that want to pivot into a data science career.Managers that want to be able to make data driven decisions.Practicing Data Scientists that want to add this value skill to their tool belt.

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