Are you looking to land that next job or hone your statistics interview skills to stay sharp? Get ready to master classic interview concepts ranging from conditional probabilities to A/B testing to the bias-variance tradeoff, and much more! You’ll work with a diverse collection of datasets including web-based experiment results and Australian weather data. Following the course, you’ll be able to confidently walk into your next interview and tackle any statistics questions with the help of Python!
Probability and Sampling DistributionsFree
This chapter kicks the course off by reviewing conditional probabilities, Bayes' theorem, and central limit theorem. Along the way, you will learn how to handle questions that work with commonly referenced probability distributions.
Exploratory Data Analysis
In this chapter, you will prepare for statistical concepts related to exploratory data analysis. The topics include descriptive statistics, dealing with categorical variables, and relationships between variables. The exercises will prepare you for an analytical assessment or stats-based coding question.
Statistical Experiments and Significance Testing
Prepare to dive deeper into crucial concepts regarding experiments and testing by reviewing confidence intervals, hypothesis testing, multiple tests, and the role that power and sample size play. We'll also discuss types of errors, and what they mean in practice.Confidence intervals50 xpConfidence interval by hand100 xpApplying confidence intervals100 xpHypothesis testing50 xpOne tailed z-test100 xpTwo tailed t-test100 xpPower and sample size50 xpEffect on type II error50 xpCalculating sample size100 xpVisualizing the relationship100 xpMultiple testing50 xpCalculating error rates100 xpBonferroni correction100 xp
Regression and Classification
Wrapping up, we'll address concepts related closely to regression and classification models. The chapter begins by reviewing fundamental machine learning algorithms and quickly ramps up to model evaluation, dealing with special cases, and the bias-variance tradeoff.Regression models50 xpLinear regression100 xpLogistic regression100 xpEvaluating models50 xpRegression evaluation100 xpClassification evaluation100 xpMissing data and outliers50 xpHandling null values100 xpIdentifying outliers100 xpBias-variance tradeoff50 xpTest and training error50 xpVisualizing the tradeoff100 xpWrapping up50 xp