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Practicing Statistics Interview Questions in Python

Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.

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4 Hours15 Videos46 Exercises11,184 Learners
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Course Description

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!

  1. 1

    Probability and Sampling Distributions


    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.

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    Conditional probabilities
    50 xp
    Setting up problems
    50 xp
    Bayes' theorem applied
    100 xp
    Central limit theorem
    50 xp
    Samples from a rolled die
    100 xp
    Simulating central limit theorem
    100 xp
    Probability distributions
    50 xp
    Bernoulli distribution
    100 xp
    Binomial distribution
    100 xp
    Normal distribution
    100 xp
  2. 2

    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.

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  3. 3

    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.

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  4. 4

    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.

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Amy PetersonMona Khalil


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Conor Dewey

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Conor is a product manager and data scientist previously at Squarespace and Unity Technologies. Subscribe to his newsletter for weekly curated links on data science, analytics, and product growth.
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