Skip to main content
HomePython

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.

Start Course for Free
4 hours15 videos46 exercises14,942 learnersTrophyStatement of Accomplishment

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
Group

Training 2 or more people?

Try DataCamp for Business

Loved by learners at thousands of companies

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!
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.
DataCamp for BusinessFor a bespoke solution book a demo.
  1. 1

    Probability and Sampling Distributions

    Free

    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.

    Play Chapter Now
    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.

    Play Chapter Now
    Descriptive statistics
    50 xp
    Mean or median
    100 xp
    Standard deviation by hand
    100 xp
    Categorical data
    50 xp
    Encoding techniques
    100 xp
    Exploring laptop prices
    100 xp
    Two or more variables
    50 xp
    Types of relationships
    100 xp
    Pearson correlation
    100 xp
    Sensitivity to outliers
    100 xp
  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.

    Play Chapter Now
    Confidence intervals
    50 xp
    Confidence interval by hand
    100 xp
    Applying confidence intervals
    100 xp
    Hypothesis testing
    50 xp
    One tailed z-test
    100 xp
    Two tailed t-test
    100 xp
    Power and sample size
    50 xp
    Effect on type II error
    50 xp
    Calculating sample size
    100 xp
    Visualizing the relationship
    100 xp
    Multiple testing
    50 xp
    Calculating error rates
    100 xp
    Bonferroni correction
    100 xp
  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.

    Play Chapter Now
    Regression models
    50 xp
    Linear regression
    100 xp
    Logistic regression
    100 xp
    Evaluating models
    50 xp
    Regression evaluation
    100 xp
    Classification evaluation
    100 xp
    Missing data and outliers
    50 xp
    Handling null values
    100 xp
    Identifying outliers
    100 xp
    Bias-variance tradeoff
    50 xp
    Test and training error
    50 xp
    Visualizing the tradeoff
    100 xp
    Wrapping up
    50 xp
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

collaborators

Collaborator's avatar
Mona Khalil
Collaborator's avatar
Amy Peterson

prerequisites

Hypothesis Testing in PythonSupervised Learning with scikit-learn
Conor Dewey HeadshotConor Dewey

Data Scientist, Squarespace

See More

What do other learners have to say?

    Join over 15 million learners and start Practicing Statistics Interview Questions in Python today!

    Create Your Free Account

    GoogleLinkedInFacebook

    or

    By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.