Data science courses

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

Learn the fundamentals of statistics, including measures of center and spread, probability distributions, and hypothesis testing with no coding involved!

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.

Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.

Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.

Learn to perform linear and logistic regression with multiple explanatory variables.

Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.

Master sampling to get more accurate statistics with less data.

In this four-hour course, you’ll learn the basics of analyzing time series data in Python.

Build the foundation you need to think statistically and to speak the language of your data.

Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.

Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.

Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.

In this course, you'll learn about the concepts of random variables, distributions, and conditioning.

Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.

In this course you'll learn about basic experimental design, a crucial part of any data analysis.

Learn the core techniques necessary to extract meaningful insights from time series data.

The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.

In this course you will learn to fit hierarchical models with random effects.

Learn the practical uses of A/B testing in Python to run and analyze experiments. Master p-values, sanity checks, and analysis to guide business decisions.

This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.

In this course you'll learn how to leverage statistical techniques for working with categorical data.

Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.

Learn to design and run your own Monte Carlo simulations using Python!

Learn to perform linear and logistic regression with multiple explanatory variables.

Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.