Data science courses

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

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

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

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

Learn how to explore, visualize, and extract insights from data.

Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

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

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

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

Elevate your data storytelling skills and discover how to tell great stories that drive change with your audience.

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

Enhance your reports with Power BI's Exploratory Data Analysis (EDA). Learn what EDA is for Power BI and how it can help you extract insights from your data.

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

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

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

Master sampling to get more accurate statistics with less data.

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

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

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

Learn survey design using common design structures followed by visualizing and analyzing survey results.

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

Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!

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

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.

Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.

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

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

Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.

Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.

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

Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

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

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

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

In this course you'll learn techniques for performing statistical inference on numerical data.

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

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

Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.

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

GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.

Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.

Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.

In this course you'll learn how to perform inference using linear models.

Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.

Learn to work with time-to-event data. The event may be death or finding a job after unemployment. Learn to estimate, visualize, and interpret survival models!

Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.

Explore Linear Regression in a tidy framework.

Learn about experimental design, and how to explore your data to ask and answer meaningful questions.

Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.

Use survival analysis to work with time-to-event data and predict survival time.

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

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

Learn to solve increasingly complex problems using simulations to generate and analyze data.

Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.

Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.

Learn A/B testing: including hypothesis testing, experimental design, and confounding variables.

Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.

Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.

Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.

Learn to analyze, plot, and model multivariate data.

Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.

Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.

Learn to analyze and model customer choice data in R.

In this course, you'll learn how to implement more advanced Bayesian models using RJAGS.

Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network

Learn to build simple models of market response to increase the effectiveness of your marketing plans.

Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.

In this course, you'll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.

Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.

Learn strategies for answering probability questions in R by solving a variety of probability puzzles.