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

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

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

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

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.

Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.

Specify and fit GARCH models to forecast time-varying volatility and value-at-risk.

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

Show what you've learned from the Professional Certificate Program in Data Science.

Learn inference and modeling, two of the most widely used statistical tools in data analysis.

Build a foundation in R and learn how to wrangle, analyze, and visualize data.