This course is for R users who want to get up to speed with Python!

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

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 read, explore, and manipulate spatial data then use your skills to create informative maps using R.

This course covers in detail the tools available in R for parallel computing.

In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.

Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.

Manipulate, visualize, and perform statistical tests on HR data.

Learn how to effectively and efficiently join datasets in tabular format using the Python Pandas library.

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

Analyze text data in R using the tidy framework.

Practice your Shiny skills while building some fun Shiny apps for real-life scenarios!

Learn how to analyze huge datasets using Apache Spark and R using the sparklyr package.

Learn a variety of feature engineering techniques to develop meaningful features that will uncover useful insights about your machine learning models.

Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.