## What you'll learn

- Fundamental R programming skills
- Statistical concepts such as probability, inference, and modeling and how to apply them in practice
- Gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr
- Become familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio
- Implement machine learning algorithms
- In-depth knowledge of fundamental data science concepts through motivating real-world case studies

## About this series

The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. The HarvardX Data Science program prepares you with the necessary knowledge base and useful skills to tackle real-world data analysis challenges. The program covers concepts such as probability, inference, regression, and machine learning and helps you develop an essential skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with Unix/Linux, version control with git and GitHub, and reproducible document preparation with RStudio.

In each course, we use motivating case studies, ask specific questions, and learn by answering these through data analysis. Case studies include: Trends in World Health and Economics, US Crime Rates, The Financial Crisis of 2007-2008, Election Forecasting, Building a Baseball Team (inspired by Moneyball), and Movie Recommendation Systems.

Throughout the program, we will be using the R software environment. You will learn R, statistical concepts, and data analysis techniques simultaneously. We believe that you can better retain R knowledge when you learn how to solve a specific problem.

## This series includes

- OnlineBuild a foundation in R and learn how to wrangle, analyze, and visualize data.Free
^{*}8 weeks longClosed - OnlineLearn basic data visualization principles and how to apply them using ggplot2.Free
^{*}8 weeks longClosed - OnlineLearn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.Free
^{*}8 weeks longClosed - OnlineLearn inference and modeling: two of the most widely used statistical tools in data analysis.Free
^{*}8 weeks longClosed - OnlineKeep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.Free
^{*}8 weeks longClosed - OnlineLearn to process and convert raw data into formats needed for analysis.Free
^{*}8 weeks longClosed - OnlineLearn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.Free
^{*}8 weeks longClosed - OnlineBuild a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.Free
^{*}8 weeks longClosed - OnlineShow what you’ve learned from the Professional Certificate Program in Data Science.Free
^{*}2 weeks longClosed