Data Science with R
June 09, 2019
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.
Job Outlook
- R is listed as a required skill in 64% of data science job postings and was Glassdoor’s Best Job in America in 2016 and 2017. (source: Glassdoor)
- Companies are leveraging the power of data analysis to drive innovation. Google data analysts use R to track trends in ad pricing and illuminate patterns in search data. Pfizer created customized packages for R so scientists can manipulate their own data.
- 32% of full-time data scientists started learning machine learning or data science through a MOOC, while 27% were self-taught. (source: Kaggle, 2017)
- Data Scientists are few in number and high in demand. (source: TechRepublic)
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
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