5 Top HarvardX Data Science Courses

The HarvardX Data Science professional certificate program from Harvard University 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. During the program, 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

The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. HarvardX's Data Science certificate 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 this HarvardX data science course, you'll dive into R, the most popular programming language for data scientists.

You'll cover functions and data types, then tackle how to operate on vectors and when to use advanced functions like sorting. You'll also learn how to apply general programming features like "if-else," and "for loop" commands, and how to wrangle, analyze and visualize data.

• Basic R syntax
• Foundational R programming concepts such as data types, vectors arithmetic, and indexing
• How to perform operations in R including sorting, data wrangling using dplyr, and making plots

Probability theory is the mathematical foundation of statistical inference, which is crucial when analyzing data affected that's by chance. It's an essential skill for data scientists to have. During this free course, you'll learn:

In this free course, you'll learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.

You will learn about training data, and how to use a set of data to discover potentially predictive relationships.

You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.

Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others. In this free HarvardX data science course, you'll learn:

• Understand a series of concepts, thought patterns, analysis paradigms, and computational and statistical tools, that together support data science and reproducible research.
• Fundamentals of reproducible science using case studies that illustrate various practices
• Key elements for ensuring data provenance and reproducible experimental design
• Statistical methods for reproducible data analysis
• Computational tools for reproducible data analysis and version control (Git/GitHub, Emacs/RStudio/Spyder), reproducible data (Data repositories/Dataverse) and reproducible dynamic report generation (Rmarkdown/R Notebook/Jupyter/Pandoc), and workflows.
• How to develop new methods and tools for reproducible research and reporting
• How to write your own reproducible paper.