Data Science Courses | Harvard University

Harvard University is offering a bunch of free Data Science courses on edX. These courses are self-paced, which means you can enroll in them anytime and start learning. There are courses covering the fundamentals of data science along with advanced topics like deep learning and artificial intelligence.

The Data Science certificate from Harvard University will help you build your career in this booming field. Here are the details of the courses that Harvard University offers:

1. Data Science: Visualization
   Learn basic data visualisation principles and how to apply them using ggplot2.

What you’ll learn

• Data visualization principles
• How to communicate data-driven findings
• How to use ggplot2 to create custom plots
• The weaknesses of several widely-used plots and why you should avoid them

Duration – 8 weeks long
Time Commitment – 1-2 hours per week
Pace – Self-paced
Course link –Register here

2. Data Science: Productivity Tools
Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.

What you’ll learn

• How to use Unix/Linux to manage your file system
• How to perform version control with git
• How to start a repository on GitHub
• How to leverage the many useful features provided by RStudio

Duration – 8 weeks long
Time Commitment – 1-2 hours per week
Pace – Self-paced
Course link –Register here

3. Data Science: R Basics
Build a foundation in R and learn how to wrangle, analyze, and visualize data.

What you’ll learn

• 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

Duration – 8 weeks long
Time Commitment – 1-2 hours per week
Pace – Self-paced
Course link –Register here

4. Data Science: Wrangling
Learn to process and convert raw data into formats needed for analysis.

What you’ll learn

• Importing data into R from different file formats
• Web scraping
• How to tidy data using the tidyverse to better facilitate analysis
• String processing with regular expressions (regex)
• Wrangling data using dplyr
• How to work with dates and times as file formats, and text mining

Duration – 8 weeks long
Time Commitment – 1-2 hours per week
Pace – Self-paced
Course link –Register here

5. Data Science: Machine Learning
Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

What you’ll learn

• The basics of machine learning
• How to perform cross-validation to avoid overtraining
• Several popular machine learning algorithms
• How to build a recommendation system
• What is regularization and why it is useful

Duration – 8 weeks long
Time Commitment – 2-4 hours per week
Pace – Self-paced
Course link –Register here

6. Data Science: Linear Regression
Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.

What you’ll learn

• How linear regression was originally developed by Galton
• What is confounding and how to detect it
• How to examine the relationships between variables by implementing linear regression in R

Duration – 8 weeks long
Time Commitment – 1-2 hours per week
Pace – Self-paced
Course link –Register here

7. Data Science: Capstone
Show what you’ve learned from the Professional Certificate Program in Data Science.

What you’ll learn
How to apply the knowledge base and skills learned throughout the series to a real-world problem
Independently work on a data analysis project

Duration – 2 weeks long
Time Commitment – 15-20 hours per week
Pace – Self-paced
Course link –Register here