Harvard University offers a wide range of free online courses in data science, catering to learners at various levels of expertise. These courses cover essential topics such as programming, data visualization, probability, statistics, machine learning, and more.
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About Harvard University
Harvard University is a private Ivy League research university in Cambridge, Massachusetts. Founded in 1636 as Harvard College and named for its first benefactor, the Puritan clergyman John Harvard, it is the oldest institution of higher learning in the United States. Its influence, wealth, and rankings have made it one of the most prestigious universities in the world.
Harvard’s founding was authorized by the Massachusetts colonial legislature, “dreading to leave an illiterate ministry to the churches”. Though never formally affiliated with any denomination, in its early years Harvard College primarily trained Congregational clergy. Its curriculum and student body were gradually secularized during the 18th century. By the 19th century, Harvard emerged as the most prominent academic and cultural institution among the Boston elite. Following the American Civil War, under President Charles William Eliot’s long tenure (1869–1909), the college developed multiple affiliated professional schools that transformed the college into a modern research university. In 1900, Harvard co-founded the Association of American Universities
Here are some of the top free online courses on data science from Harvard University:
1. Data Science: Inference and Modeling
This course will demonstrate, using R, how inference and modelling can be used to construct statistical methodologies that make polls a useful tool. The principles required to create estimates and margins of error will be covered, along with how to apply them to make reasonably accurate predictions and evaluate the forecast’s precision.
You will be able to comprehend confidence intervals and p-values, two ideas that are often used in data science, once you have mastered this. After that, you will study Bayesian modelling in order to comprehend assertions regarding the likelihood of a candidate prevailing. At the conclusion of the course, we will finally combine everything to make a reduced version.
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2. Data Science: Productivity Tools
A typical data analysis project may involve several parts, each including several data files and different scripts with code. Keeping all this organized can be challenging.
Part of our Professional Certificate Program in Data Science, this course explains how to use Unix/Linux as a tool for managing files and directories on your computer and how to keep the file system organized. You will be introduced to the version control systems git, a powerful tool for keeping track of changes in your scripts and reports. We also introduce you to GitHub and demonstrate how you can use this service to keep your work in a repository that facilitates collaborations.
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3. Data Science: Machine Learning
Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer-guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.
In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.
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4. Data Science: Probability
In this course, part of our Professional Certificate Program in Data Science, you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007–2008. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability.
We will introduce important concepts such as random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem. These statistical concepts are fundamental to conducting statistical tests on data and understanding whether the data you are analyzing is likely occurring due to an experimental method or to chance.
Course Link Click Here
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5. Data Science: R Basics
The first in our Professional Certificate Program in Data Science, this course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so you’ll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states.
We’ll cover R’s functions and data types, then tackle how to operate on vectors and when to use advanced functions like sorting. You’ll learn how to apply general programming features like “if-else,” and “for loop” commands, and how to wrangle, analyze and visualize data.
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