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Harvard offers an online data science certificate program for under $800, but you can also audit each class for free

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Data scientists are the behind-the-scenes wizards tasked with making meaning out of seemingly incomprehensible piles of ever-growing data. Beyond using applied statistics skills and tools like RStudio and GitHub, data scientists communicate their results to help improve business decisions.

This multi-layered skillset, according to Glassdoor, is worth a US median base salary of $113,309 in 2020. It's also rated the second-best job in the country in terms of job satisfaction.

If you're looking to brush up on specific skills or enter a new field, online courses are one of your most affordable options; Right now, you can enroll in a handful of Harvard University courses for free.

edX, an e-learning non-profit, was founded by Harvard and MIT. It has dozens of free online courses from both Ivy League institutions, as well as many other top schools and companies. Below, you'll find nine Harvard University data science courses that you can take together as part of a cohesive data science certificate program ($792) or individually for free (with one-off paid certificate options available as well).

You can audit courses, or pay for a verified certificate of completion and features like graded homework and projects. There's also financial aid of up to 90% of the course cost available. 

What is the difference between auditing an edX course and paying for a certificate?

The audit option is free and gives you access to review course materials and discussion forums. You can also pick and choose which courses you're most interested in browsing, which can be helpful if you already have some data science experience and only want to brush up on certain skills. Another option is paying between $99-$149 for a one-course certificate instead of committing to an entire program.

However, the free versions will not include graded assignments or unlimited course access. For those, you must enroll in the professional certificate program, which comes with the $792 fee (note: there's financial aid of up to 90% of the course cost available). You can read more answers to edX FAQs here.

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The self-paced data science courses below are taught by Rafael Irizarry, a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and a Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute.

Most courses take an estimated 8 weeks to complete with an hour or two of work per week. At that pace, you can finish the full professional certificate program in 1.5 years for $792.80. Once completed, the certificate may be added to your resume and LinkedIn profile for employers to see.

9 Harvard data science courses you can take online:

Data Science: R Basics

Time: 8 weeks

Cost: Free to audit; $149 for a certificate 

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This is the first course in edX's professional certificate program in data science. Rather than focusing on every R skill, students will build a strong foundation in this course: R's functions and syntax and how to operate on vectors or apply general programming features ("If-else"). Later courses in the professional certificate build upon these skills.



Data Science: Visualization

Time: 8 weeks

Cost: Free to audit; $149 for a certificate

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In the second course of the certification program, students will learn data visualization principles, how to communicate their findings, and how to use ggplot2 to create custom plots as well as find weaknesses in some of the most common plots.



Data Science: Probability

Time: 8 weeks

Cost: Free to audit; $99 for a certificate

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The third course in the certification focuses on probability, motivated by the 2007-2008 financial crisis. In it, students will learn important probability theory concepts (random variables, independence); expected values; standard errors; the Central Limit Theorem, and how to perform a Monte Carlo simulation.



Data Science: Inference and Modeling

Time: 8 weeks

Cost: Free to audit; $99 for a certificate

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The next course focuses on statistical inference and modeling through a case study on election forecasting. Students will learn the concepts needed to define estimates and margins of errors, as well as how to use them to make predictions and provide an estimate of precision. They'll also learn about Bayesian modeling in order to understand statements about the probability of a candidate winning. In the end, these new tools will be used to recreate a simplified election forecast model.



Data Science: Productivity Tools

Time: 8 weeks

Cost: Free to audit; $99 for a certificate

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Students learn how to use Unix/Linux to manage their file system as well as how to use git for version control, start a repository on GitHub, and maximize RStudio features. 



Data Science: Wrangling

Time: 8 weeks

Cost: Free to audit; $99 for a certificate

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This course is all about wrangling and cleaning data — importing data into R; tidying data; string processing; HTML parsing; working with dates and times; and text mining.



Data Science: Linear Regression

Time: 8 weeks

Cost: Free to audit; $99 for a certificate

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This course covers implementing linear regression and adjusting for confounding with R. Students will learn about how linear regression was originally developed, what confounding is and how to spot it, and how to use linear regression in R to examine relationships between variables.



Data Science: Machine Learning

Time: 8 weeks

Cost: Free to audit; $99 for a certificate

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This course will teach students the basics of machine learning by building a movie recommendation system. They'll learn popular ML algorithms; principal component analysis; regularization; training data; how to avoid overtraining; and how to use data to find potentially predictive relationships.



Data Science: Capstone

Time: 2 weeks

Cost: Free to audit; $99 for a certificate

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This Capstone project is also the final course in the Data Science Professional Certificate Program. It combines all of the skills learned in the courses above — data visualization; probability; inference and modeling; data wrangling; data organization; regression, and machine learning — in the service of solving a real-world problem. The finished product will be something you can show to potential employers and educational programs.




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