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Data Science: Productivity Tools

Provided by Harvard University (HarvardX)
Introductory
1–2 hours
per week, for 8 weeks
Free

$49 USD for graded exams and assignments, plus a certificate

Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.

Before you start

  • No prerequisites.

Choose your pace

Self-Paced courses contain assignments without due dates. You can progress at your own speed.

Steady Learners
80% complete in less than 11 weeks
Accelerated Learners
51% complete in less than 3 weeks
Course opens: Jul 16, 2019
Course ends: Jan 3, 2020

What you will 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

Overview

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.

Finally, you will learn to write reports in R markdown which permits you to incorporate text and code into a document. We'll put it all together using the powerful integrated desktop environment RStudio.

Meet your instructors

Rafael Irizarry
Professor of Biostatistics
Harvard University

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View Courses
This course is part of:

Earn a Professional Certificate in 2-4 months if courses are taken one at a time.

View the program
  1. 8–16 hours of effort

    Build a foundation in R and learn how to wrangle, analyze, and visualize data.

  2. 8–16 hours of effort

    Learn basic data visualization principles and how to apply them using ggplot2.

  3. 8–16 hours of effort

    Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.

  4. 8–16 hours of effort

    Learn inference and modeling, two of the most widely used statistical tools in data analysis.

  5. Data Science: Productivity Tools
  6. 8–16 hours of effort

    Learn to process and convert raw data into formats needed for analysis.

  7. 8–16 hours of effort

    Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.

  8. 16–32 hours of effort

    Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

  9. 30–40 hours of effort

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

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