edX: HarvardX Data Science: Productivity Tools Review

HarvardX Data Science: Productivity Tools is the 5th course in a 9-part data science professional certificate track offered by Harvard on the edX course platform. The course focuses on helping you install and learn the basics of a few common software tools in the R Data Science ecosystem, including R Studio, Unix/Linux shells, Git, GitHub and R Markdown. Although it is the 5th course in the specialization and the course page recommends taking the other courses in the series first, there is no particular reason you couldn't take this course out of order, as it does not include programming assignments. Instead, grading is based on periodic comprehension questions which allow 2 attempts apiece.


Productivity Tools is divided up into 5 main topic sections: Installing Software, Unix, Reproducible Reports, Git and GitHub and Advanced Unix. Each section contains several subtopics with a handful of lecture videos, readings and supporting materials. The lecture quality and clarity are great and the course does a fine job introducing tools, but it doesn't get much beyond the most basic functionality of each tool it covers. Since there are no programming assignments, you won't get much hands on practice with the tools presented even if you follow along with everything shown in lecture. Simply installing the various tools the course covers and getting them working on your computer will probably be the most time consuming aspect of this course. The purpose of the course seems to be to make you aware of the various tools that are available, largely eschewing details to cover a broader range of topics within the confines of a 4-week course that requires minimal time commitment.


HarvardX Data Science: Productivity Tools is a fine guide for installing and exposing yourself to common data science tools, but you won't learn much beyond the absolute basics. You'll finish the course with a general sense of what the tools are and how they are used, but you probably won't feel confident using them without significant outside research and practice, as each main topic after the introduction and installation sections could easily have its own dedicated course. You will, however, be well-positioned to take a more comprehensive course covering a specific tool of interest or to start getting practice on your own.


I give HarvardX Data Science: Productivity Tools 3.25 out of 5 stars: Good.