What the Open Source Data Science Masters 6-Month Curriculum Covers

Updated

The Open Source Data Science Masters site documents a roughly six-month, intensive self-study path through open-source data science materials, and its final project — a dynamic D3.js website built by two learners, Fras and Sabine, and posted on November 23, 2014 — is designed to answer three questions: what it is like to study data science online intensely for six months, how a skill set changes and grows over that time, and which topics the curriculum covered and how those topics connect to one another. So the site is less a fixed syllabus than a reflective map of one self-directed curriculum and its connections.

What the site actually presents

Based on the site's own description, the core content is:

  • A six-month timeline of intensive online study, framed as a personal experiment rather than an accredited program.
  • A topic map showing which data science subjects the curriculum covered and how they link together.
  • A skill-growth narrative describing how the learners' abilities changed across the six months.
  • A final project — a dynamic website built with the JavaScript library D3.js — used as the vehicle for explaining all of the above.

The site does not publish a fixed week-by-week syllabus in the material available, so treat the topic list as something you reconstruct from the project's topic map rather than a downloadable schedule.

The topics a curriculum like this typically spans

Because the site describes coverage of "multiple data science topics" and their connections without listing them individually, the specific sequence isn't documented here. A six-month open-source data science path of this kind generally moves through:

Phase Typical focus What it builds
Early Programming fundamentals, statistics Ability to manipulate data and reason about it
Middle Data wrangling, visualization, machine learning basics Turning raw data into findings
Late A capstone project, communication of results Applying and explaining the full workflow

Use this as orientation only — the site's own value is in showing how its chosen topics interlock, which you can read off the D3.js project.

How to use the site

  1. Read the final project first. It is the site's summary artifact and the fastest way to see the topic map and the skill-growth story.
  2. Extract the topic list from that map and compare it against your current skills to find gaps.
  3. Treat the six-month framing as a pace reference, not a guarantee — the site describes one pair's intensive schedule, and your timeline will depend on your starting point and hours per week.
  4. Check the comments on the final project post, where readers discuss the approach.

Common sticking points

  • No fixed syllabus. You won't find a downloadable week-by-week plan, so expect to assemble your own sequence from the topics shown.
  • Dated resources. The project is from 2014; open-source data science tooling has moved on, so verify that any library or course it points to is still maintained.
  • Pace assumptions. "Six months" reflects an intensive commitment. If you can only study part-time, scale the timeline rather than the topic list.

If your goal is to see how a self-built open-source data science curriculum fits together and how skills compound over roughly half a year, this site's project is a useful worked example. If you need a structured, credentialed program with a published syllabus, it is not that — look for a course with an explicit schedule instead.