How the Open Source Data Science Masters Project Shows Skill Growth Over 6 Months

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The Open Source Data Science Masters blog documents a six-month, self-directed data science curriculum, and its clearest evidence of skill growth is the final project: a dynamic website built with the D3.js JavaScript library. According to the site, the project was created "at the end of 6 months studying" and set out to explain what intense online data science study is actually like, how a learner's skill set can change and grow over that period, and which topics the curriculum covered and how they connect. If you are planning a self-taught data science path and want a realistic sense of what six months of focused work can produce, this project is a useful reference point — with the caveat that it reflects one pair of learners' experience, not a guaranteed outcome.

What the final project actually demonstrates

The project is not a certificate or a grade — it is a working artifact. The site describes it as a dynamic website using D3.js, and its stated purpose is explanatory: to communicate the experience of studying data science online, intensely, for six months.

That choice of medium matters. Building an interactive, data-driven web visualization requires combining several distinct skills at once:

  • Programming — writing JavaScript and working with a library (D3.js) rather than only analyzing data in a notebook.
  • Data handling — turning the raw material of a learning journey (topics, time, connections) into structured data a visualization can consume.
  • Visual design and communication — deciding how to represent relationships between curriculum topics so a viewer understands them.
  • Web delivery — producing something that runs as a website, not just a local script.

In other words, the endpoint is a project that sits at the intersection of analysis, code, and communication — which is a reasonable proxy for the kind of integrated skill set data science work tends to demand.

The three questions the project answers

The site frames the final project around three explicit questions, and each maps to a dimension of growth:

Question posed by the project What it reveals about skill growth
What is it like to study data science online, intensely, for 6 months? The experience and intensity of self-directed study
How could your skill set change and grow over this time? The before-and-after trajectory of capabilities
What topics did the curriculum cover, and how are they connected? The structure of the knowledge gained, not just a list

The third question is the most instructive for planning. Asking how topics connect implies the learner moved past treating subjects as isolated modules toward seeing a curriculum as a network — a sign of more integrated understanding.

Why "6 months" is the unit of measurement

The site's own framing — "6 months to becoming a data scientist" — treats the half-year as the planning horizon. The final project then becomes the checkpoint that closes that horizon: it was posted at the end of the six months, and it exists to make the transformation legible to others.

For anyone building a self-study plan, this suggests a practical structure: define a fixed time box, then require a single capstone artifact at the end that forces you to demonstrate the connections between everything you learned. The artifact does double duty — it proves the skills and it communicates them.

How to use this as a reference for your own plan

If you are considering a similar path, treat the project as a template for what to aim at rather than a fixed syllabus:

  • Pick an end artifact that combines skills. A D3.js visualization works because it forces programming, data work, and communication together. Choose something with the same property.
  • Build in a "connections" deliverable. Don't just list topics you studied — map how they relate. That is where the project's value as evidence comes from.
  • Expect the outcome to be personal. The site presents one pair of learners' journey; your topics, pace, and final project will differ.

The site also shows subscription-related signals (subscribe/subscription keywords), so if you want to follow updates or comments, check the site directly for what access involves rather than assuming it is open. The blog's excerpt also notes reader engagement (comments on the final project post), which is a reminder that publishing your capstone publicly can turn it into a conversation and a portfolio piece at the same time.