In June 2026 we ran our third Data Science & Machine Learning Bootcamp, and it ended up being the biggest curriculum we'd built for the program, eight core lessons covering the full ML workflow, plus seven additional bonus sessions covering everything from the math underneath machine learning to career advice for people about to go looking for their first data role.
Three cohorts in
By June 2026 we'd already run the September 2025 and February 2026 Data Science & Machine Learning cohorts, plus Python for Everyone in between. That last one mattered more than we expected: participants arriving at this cohort were far more likely to already have working Python fluency, which meant the full month could go toward data science and machine learning itself, instead of also being a crash course in the language underneath it.
The same core seven stages
The underlying philosophy hasn't moved since the first cohort: collect data, preprocess it, split it into train and test sets, choose a model, train it, evaluate it, deploy it. Every cohort we run is a different arrangement of lessons around that same seven-stage backbone.
Eight lessons, not ten
This cohort restructured the core material into eight lessons, opening with something the earlier cohorts didn't have: a standalone lesson on what data science is, as its own discipline, before moving into machine learning specifically.
- Lesson One: Data Science, framing the field before diving into ML
- Lesson Two: Introduction to Machine Learning
- Lesson Three: Data Foundations
- Lesson Four: Data Preprocessing Pipeline
- Lesson Five: Regression, in both theory and a live "in action" walkthrough
- Lesson Six: Classification, split across a theory session and two hands-on practice sessions
- Lesson Seven: Clustering, again paired theory with practice
- Lesson Eight: Deployment
Seven bonus sessions
This was also the first cohort to go beyond the core workflow entirely. After two prior runs, a consistent set of questions kept coming up that the core lessons were never designed to answer, how does this connect to deep learning, what's going on with generative AI, and what does a career in this field even look like. We built seven bonus sessions specifically to answer them:
- Math for Data Science & Machine Learning
- Introduction to Deep Learning
- AI & Generative AI
- AI Ethics & Responsible AI
- Career Path in Machine Learning
- Building a Strong Portfolio
- Final Instructor Advice
The final project
The bootcamp still closes the way every cohort has: a deployment lesson, followed by a final project that asks each participant to run the full workflow themselves, from a fresh dataset to a deployed result, without a lesson video to follow along with.
Open and free
As with every cohort in this series, the bootcamp was fully sponsored by Dugsiiye, keeping every seat free. The full curriculum (core lessons, bonus sessions, assignments, and the final project) is public in the Jun-ds-ml-bootcamp-2026 repository on GitHub under a Creative Commons BY-NC-SA 4.0 license.
What comes next
Three Data Science & Machine Learning cohorts and a Python bootcamp in, one gap was still showing up in almost every project submission: participants who could build a model but weren't confident collaborating on code the way a real engineering team does. That gap became our next bootcamp, built entirely around Git and GitHub.
Writing about Somali language technology, open data, and AI from the lab in Mogadishu.