This is a story of my 5 month DS learning journey as a PM.
This isn’t a bootcamp success story.
It’s not about becoming a data scientist in 90 days.
It’s for people who want a real, unvarnished view of what it actually takes to break into ML / DS without a CS degree.
I’m a product manager with 9 years in tech, formal training in science and systems design, and I’ve been self-training in machine learning for 5 months while working full-time. This video breaks down what the journey really looks like - the mindset shift, the technical gaps, and who I think can realistically make the jump.
If you’ve been toying with the idea of pivoting into DS or ML, this might help you sanity-check your odds.
⏱️ Chapters:
00:00 – Intro: No Bootcamp Fantasy
01:04 – Why I’m Doing This
04:28 – Where I’m Learning
05:36 – How It’s Going
08:23 – Who Can Actually Make the Switch
09:57 – Mental Blocks & Math Myths
13:03 – The Reality Check
14:48 – Do Degrees Matter?
15:11 – Who Has a Realistic Shot
16:01 - Who is Unlikely to Make It
16:57 – Final Thoughts
⚠️ Disclaimer: Data Science ≠ One Role
Data Science is a broad field with many specialized domains (e.g. computer vision, Natural Language Processing (NLP), Time Series Forecasting, Recommender Systems, and more.
The program I’m currently following is focused on the core foundations of applied data science and machine learning. This is not a specialization track in CV, NLP, or MLOps - it’s a generalist path aimed at preparing for junior ML developer or full-stack data science roles.
✅ Who This Is For:
PMs, tech leads, and analysts in tech who want to level up with ML fluency
Engineers/scientists from adjacent fields considering a pivot
People with formal education in math, life sciences, or engineering
Anyone who values truth over tactics when it comes to breaking into DS
🎓 Tools mentioned (not sponsored):
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#datascience #machinelearning #productmanagement #ml #ai #techlayoffs