90-day learning roadmap (build a portfolio & land interviews)
Goal: Build 3–4 high-quality portfolio projects and prepare for interviews.
Days 1–14 — Foundations
- Complete an introductory course (Coursera or DataCamp) covering Python basics, pandas, and data visualization.
- Daily practice: 30–60 minute coding exercises (DataCamp) and a 1-page Jupyter notebook explaining your outputs.
Days 15–45 — Applied projects
- Build two small projects: exploratory data analysis (EDA) on a public dataset; a small supervised ML model (classification/regression) and deployment notebook.
- Publish your notebooks on GitHub and host a short write-up on a personal site or Medium.
Days 46–75 — Specialization & capstone
- Enroll in a Coursera specialization course with a capstone (e.g., Johns Hopkins). Complete graded assignments and submit the capstone as a portfolio project.
- Prepare concise case studies: problem, approach, dataset, code snippets, results and lessons learned.
Days 76–90 — Interview prep & polish
- Practice coding interviews (LeetCode/Interview Query for data roles), review common SQL questions, and prepare 3–5 project talking points for interviews.
- Polish GitHub repo, ensure README and one-click notebook runs (Binder/Colab links).