Data Science Foundations
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10 lessons3 levels1 capstone project
Build the end-to-end data-science workflow: problem framing, data exploration, cleaning, visualization, baseline modeling, validation, communication and reproducible notebooks before specializing in individual model families.
BASIC
- Practical Deep Learning Lesson 1 — fast.ai (30 min selected)
Top-down path from data to a working model quickly. - Introduction to Random Forests — fast.ai (30 min selected)
Strong classical-ML grounding using real tabular data and practical model interpretation. - Micrograd / Neural Networks from Scratch — Andrej Karpathy (35 min selected)
Builds neural-network intuition from first principles and code.
INTERMEDIATE
- ROC and AUC, Clearly Explained — StatQuest
A deeper treatment of the same skill; use it to move from concept to working patterns. - Cross Validation — StatQuest
A deeper treatment of the same skill; use it to move from concept to working patterns. - Machine Learning Full Course for Beginners
Apply Data Science Foundations through the adjacent capability: Machine Learning.
ADVANCED
- Step by Step — Machine Learning Project Workflow
Extend Data Science Foundations into senior/production practice through Machine Learning. - Machine Learning with Python and Scikit-Learn — freeCodeCamp
Extend Data Science Foundations into senior/production practice through Machine Learning. - Intermediate Course + Project — SQL for Data Analytics
Extend Data Science Foundations into senior/production practice through SQL & Analytics.