Stage 1Complete first
Prerequisite: Data Analysis
- Python + Pandas
- SQL
- Statistics basics
- Data visualization
Data Science
Build models that predict, classify, and discover patterns in data.
Advanced9 to 14 monthsanalyze
Your Progress0%
Overview
Data scientists build machine learning models that predict outcomes, classify data, and find hidden patterns. The field sits at the intersection of statistics, programming, and domain expertise.
Who is this for?
- ✓ Students starting from zero who want structure
- ✓ Self-taught learners who need a clear path
- ✓ Career switchers ready to commit consistently
What can you build?
- Predictive models
- Recommendation systems
- Image classifiers
- NLP pipelines
- Fraud detection systems
Jobs you can get
Data ScientistML EngineerAI ResearcherNLP EngineerComputer Vision Engineer
Salary range: $70,000 to $160,000/year
Roadmap
Follow each stage in order. Mark stages complete as you finish them.
- Stage 23 to 4 weeks
Statistics & Math
- Probability distributions
- Linear algebra basics
- Calculus concepts (gradients)
- Hypothesis testing
- Stage 36 to 8 weeks
Machine Learning
- Supervised learning: regression, classification
- Unsupervised: clustering
- Model evaluation metrics
- Feature engineering
- Scikit-learn
- Stage 44 to 5 weeks
Deep Learning Basics
- Neural networks fundamentals
- TensorFlow or PyTorch
- CNNs for image tasks
- Intro to NLP
- Transfer learning
- Stage 52 to 3 weeks
MLOps & Deployment
- Saving and loading models
- FastAPI ML endpoints
- Model monitoring
- Experiment tracking (MLflow)
Resources
fast.ai
FREEPractical deep learning from the top down. One of the best AI courses in existence. Free.
www.fast.ai
Andrew Ng ML Course
PAIDThe course that introduced millions to machine learning. Audit free on Coursera.
www.coursera.org/specializations/machine-learning-introduction
Kaggle Learn ML
FREEFree ML courses: intro to ML, intermediate ML, feature engineering, deep learning.
www.kaggle.com/learn
Introduction to Statistical Learning
FREEEssential ML statistics textbook. Free PDF. Read alongside coding practice.
www.statlearning.com
ML Full Course
FREEMachine learning course covering core algorithms with Python.
www.youtube.com/watch?v=i_LwzRVP7bg
PyTorch Official Tutorials
FREEOfficial PyTorch tutorials, the industry-standard deep learning framework.
pytorch.org/tutorials
StatQuest with Josh Starmer
FREEStatistics and ML concepts explained visually and clearly. Essential channel.
www.youtube.com/@statquest
Projects
beginner
#01
Titanic Survival Predictor
Classic Kaggle competition. Build a classifier with feature engineering.
intermediate
#01
House Price Prediction
Regression model predicting property prices with feature analysis and cross-validation.
advanced
#01
Image Classifier
CNN classifier for 10-class image recognition using PyTorch.
#02
Deployed ML API
Train a model, save it, and serve predictions via FastAPI endpoint.
AI Guide
What AI helps with
- ✓ Explaining ML algorithms in plain terms
- ✓ Debugging model training code
- ✓ Suggesting feature engineering ideas
- ✓ Explaining statistical concepts
What AI cannot replace
- ✗ Replacing mathematical understanding, you must grasp why models work
- ✗ Validating results without your own analysis
Recommended Tools
ChatGPT Code Interpreter
Upload datasets and run ML experiments interactively
Claude
Explaining concepts, debugging training pipelines
Sample Prompts
Explain gradient descent as if I understand basic algebra but not calculus. Include why it works and what can go wrong with learning rate.My Random Forest has 97% training accuracy but only 61% test accuracy. Explain what is happening and give me 5 concrete steps to fix it.Career Path
Data ScientistML EngineerAI ResearcherNLP EngineerComputer Vision Engineer
$70,000 to $160,000/year
Kaggle competitions are your portfolio. Even finishing in the top 40% shows employers you can apply ML to real problems. Publish your notebooks.