Data Scientist Roadmap 2026

Stage 1: Statistics and SQL (4-5 weeks)

1. Descriptive and inferential statistics, hypothesis testing
2. SQL -- joins, aggregations, window functions
3. Example: pull and analyze a dataset entirely through SQL before touching Python

Stage 2: Python for Data Science (3-4 weeks)

1. pandas, NumPy
2. Data cleaning and exploratory data analysis (EDA)
3. matplotlib/seaborn for visualization
4. Example: take a genuinely messy real-world dataset (missing values, inconsistent formatting) and clean it fully

Stage 3: Machine Learning Fundamentals (5-6 weeks)

1. Supervised learning -- regression and classification algorithms
2. Model evaluation and cross-validation
3. Feature engineering
4. Example: build a model predicting a real, business-relevant outcome (e.g. customer churn) on public data

Stage 4: Experimentation (2-3 weeks)

1. A/B testing design and analysis
2. Causal reasoning -- correlation vs. causation
3. Example: design and simulate an A/B test, including calculating required sample size

Stage 5: Deep Learning Basics (3-4 weeks, optional but increasingly common)

1. Neural network fundamentals
2. PyTorch or TensorFlow basics
3. When deep learning is (and isn't) the right tool vs. simpler models
4. Example: compare a simple model against a neural network on the same problem, and reason honestly about which is actually better for the use case

Stage 6: End-to-End Project and Communication (3-4 weeks)

1. A full project from raw data to a model to a clearly communicated business recommendation
2. Practice explaining technical findings to a non-technical audience -- this is a genuinely underrated skill gap
3. Deploy the model behind a simple API or dashboard so it's usable, not just a notebook

Frequently Asked Questions