Data Scientist Roadmap
The skill roadmap for a data scientist role — covering Python, SQL, statistics, machine learning, and the communication skills that make data work valuable.
Phase 1: Phase 1 — Python & SQL
Python Fundamentals
Variables, functions, loops, data structures, and file I/O.
SQL
SELECT, WHERE, JOIN, GROUP BY, subqueries, and window functions.
Git Basics
Version control for code and notebooks.
Phase 2: Phase 2 — Data Analysis
NumPy
Numerical computing, array operations, and mathematical functions.
pandas
Load, clean, transform, and analyze structured data with DataFrames.
Data Visualization
Communicate insights with matplotlib, seaborn, and plotly.
Statistics
Descriptive statistics, probability distributions, hypothesis testing, and p-values.
Phase 3: Phase 3 — Machine Learning
Scikit-learn
Classification, regression, clustering, and model evaluation with sklearn.
Feature Engineering
Transform raw data into informative features that improve model performance.
Model Evaluation
Cross-validation, confusion matrices, ROC curves, and avoiding data leakage.
Deep Learning
Neural networks, backpropagation, and building models with TensorFlow or PyTorch.
Phase 4: Phase 4 — Professional Data Science
MLOps Basics
Deploy models to production, monitor drift, and version datasets.
Communication
Write reports, build dashboards, and present findings to non-technical stakeholders.
Portfolio Projects
Build 2–3 end-to-end data science projects with real datasets.