Data Science Roadmap — Step-by-step Learning Path
Data analysis, statistics, visualization and data-driven modeling
The Data Science roadmap
- Python Programming Fundamentals — beginner
- Python Syntax & Data Types — beginner
- Control Flow & Functions — beginner
- Data Structures (Lists, Dicts, Sets, Tuples) — beginner
- Object-Oriented Python — beginner
- File I/O & Working with Modules — beginner
- Virtual Environments & pip — beginner
- Statistics & Probability for Data Science — beginner
- Descriptive Statistics — beginner
- Probability Fundamentals — beginner
- Probability Distributions — beginner
- Hypothesis Testing & p-values — beginner
- Confidence Intervals — beginner
- Correlation vs Causation — beginner
- Bayesian Thinking Basics — beginner
- SQL & Databases — beginner
- SQL Fundamentals (SELECT, WHERE, ORDER BY) — beginner
- Joins & Set Operations — beginner
- Aggregations & GROUP BY — beginner
- Window Functions — intermediate
- Subqueries & CTEs — intermediate
- Database Design Basics — beginner
- NoSQL Databases Overview — intermediate
- Query Performance for Analysts — intermediate
- Python for Data Analysis — beginner
- NumPy Fundamentals — beginner
- Pandas for Data Wrangling — beginner
- Merging, Joining & Reshaping Data — beginner
- Working with Dates & Times — beginner
- Working with Text Data in Pandas — beginner
- Exploratory Data Analysis & Data Cleaning — intermediate
- The EDA Process — intermediate
- Handling Missing Data — intermediate
- Duplicate & Inconsistent Data — intermediate
- Outlier Detection — intermediate
- Data Profiling & Quality Assessment — intermediate
- Univariate & Bivariate Analysis — intermediate
- Data Visualization & Storytelling — intermediate
- Principles of Visual Design — intermediate
- Choosing the Right Chart Type — intermediate
- Data Visualization with Matplotlib & Seaborn — intermediate
- Interactive Dashboards with Tableau/Power BI — intermediate
- Storytelling with Data — advanced
- Avoiding Misleading Visualizations — advanced
- Business Acumen & Metrics — intermediate
- Translating Business Questions into Data Questions — advanced
- Defining and Choosing the Right Metrics (KPIs) — advanced
- Cohort Analysis — advanced
- Funnel Analysis — advanced
- Understanding Unit Economics — advanced
- Experimentation & A/B Testing — advanced
- Designing a Valid Experiment — advanced
- Sample Size & Statistical Power — advanced
- Running & Analyzing an A/B Test — advanced
- Common A/B Testing Pitfalls — advanced
- Multi-Armed Bandits Overview — advanced
- Causal Inference Basics — advanced
- Introduction to Machine Learning for Data Scientists — advanced
- When to Use Machine Learning vs Simple Analysis — advanced
- Regression Basics for Data Scientists — advanced
- Classification Basics for Data Scientists — advanced
- Clustering for Segmentation — advanced
- Model Evaluation Basics — advanced
- Feature Engineering & Data Preparation for Modeling — advanced
- Feature Engineering Fundamentals — advanced
- Encoding & Scaling for Models — advanced
- Handling Imbalanced Data — advanced
- Time-Based Feature Engineering — advanced
- Basic Text Feature Engineering — advanced
- Big Data & Data Engineering Basics — advanced
- Big Data Concepts & the Three Vs — advanced
- Introduction to Apache Spark — advanced
- Distributed Data Processing Basics — advanced
- ETL vs ELT Pipeline Concepts — advanced
- Data Warehouses (Snowflake/BigQuery) Overview — advanced
- Workflow Orchestration Overview (Airflow) — advanced
- Data Storytelling & Reporting — advanced
- Building Effective Dashboards — advanced
- Writing Data-Driven Reports — advanced
- Presenting Findings to Non-Technical Stakeholders — advanced
- Data Ethics & Privacy Considerations — advanced
- Version Control & Collaboration for Data Science — intermediate
- Git for Data Scientists — intermediate
- Reproducible Notebooks — intermediate
- Code Review for Analysis Code — intermediate
- From Notebook to Production Considerations — advanced
- Cloud & Tools for Data Science — intermediate
- Cloud Platforms for Data Science (AWS/GCP/Azure) — intermediate
- Jupyter, Colab & Notebook Environments — intermediate
- Data Science Project Structure — intermediate
- Environment & Dependency Management — intermediate
- Case Studies & Capstone — advanced
- Case Study: Customer Segmentation Analysis — advanced
- Case Study: A/B Test Analysis for a Product Launch — advanced
- Case Study: Building an Executive Dashboard — advanced
- Case Study: Cohort & Retention Analysis — advanced
- Capstone: End-to-End Data Science Project — advanced