A comprehensive curriculum for learning data analysis, designed for non-technical professionals who want to develop data analysis skills.
- Introduction to Data Analysis
- Essential Mathematics for Data Analysis
- Excel for Data Analysis
- Introduction to Python
- Python Libraries for Data Analysis
- SQL for Data Analysis
- Final Project
This 3-month course meets twice weekly (Sunday and Wednesday) for 3-hour sessions.
| Week | Sunday Session | Wednesday Session | ||
|---|---|---|---|---|
| Module & Topics | Content | Module & Topics | Content | |
| 1 | Module 1 | Introduction to Data Analysis (Lessons 1-3) |
Module 1 | Introduction to Data Analysis (Lessons 4-6) |
| 2 | Module 2 | Essential Mathematics (Basic Statistics) |
Module 2 | Essential Mathematics (Probability & Distributions) |
| 3 | Module 2 | Essential Mathematics (Correlation & Regression) |
Module 3 | Excel Basics (Lessons 1-2) |
| 4 | Module 3 | Excel Data Management (Lessons 3-4) |
Module 3 | Excel Analysis Tools (Lessons 5-6) |
| 5 | Module 3 | Excel Advanced Analysis (Lessons 7-9) |
Module 4 | Python Intro (Notebooks, Data Types) |
| 6 | Module 4 | Python Basics (Lists, Conditionals, Loops) |
Module 4 | Python Functions & File Handling |
| 7 | Module 4 | Python Advanced Concepts |
Module 5 | NumPy Fundamentals |
| 8 | Module 5 | Pandas Basics | Module 5 | Pandas Advanced |
| 9 | Module 5 | Data Visualization (Matplotlib) |
Module 5 | Data Visualization (Seaborn, Plotly) |
| 10 | Module 6 | SQL Fundamentals | Module 6 | SQL Joins & Advanced Queries |
| 11 | Module 6 | SQL for Data Analysis | Module 7 | Final Project Planning |
| 12 | Module 7 | Final Project Work | Module 7 | Final Project Presentations |
- Module 1: Introduction to Data Analysis - 6 hours
- Module 2: Essential Mathematics for Data Analysis - 12 hours
- Module 3: Excel for Data Analysis - 12 hours
- Module 4: Introduction to Python - 12 hours
- Module 5: Python Libraries for Data Analysis - 12 hours
- Module 6: SQL for Data Analysis - 12 hours
- Module 7: Final Project - 6 hours
This curriculum is structured as a step-by-step guide through data analysis fundamentals to more advanced topics. Start with Module 1 and progress through each module sequentially for the most effective learning experience.
- No prior programming experience required
- Basic computer skills
- For Python modules: Anaconda or Python installation (instructions in Module 4)
- Module 1: Fundamentals of data analysis, key concepts, and workflow
- Module 2: Statistical foundations and mathematical concepts needed for data analysis
- Module 3: Using Excel for data manipulation, visualization, and analysis
- Module 4: Python programming basics for data analysis
- Module 5: Essential Python libraries including NumPy, Pandas, Matplotlib, and Seaborn
- Module 6: SQL fundamentals for database querying and data extraction
- Module 7: Capstone project applying all learned concepts
This project is licensed under the MIT License - see the LICENSE file for details.
- Contributors and content creators
- Open-source tools and libraries that make data analysis accessible