📌 Introduction
Data Analysis is essential for transforming raw data into actionable insights. This course roadmap teaches you how to collect, clean, analyze, and visualize data using Python libraries (Pandas, NumPy, Matplotlib, and Seaborn) and SQL databases. By mastering these skills, learners can effectively handle real-world datasets, build informative dashboards, and generate actionable reports for business intelligence and informed decision-making.
📘 Detailed Course Outline
Module 1. Introduction to Data Analysis
Data Analysis workflow: Collection → Cleaning → Analysis → Visualization → Reporting
Tools overview: Python, SQL, Jupyter Notebook, VS Code, Excel
Module 2. Python Basics for Data Analysis
Module 3. Working with SQL for Data Analysis
Connecting Python with SQL using sqlite3, SQLAlchemy, or PyODBC
Module 4. Python Libraries for Data Analysis
Module 5. Data Cleaning & Preprocessing
Module 6. Exploratory Data Analysis (EDA)
Module 7. Data Visualization
Module 8: Advanced SQL for Data Analysis
Module 9. Combining Python & SQL
Module 10. Advanced Data Analysis & Reporting
Module 11. Advanced Data Analysis & Reporting
📘 Master Data Analysis: Complete Python & SQL Course Outline
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