1. Introduction
● Overview of the training
● Objectives and outcomes
● Prerequisites
2. SQL for Data Extraction
● Basics of SQL
- Introduction to SQL
- SQL syntax and structure
● Data Retrieval
- SELECT statements
- Filtering data with WHERE clause
- Sorting data with ORDER BY
● Advanced Data Retrieval
- JOIN operations (INNER, LEFT, RIGHT, FULL)
- Subqueries and nested queries
- Aggregation functions (SUM, AVG, COUNT, etc.)
- Grouping data with GROUP BY and HAVING
● Working with Databases
- Connecting to databases
- Importing and exporting data
3. Python for Data Exploration and Analysis
● Introduction to Python
- Python basics (variables, data types, control structures)
- Installing and setting up Python environment
● Data Handling with Pandas
- Introduction to Pandas
- DataFrames and Series
- Importing data (CSV, Excel, SQL databases)
- Data cleaning and preprocessing
● Data Exploration
- Descriptive statistics
- Data visualization with Matplotlib and Seaborn
- Handling missing data
- Data transformation and feature engineering
● Advanced Data Analysis
- Time series analysis
- Grouping and aggregating data
- Merging and joining datasets
● Regression and Forecasting for Sales
- Introduction to regression analysis
- Linear regression
- Multiple regression
- Forecasting techniques
- Sales data forecasting
4. Integrating SQL and Python
● Connecting Python to SQL Databases
- Using libraries like SQLAlchemy and Pandas
- Executing SQL queries from Python
● Data Pipeline Creation
- Automating data extraction and loading
- Scheduling and managing data workflows
5. Case Studies and Hands-On Projects
● Real-World Scenarios
- End-to-end data analysis projects
- Combining SQL and Python for comprehensive analysis
● Group Activities
- Collaborative projects
- Peer reviews and presentations
6. Conclusion
● Recap of key concepts
● Q&A session
● Additional resources and next steps