Live Online MS SQL Programming Course for Data Science

MS SQL for Data Science – Live Course Module

Total Duration: 5 Weeks (30 Hours)

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Week 1: Introduction & Basics (6 Hours)

1. Introduction to Databases & MS SQL (1 Hr)

   • What is a Database?

   • Role of SQL in Data Science

   • Installing MS SQL Server & SSMS

2. SQL Basics (2 Hrs)

   • Database, Tables, Schema

   • Data Types, Primary Key, Foreign Key

   • Creating & Dropping Tables

3. Basic Queries (3 Hrs)

   • SELECT, WHERE, ORDER BY, DISTINCT

   • Filtering Data (>, <, BETWEEN, LIKE, IN)

   • LIMIT / TOP in SQL

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Week 2: Intermediate SQL for Data Science (6 Hours)

4. Functions & Expressions (2 Hrs)

   • String Functions (LEN, UPPER, LOWER, CONCAT)

   • Numeric Functions (ROUND, CEIL, FLOOR)

   • Date Functions (GETDATE, DATEADD, DATEDIFF)

5. Sorting, Grouping & Aggregations (2 Hrs)

   • GROUP BY, HAVING

   • COUNT, SUM, AVG, MIN, MAX

   • Practical Aggregation Use Cases in Data Science

6. Joins & Relationships (2 Hrs)

   • INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN

   • Combining Multiple Tables

   • Practical Dataset Examples

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Week 3: Advanced SQL Concepts (6 Hours)

7. Subqueries & Nested Queries (2 Hrs)

   • Scalar, Correlated Subqueries

   • Use Cases in Analytics

8. Set Operators (1 Hr)

   • UNION, INTERSECT, EXCEPT

9. Window Functions for Analytics (3 Hrs)

   • ROW_NUMBER, RANK, DENSE_RANK

   • LEAD, LAG

   • Running Totals, Moving Averages

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Week 4: Data Science-Oriented SQL (6 Hours)

10. Data Cleaning with SQL (2 Hrs)

    • Handling NULLs (ISNULL, COALESCE)

    • Removing Duplicates

    • Case Statements

11. Advanced Aggregations (2 Hrs)

    • Pivot Tables in SQL

    • CUBE, ROLLUP

    • Data Summarization for Analytics

12. Performance Tuning & Indexing (2 Hrs)

    • Indexing Basics

    • Query Optimization Tips

    • Execution Plans

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Week 5: Applied Data Science with SQL (6 Hours)

13. Integrating SQL with Data Science Tools (2 Hrs)

    • Connecting MS SQL with Python (pandas, pyodbc, SQLAlchemy)

    • Exporting Data to CSV/Excel

14. Case Studies (2 Hrs)

    • SQL Queries on Real Datasets (Sales, Healthcare, Finance)

    • Data Wrangling for Machine Learning

15. Capstone Project (2 Hrs)

    • Build a Mini Data Science Project Using SQL + Python

    • Example: Customer Segmentation or Sales Forecasting Dataset