SQL Basics using Microsoft SQL Server 2008/2012/2016

What Will You Learn?

• Course Outline: SQL for Business Analysts
• Module 1: Introduction to SQL
•  Database Types: Overview of relational vs. non-relational databases
•  SQL Query Structure: Understanding SELECT statements, clauses (WHERE, ORDER BY, etc.), and
• syntax conventions
• Module 2: Working with Joins
•  Combining Data from Multiple Tables: Importance of joins in data extraction
•  EntityRelationship Diagrams (ERD): Understanding primary and foreign keys
•  Types of Joins:
• i. INNER JOIN
• ii. OUTER JOIN (LEFT, RIGHT, FULL)
• iii. CROSS JOIN
•  Practical Examples: Real world scenarios for using joins in data analysis
• Module 3: Aggregation Functions
•  Motivation for Aggregating Data: Why aggregation is essential for analysis
•  Common Aggregation Functions: COUNT, SUM, AVG, MIN, MAX
•  Aggregation by Groups: Using GROUP BY to summarize data
•  Distinct Count Aggregation: Counting unique values
•  Conditional Aggregation: Utilizing CASE statements and date functions for complex calculations
• Module 4: Subqueries and Temporary Tables
•  Understanding Subqueries: When and why to use them for data extraction
•  Structure and Format: Writing effective nested queries
•  Common Table Expressions (CTEs): Using WITH statements for temporary data manipulation
• Module 5: Data Cleaning and Handling Missing Values
•  Extracting Substrings: Techniques for parsing text data
•  Concatenation of Multiple Columns: Merging data for comprehensive analysis
•  Handling Null Values: Strategies for managing and replacing nulls
• Module 6: Window Functions
•  Core Functions: Using window functions for advanced data analysis
•  Ranking Functions: ROW_NUMBER, RANK, DENSE_RANK for data organization
•  Basic Advanced Window Functions: Focus on cumulative totals and moving averages for trend
• analysis
• Module 7: Data Profiling
•  Understanding Data Quality: Techniques for profiling datasets to assess quality before analysis
•  Identifying and Handling Anomalies: Strategies for recognizing and addressing data issues
• Module 8: Data Visualization Integration
•  Connecting SQL with Visualization Tools: Overview of how to use SQL outputs in tools like
• Power BI or Tableau
• Module 9: Best Practices in SQL
•  Writing Efficient Queries: Guidelines for performance and readability
•  Commenting and Documentation: Importance of documenting queries for future reference
• Module 10: Common SQL Errors and Troubleshooting
•  Debugging Techniques: How to identify and fix common issues in SQL queries
• Module 11: Hands-On Projects and Case Studies
•  Final Project: A final project combining all topics, where participants analyze a dataset and
• present findings