Database Management Courses

Week 1: Introduction to Databases and SQL (4 Hours)

Session 1 (2 Hours): Fundamentals of Database Systems

Overview of Database Management Systems (DBMS)

Types of Databases: Relational vs. Non-Relational

Basic Database Terminology and Concepts

Session 2 (2 Hours): Introduction to SQL

Understanding SQL and its Role in Databases

Basic SQL Syntax and Commands

Setting Up a SQL Environment (e.g., MySQL, PostgreSQL)

Week 2: SQL for Data Retrieval (6 Hours)

Session 3 (2 Hours): Basic SQL Queries

SELECT Statements, WHERE Clauses

Working with Columns and Rows

Sorting and Filtering Data

Session 4 (2 Hours): Advanced Data Retrieval

JOIN Operations: Combining Data from Multiple Tables

Aggregating Data with GROUP BY and HAVING Clauses

Subqueries and Nested Queries

Session 5 (2 Hours): Practical SQL Exercises

Hands-on SQL Query Exercises

Case Studies on Data Retrieval in Business Scenarios

Week 3: Database Design and Data Manipulation (6 Hours)

Session 6 (2 Hours): Database Design Principles

Understanding Data Modeling and Entity-Relationship Diagrams

Normalization and Database Schemas

Creating Tables and Setting Primary/Foreign Keys

Session 7 (2 Hours): SQL for Data Manipulation and Management

INSERT, UPDATE, and DELETE Operations

Data Integrity and Transactions

Basics of Stored Procedures and Functions

Session 8 (2 Hours): Working with Real-World Data

Importing and Exporting Data

SQL for Business Analytics and Reporting

Integrating SQL Queries with Business Intelligence Tools

Week 4: Advanced Topics and Practical Applications (4 Hours)

Session 9 (2 Hours): Advanced SQL and Performance Optimization

Advanced SQL Features (e.g., Window Functions, Indexing)

Query Optimization Techniques

Understanding SQL Execution Plans

Session 10 (2 Hours): Capstone Project and Course Wrap-Up

Applying SQL Skills to a Real Business Case

Group Project Presentations

Course Summary and Further Learning Resources

This course would ideally mix theoretical lectures with practical exercises and case studies. Each session should include hands-on SQL practice, progressively building in complexity. The final capstone project should involve a comprehensive business-related database task, requiring students to apply all the skills learned throughout the course. This structure ensures that MBA students not only understand the technical aspects of SQL and database management but also how to apply these skills in a business context.

Week 1: Introduction to Power Query and Data Manipulation (4 Hours)

Session 1 (2 Hours): Fundamentals of Power Query

Introduction to Power Query and its Business Applications

Navigating the Power Query Interface

Basic Data Importing and Transformation Techniques

Session 2 (2 Hours): Advanced Data Handling in Power Query

Merging and Appending Queries

Working with Different Data Sources (Web, SQL, etc.)

Advanced Data Transformations and Cleaning Techniques

Week 2: Power Pivot and Data Modeling (6 Hours)

Session 3 (2 Hours): Introduction to Power Pivot

Overview of Power Pivot and its Role in Data Analysis

Creating Data Models in Power Pivot

Understanding Relationships and Data Model Optimization

Session 4 (2 Hours): Advanced Data Modeling in Power Pivot

Working with Large Datasets

Creating Complex Relationships and Hierarchies

Introduction to Key Performance Indicators (KPIs) in Power Pivot

Session 5 (2 Hours): PivotTables and PivotCharts with Power Pivot

Building Advanced PivotTables and PivotCharts

Using Slicers and Timelines for Interactive Reports

Combining Power Query and Power Pivot for Analysis

Week 3: Introduction to DAX (Data Analysis Expressions) (6 Hours)

Session 6 (2 Hours): Basics of DAX

Understanding DAX and its Syntax

Basic DAX Functions and Formulas

Creating Calculated Columns and Measures

Session 7 (2 Hours): Advanced DAX Functions

Time Intelligence Functions

Advanced DAX Formulas for Complex Calculations

Understanding Context in DAX (Row vs. Filter Context)

Session 8 (2 Hours): Practical DAX Applications

Applying DAX in Real-World Business Scenarios

Using DAX for Data Analysis and Reporting

Hands-On Exercises and Case Studies

Week 4: Integrating Skills and Capstone Project (4 Hours)

Session 9 (2 Hours): Integrating Power Query, Power Pivot, and DAX

Combining Skills for Comprehensive Data Analysis

Best Practices for Building Scalable and Efficient Excel Models

Addressing Common Business Data Analysis Challenges

Session 10 (2 Hours): Capstone Project and Course Wrap-Up

Applying Learned Skills to a Real-World Business Problem

Group Project Presentation and Review

Course Summary and Pathways for Further Learning

The course should include a mix of theoretical instruction, practical demonstrations, and hands-on exercises. The capstone project in the final week should involve a comprehensive business analysis task, enabling students to apply their newly acquired skills in Power Query, Power Pivot, and DAX to a real-world business scenario. This structure ensures that MBA students not only learn advanced Excel features but also understand how to apply these tools strategically for business analysis and decision-making.

Week 1: Introduction to Databases and SQL (4 Hours)

Session 1 (2 Hours): Fundamentals of Database Systems

Overview of Database Management Systems (DBMS)

Types of Databases: Relational vs. Non-Relational

Basic Database Terminology and Concepts

Session 2 (2 Hours): Introduction to SQL

Understanding SQL and its Role in Databases

Basic SQL Syntax and Commands

Setting Up a SQL Environment (e.g., MySQL, PostgreSQL)

Week 2: SQL for Data Retrieval (6 Hours)

Session 3 (2 Hours): Basic SQL Queries

SELECT Statements, WHERE Clauses

Working with Columns and Rows

Sorting and Filtering Data

Session 4 (2 Hours): Advanced Data Retrieval

JOIN Operations: Combining Data from Multiple Tables

Aggregating Data with GROUP BY and HAVING Clauses

Subqueries and Nested Queries

Session 5 (2 Hours): Practical SQL Exercises

Hands-on SQL Query Exercises

Case Studies on Data Retrieval in Business Scenarios

Week 3: Database Design and Data Manipulation (6 Hours)

Session 6 (2 Hours): Database Design Principles

Understanding Data Modeling and Entity-Relationship Diagrams

Normalization and Database Schemas

Creating Tables and Setting Primary/Foreign Keys

Session 7 (2 Hours): SQL for Data Manipulation and Management

INSERT, UPDATE, and DELETE Operations

Data Integrity and Transactions

Basics of Stored Procedures and Functions

Session 8 (2 Hours): Working with Real-World Data

Importing and Exporting Data

SQL for Business Analytics and Reporting

Integrating SQL Queries with Business Intelligence Tools

Week 4: Advanced Topics and Practical Applications (4 Hours)

Session 9 (2 Hours): Advanced SQL and Performance Optimization

Advanced SQL Features (e.g., Window Functions, Indexing)

Query Optimization Techniques

Understanding SQL Execution Plans

Session 10 (2 Hours): Capstone Project and Course Wrap-Up

Applying SQL Skills to a Real Business Case

Group Project Presentations

Course Summary and Further Learning Resources

This course would ideally mix theoretical lectures with practical exercises and case studies. Each session should include hands-on SQL practice, progressively building in complexity. The final capstone project should involve a comprehensive business-related database task, requiring students to apply all the skills learned throughout the course. This structure ensures that MBA students not only understand the technical aspects of SQL and database management but also how to apply these skills in a business context.

Week 1: Introduction to Power Query and Data Manipulation (4 Hours)

Session 1 (2 Hours): Fundamentals of Power Query

Introduction to Power Query and its Business Applications

Navigating the Power Query Interface

Basic Data Importing and Transformation Techniques

Session 2 (2 Hours): Advanced Data Handling in Power Query

Merging and Appending Queries

Working with Different Data Sources (Web, SQL, etc.)

Advanced Data Transformations and Cleaning Techniques

Week 2: Power Pivot and Data Modeling (6 Hours)

Session 3 (2 Hours): Introduction to Power Pivot

Overview of Power Pivot and its Role in Data Analysis

Creating Data Models in Power Pivot

Understanding Relationships and Data Model Optimization

Session 4 (2 Hours): Advanced Data Modeling in Power Pivot

Working with Large Datasets

Creating Complex Relationships and Hierarchies

Introduction to Key Performance Indicators (KPIs) in Power Pivot

Session 5 (2 Hours): PivotTables and PivotCharts with Power Pivot

Building Advanced PivotTables and PivotCharts

Using Slicers and Timelines for Interactive Reports

Combining Power Query and Power Pivot for Analysis

Week 3: Introduction to DAX (Data Analysis Expressions) (6 Hours)

Session 6 (2 Hours): Basics of DAX

Understanding DAX and its Syntax

Basic DAX Functions and Formulas

Creating Calculated Columns and Measures

Session 7 (2 Hours): Advanced DAX Functions

Time Intelligence Functions

Advanced DAX Formulas for Complex Calculations

Understanding Context in DAX (Row vs. Filter Context)

Session 8 (2 Hours): Practical DAX Applications

Applying DAX in Real-World Business Scenarios

Using DAX for Data Analysis and Reporting

Hands-On Exercises and Case Studies

Week 4: Integrating Skills and Capstone Project (4 Hours)

Session 9 (2 Hours): Integrating Power Query, Power Pivot, and DAX

Combining Skills for Comprehensive Data Analysis

Best Practices for Building Scalable and Efficient Excel Models

Addressing Common Business Data Analysis Challenges

Session 10 (2 Hours): Capstone Project and Course Wrap-Up

Applying Learned Skills to a Real-World Business Problem

Group Project Presentation and Review

Course Summary and Pathways for Further Learning

The course should include a mix of theoretical instruction, practical demonstrations, and hands-on exercises. The capstone project in the final week should involve a comprehensive business analysis task, enabling students to apply their newly acquired skills in Power Query, Power Pivot, and DAX to a real-world business scenario. This structure ensures that MBA students not only learn advanced Excel features but also understand how to apply these tools strategically for business analysis and decision-making.