Course Overview:
This course focuses on equipping participants with the skills to use Power Query and Power Pivot in Microsoft Excel to transform raw data into actionable insights. Participants will learn how to clean, shape, and combine data from various sources, create data models, and develop advanced reports and dashboards.
Course Objectives:
- Understand Power Query and Power Pivot Fundamental
- Connect to and Transform Data
- Create Data Models
- Master DAX for Advanced Calculations
- CDevelop Interactive Reports
- Enhance Data Analysis Efficiency
- Enable Data-Driven Decision Making
Who Should Attend?
Data Analysts
Business Intelligence Professionals
Excel Users
Finance and Accounting Professionals
IT and Database Professionals
Course Prerequisites
Basic knowledge of Microsoft Excel
Familiarity with fundamental Excel functions such as formulas, charts, and pivot tables
No prior experience with Power Query or Power Pivot is required, but it is helpful
Course Content:
Module 1: Introduction to Power Query and Power Pivot
Overview of Power Query and Power Pivot in Microsoft Excel
Key differences and how they complement each other
Connecting to various data sources
Module 2: Data Transformation with Power Query
Cleaning and preparing raw data
Merging, appending, and filtering datasets
Applying transformations such as splitting, grouping, and pivoting data
Managing query dependencies and advanced editor options
Module 3: Building Data Models with Power Pivot
Creating and managing data models in Excel
Adding calculated columns and measures using DAX (Data Analysis Expressions)
Setting up relationships between tables
Handling large datasets with efficient modeling
Module 4: Advanced DAX Functions
Overview of DAX syntax and concepts
Advanced calculations and measures
Using time intelligence functions for date-related analysis
Module 5: Creating Visualizations and Reports
Designing interactive dashboards with slicers and timelines
Building pivot tables and pivot charts
Customizing visuals for data storytelling
Module 6: Best Practices for Data Analysis
Performance optimization for queries and models
Effective naming conventions and documentation
Common pitfalls and how to avoid them