Power BI: Data Analysis Professional
Learn to manipulate data with Power BI, turning numbers into easy-to-read visuals and insights.
(PWRBI-EXCEL.AP1) / ISBN : 978-1-64459-388-2About This Course
Get your hands dirty with data analysis using the Power BI app and Power Pivot tool within Excel. This hands-on training course arms you with skills to analyze, visualize, and report information. You’ll learn to connect various data sources, build data models, create dashboards, and perform advanced analysis techniques that will spot hidden insights. With this “Analyzing data with Power BI” course, you’ll make decisions to improve your business processes and get ahead of the competition in the data-centric industry.
Skills You’ll Get
- Become proficient in using Excel, Power Pivot, and Power BI for data analysis and data visualization
- Become proficient in using Excel, Power Pivot, and Power BI for data analysis and data visualization
- Connect various data sources, such as Excel, SQL Server, CSV files, and online databases
- Learn to create effective star and snowflake schemas, establish relationships, and implement hierarchies
- Utilize DAX (Data Analysis Expressions) to create calculated columns, measures, and perform complex calculations
- Develop visually appealing and informative dashboards and reports to communicate insights effectively
- Understand and apply various data analysis techniques, including time intelligence, data cleansing, and performance optimization
- Integrate Power BI with other tools and technologies, such as SQL Server and Python
Get the support you need. Enroll in our Instructor-Led Course.
Interactive Lessons
13+ Interactive Lessons | 87+ Exercises | 50+ Quizzes | 43+ Flashcards | 43+ Glossary of terms
Gamified TestPrep
37+ Pre Assessment Questions | 38+ Post Assessment Questions |
Hands-On Labs
21+ LiveLab | 21+ Video tutorials | 01:48+ Hours
Introduction
- Who this course is for?
- Organization of this course
- Conventions
Introduction to data modeling
- Working with a single table
- Introducing the data model
- Introducing star schemas
- Understanding the importance of naming objects
- Conclusions
Using header/detail tables
- Introducing header/detail
- Aggregating values from the header
- Flattening header/detail
- Conclusions
Using multiple fact tables
- Using denormalized fact tables
- Filtering across dimensions
- Understanding model ambiguity
- Using orders and invoices
- Conclusions
Working with date and time
- Creating a date dimension
- Understanding automatic time dimensions
- Using multiple date dimensions
- Handling date and time
- Time-intelligence calculations
- Handling fiscal calendars
- Computing with working days
- Handling special periods of the year
- Working with weekly calendars
- Conclusions
Tracking historical attributes
- Introducing slowly changing dimensions
- Using slowly changing dimensions
- Loading slowly changing dimensions
- Rapidly changing dimensions
- Choosing the right modeling technique
- Conclusions
Using snapshots
- Using data that you cannot aggregate over time
- Aggregating snapshots
- Understanding derived snapshots
- Understanding the transition matrix
- Conclusions
Analyzing date and time intervals
- Introduction to temporal data
- Aggregating with simple intervals
- Intervals crossing dates
- Modeling working shifts and time shifting
- Analyzing active events
- Mixing different durations
- Conclusions
Many-to-many relationships
- Introducing many-to-many relationships
- Cascading many-to-many
- Temporal many-to-many
- Using the fact tables as a bridge
- Conclusions
Working with different granularity
- Introduction to granularity
- Relationships at different granularity
- Conclusions
Segmentation data models
- Computing multiple-column relationships
- Computing static segmentation
- Using dynamic segmentation
- Understanding the power of calculated columns: ABC analysis
- Conclusions
Working with multiple currencies
- Understanding different scenarios
- Multiple source currencies, single reporting currency
- Single source currency, multiple reporting currencies
- Multiple source currencies, multiple reporting currencies
- Conclusions
Appendix A. Data modeling 101
- Tables
- Data types
- Relationships
- Filtering and cross-filtering
- Different types of models
- Measures and additivity
Introduction to data modeling
- Exploring a Dataset
Using header/detail tables
- Aggregating Values from the Header Table
Using multiple fact tables
- Analyzing Denormalized Fact Tables
- Understanding Model Ambiguity
Working with date and time
- Creating a Date Dimension
Tracking historical attributes
- Analyzing Slowly Changing Dimensions
Using snapshots
- Analyzing Snapshots
- Analyzing Derived Snapshots
- Understanding the Transition Matrix
Analyzing date and time intervals
- Understanding Temporal Data
- Analyzing Events that Cross Dates
- Analyzing Active Events
- Mixing Different Durations
Many-to-many relationships
- Exploring Many-to-Many Relationships
- Exploring a Temporal Many-to-Many Relationship
Working with different granularity
- Analyzing Relationships at Different Granularity
Segmentation data models
- Analyzing Calculated Physical Relationships
- Analyzing Dynamic Segmentation
- Understanding ABC Analysis
Working with multiple currencies
- Producing a Report Containing Information With a Single Type of Currency
- Producing a Report in Multiple Currencies
Power BI is a business intelligence tool for analyzing and visualizing data, while Power Pivot is a data modeling engine within Excel.
Power Pivot is used to create data models, and Power BI is used to build interactive dashboards and reports based on those models.
Basic knowledge of Excel is recommended for our Power BI and Excel for data analysis training, but no prior experience with Power BI or Power Pivot is necessary.
Learning Power BI and Power Pivot can enhance your skills in data analysis and visualization, making you an attractive candidate in the job market.