Course Information
Advanced Data Modeling and DAX in Power BI
Parking for SAO, Professional Development courses is in Garage B (1511 San Jacinto Blvd.). The Garage signage may read 1511 San Jacinto or Garage B. The elevator in Garage B is not reliable. If you are unable to walk the stairs, please contact the professionaldevelopment@sao.texas.gov for alternate parking arrangements. Handicapped parking is free at the meters around the downtown area.
A course coordinator will email you a parking permit prior to the course start date. A permit must be displayed or you will be ticketed.
Course Description
In this Power BI course, participants develop a deep understanding of Power BI’s data modeling and DAX capabilities. They explore advanced techniques for creating and optimizing data relationships, categorization, and hierarchical structures. The course also covers powerful DAX expressions, statistical functions, and best practices for enhancing performance. Through hands-on exercises, participants learn to build efficient, high-performing reports and dashboards using advanced Power BI techniques.
Course Objectives
Objectives
Understand and implement different data modeling approaches, including Star and Snowflake schemas
Create and optimize relationships, hierarchies, and groupings for efficient data representation
Develop proficiency in writing and debugging complex DAX expressions
Apply statistical and ranking functions to perform advanced analytics in Power BI
Optimize Power BI performance using best practices, query folding, and performance analysis tools
Outline
Data Modeling in Power BI
Understanding Data Modeling
Star vs. Snowflake Schema: When to Use Each
Creating Relationships: One-to-One, One-to-Many, and Many-to-Many
Organizing Data: Display Formats, Categorization, and Folders
Building Hierarchies for Efficient Data Navigation
Grouping and Binning for Aggregated Insights
A Quick Overview of DAX
What is DAX?
Calculated measures, columns, and tables
Using SUM and SUMX
Usuing FILTER
Using CALCULATE
Statistical and Ranking Functions in DAX
Ranking Functions: RANKX, RANK.EQ, RANK.AVG, DENSERANK, PERCENTRANKX
Quartile and Percentile Functions: NTILE, QUARTILE.EXC, QUARTILE.INC, PERCENTILE.EXC, PERCENTILE.INC
Central Tendency Measures: MEAN, MEDIAN
Standard Deviation and Variance: STDEV.P, STDEV.S, VAR.P, VAR.S
Advanced Statistical Functions: SKEWNESS, KURTOSIS, COVARIANCE.P, COVARIANCE.S, CORREL
ANOVA: Performing Variance Analysis in Power BI
Data Sampling and Filtering in DAX
Using SAMPLE and TOPN for Data Sampling
Generating Random Data with RAND()
Filtering Techniques: SWITCH, FILTER, and ALL Functions
Concatenating Text Data: CONCATENATE() and CONCATENATEX()
Counting Functions: COUNT(), COUNTA(), COUNTBLANK(), COUNTROWS(), DISTINCTCOUNT(), DISTINCTCOUNTNOBLANK()
Advanced DAX Concepts
Working with Calculation Groups in Tabular Editor
Creating Dedicated Tables for Measures
Using Variables in DAX for Performance Optimization
Advanced Variable Techniques for Complex Calculations
Debugging DAX Expressions Using DAX Studio
Power BI Performance Optimization
Choosing Between DirectQuery, Import, and Hybrid Models
Using Performance Analyzer to Identify Bottlenecks
Understanding Query Folding and How It Affects Performance
Leveraging the Best Practice Analyzer for Optimized Data Models
Effective Strategies for Data Grouping and Summarization
Prerequisites
Basic understanding of Power BI, including data loading and report creation Familiarity with fundamental DAX functions and concepts Basic knowledge of relational database concepts and SQL (recommended).
Instructors
Drew is the Head of Consulting and a Senior Software Trainer for Knack Training with a master's degree in Curriculum Design & Instruction. She brings together a strong technical foundation with a deep understanding of adult learning, which allows her to design training that is clear, practical, and rooted in real workplace scenarios. Her background includes years of experience in Excel, PowerPoint, SharePoint, and the Power Platform, along with a history of developing training programs that help learners build confidence with complex tools.
As both a consultant and trainer, Drew is known for breaking down technical concepts into practical steps that resonate with learners who are new to data tools as well as those looking to deepen their analytical skills. Her approach focuses on helping professionals solve real problems through better data design, clear modeling practices, and thoughtful visualization. She aims to help every learner gain confidence with their tools and understand how strong analytics can support high-quality decision making across an organization.