NEVADA | PROFESSIONAL EXPERIENCE

Program Professional Framework

Power BI: Advanced Data Analysis Techniques

Program Overview

Experienced Power BI users eventually meet models that are slow, measures that return unexpected results and questions that basic visuals cannot answer. This advanced programme addresses them: evaluation context and context transition, iterators and variables, advanced time intelligence, calculation groups, many-to-many and role-playing relationships, performance tuning with Performance Analyzer and DAX Studio, incremental refresh, composite models, and analytical features such as key influencers, decomposition trees and Python or R visuals.

Learning Outcomes

✓Explain filter and row context and context transition, and debug measures that return unexpected values.
✓Write advanced DAX using variables, iterators and table functions for ranking, segmentation and cohort analysis.
✓Implement calculation groups for reusable time intelligence and currency logic.
✓Diagnose and improve report and model performance using Performance Analyzer and DAX Studio.
✓Configure incremental refresh and composite models for large datasets.
✓Apply key influencers, decomposition tree and Python or R visuals to explain drivers of a KPI.

Target Audience

Experienced Power BI developers
Senior data and BI analysts
BI team leads responsible for model quality
Data engineers supporting Power BI models

Detailed Program Modules

01DAX Evaluation Context

Filter and row context
Context transition and CALCULATE modifiers
Debugging measures

02Advanced DAX Patterns

Variables and iterators
Table functions and virtual tables
Ranking, segmentation and cohort patterns

03Advanced Modelling

Many-to-many and role-playing relationships
Calculation groups
Composite models and aggregations

04Performance Optimisation

VertiPaq storage and compression
Performance Analyzer and DAX Studio
Optimising measures and visuals

05Large Data Scenarios

Incremental refresh
DirectQuery trade-offs
Dataflows and reusable preparation

06Advanced Analytical Features

Key influencers and decomposition tree
Anomaly detection and forecasting
Python and R visuals

Training Methodology

Interactive expert-led sessions grounded in workplace examples

Case studies and practical exercises drawn from the region

Group workshops with peer review of decisions and deliverables

Formative feedback against clear performance criteria

Applied Project & Assessment

Applied Project

Participants optimise a supplied slow Power BI model: diagnose performance issues, rewrite key measures, add calculation groups and incremental refresh, then build a driver analysis page and document before-and-after performance results.

Assessment

Short diagnostic at the start, practical tasks linked to each learning outcome, and evaluation of the applied project against published criteria.

University Certificate & International Accreditation

Professional Certificate accredited by Twintech International University College of Technology in Power BI: Advanced Data Analysis Techniques

International accreditation: American Association of Innovation (AAI)