Prepare for the PL-300 exam with this course covering data preparation, modeling, visualization, and deployment. Learn how to extract insights through effective data management and analysis.

Syllabus

Introduction

Exam PL-300 Microsoft Power BI Data Analyst: Introduction

Module 1: Prepare the Data

Module introduction

Lesson 1: Get Data from Data Sources

Learning objectives
Identify and connect to a data source
Change data source settings, including credentials, privacy levels, and data source locations
Select a shared semantic model, or create a local semantic model
Choose between DirectQuery, Import, and Dual mode
Change the value in a parameter

Lesson 2: Clean the Data

Learning objectives
Evaluate data, including data statistics and column properties
Resolve inconsistencies, unexpected or null values, and data quality issues
Resolve data import errors

Lesson 3: Transform and Load the Data

Learning objectives
Select appropriate column data types
Create and transform columns
Transform a query
Design a star schema that contains facts and dimensions
Identify when to use reference or duplicate queries and the resulting impact
Merge and append queries
Identify and create appropriate keys for relationships
Configure data loading for queries

Module 2: Model the Data

Module introduction

Lesson 4: Design and Implement a Data Model

Learning objectives
Configure table and column properties
Implement role-playing dimensions
Define a relationship's cardinality and cross-filter direction
Create a common date table
Implement row-level security roles

Lesson 5: Create Model Calculations by Using DAX

Learning objectives
Create single aggregation measures
Use CALCULATE to manipulate filters
Implement time intelligence measures
Identify implicit measures and replace with explicit measures
Use basic statistical functions
Create semi-additive measures
Create a measure by using quick measures
Create calculated tables

Lesson 6: Optimize Model Performance

Learning objectives
Improve performance by identifying and removing unnecessary rows and columns
Identify poorly performing measures, relationships, and visuals by using Performance Analyzer
Improve performance by choosing optimal data types
Improve performance by summarizing data

Module 3: Visualize and Analyze the Data