Prepare for Microsoft Exam DA-100 and demonstrate your mastery of Power BI data analysis and visualization.
This Exam DA-100: Analyzing Data with Microsoft Power BI video is designed for data analysts responsible for designing scalable data models, cleaning and transforming data, and presenting analytic insights through data visualizations using Power BI. This video focuses on the skills measured by the exam objectives, as updated by Microsoft on July 29, 2021.
Prepare the data
Model the data
Visualize the data
Analyze the data
Deploy and maintain deliverables
Using his years of experience teaching Power BI to a variety of learners, Microsoft Certified Trainer Chris Sorensen explains how to optimize Power BI features and functions and prepares you for what to expect on the DA-100 exam. In his engaging style grounded in real-world scenarios, Chris gives you insights to navigate and build effective Power BI solutions, quickly and effectively. With Chris as your guide, you are well-equipped to advance in your career as a data analyst.
Who Should Take This Course
Certification candidates preparing for Exam DA-100: Analyzing Data with Microsoft Power BI
Data analysts who want to use Microsoft Power BI to maximize their data assets
Business intelligence professionals who want to advance their knowledge of data processing and analytics
Power BI Desktop installed on your machine
Access to the Power BI service
Familiarity with the end-to-end process of connecting to data sources, cleaning and transforming data, modeling data for self-service consumption, building reports, and securely distributing reports and dashboards
Table of Contents
Module 1: Prepare the Data Lesson 1: Get Data from Different Data Sources Learning objectives 1.1 Identify and connect to a data source 1.2 Change data source settings 1.3 Select a shared dataset or create a local dataset 1.4 Select a storage mode 1.5 Choose an appropriate query type 1.6 Identify query performance issues 1.7 Use Microsoft Dataverse 1.8 Use parameters 1.9 Use or create a PBIDS file 1.10 Use or create a data flow 1.11 Connect to a dataset using the XMLA endpoint Lesson 2: Profile the Data Learning objectives 2.1 Identify data anomalies 2.2 Examine data structures 2.3 Interrogate column properties 2.4 Interrogate data statistics Lesson 3: Clean, Transform, and Load the Data Learning objectives 3.1 Resolve inconsistencies, unexpected or null values, and data quality issues 3.2 Apply user-friendly value replacements 3.3 Identify and create appropriate keys for joins 3.4 Evaluate and transform column data types 3.5 Apply data shape transformations to table structures 3.6 Combine queries 3.7 Apply user-friendly naming conventions to columns and queries 3.8 Leverage Advanced Editor to modify Power Query M code 3.9 Configure data loading 3.10 Resolve data import errors
Module 2: Model the Data Lesson 4: Design a Data Model Learning objectives 4.1 Define the tables 4.2 Configure table and column properties 4.3 Define quick measures 4.4 Flatten out a parent-child hierarchy 4.5 Define role-playing dimensions 4.6 Define a relationships cardinality and cross-filter direction 4.7 Design the data model to meet performance requirements 4.8 Resolve many-to-many relationships 4.9 Create a common date table 4.10 Define the appropriate level of data granularity Lesson 5: Develop a Data Model Learning objectives 5.1 Apply cross-filter direction and security filtering 5.2 Create calculated tables 5.3 Create hierarchies 5.4 Create calculated columns 5.5 Implement row-level security roles 5.6 Set up the Q&A feature 5.7 Implement object-level security Lesson 6: Create Measures by Using DAX Learning objectives 6.1 Use DAX to build complex measures 6.2 Use CALCULATE to manipulate filters 6.3 Implement Time Intelligence using DAX 6.4 Replace numeric columns with measures 6.5 Use basic statistical functions to enhance data 6.6 Create semi-additive measures Lesson 7: Optimize Model Performance Learning objectives 7.1 Remove unnecessary rows and columns 7.2 Identify poorly performing measures, relationships, and visuals 7.3 Improve cardinality levels 7.4 Optimize DirectQuery models 7.5 Create and manage aggregations 7.6 Use Query Diagnostics
Module 3: Visualize the Data Lesson 8: Create Reports Learning objectives 8.1 Add visualization items to reports 8.2 Choose an appropriate visualization type 8.3 Format and configure visualizations 8.4 Import a custom visual 8.5 Configure and apply conditional formatting 8.6 Apply slicing and filtering 8.7 Add an R or Python visual 8.8 Configure the report page 8.9 Design and configure for accessibility 8.10 Configure automatic page refresh 8.11 Create a paginated report Lesson 9: Create Dashboards Learning objectives 9.1 Build a dashboard 9.2 Set mobile view 9.3 Manage tiles on a dashboard 9.4 Configure data alerts 9.5 Use the Q&A feature 9.6 Add a dashboard theme 9.7 Pin a live report page to a dashboard Lesson 10: Enrich Reports for Usability Learning objectives 10.1 Configure bookmarks 10.2 Create custom tooltips 10.3 Edit and configure interactions between visuals 10.4 Configure navigation for a report 10.5 Apply sorting 10.6 Configure Sync Slicers 10.7 Use the selection pane 10.8 Use drillthrough and cross filter 10.9 Drilldown into data using interactive visuals 10.10 Export report data 10.11 Design reports for mobile devices
Module 4: Analyze the Data Lesson 11: Enhance Reports to Expose Insights Learning objectives 11.1 Perform top N analysis 11.2 Explore statistical summary 11.3 Use the Q&A visual 11.4 Add a Quick Insights result to a report 11.5 Create reference lines by using Analytics pane 11.6 Personalize visuals Lesson 12: Perform Advanced Analysis Learning objectives 12.1 Identify outliers 12.2 Conduct Time Series analysis 12.3 Use groupings and binnings 12.4 Use the Key Influencers to explore dimensional variances 12.5 Use the decomposition tree visual to break down a measure 12.6 Apply AI Insights
Module 5: Deploy and Maintain Deliverables Lesson 13: Manage Datasets Learning objectives 13.1 Configure a dataset scheduled refresh 13.2 Configure row-level security group membership 13.3 Provide access to datasets 13.4 Configure incremental refresh settings 13.5 Promote or certify Power BI datasets 13.6 Identify downstream dataset dependencies 13.7 Configure large dataset format Lesson 14: Create and Manage Workspaces Learning objectives 14.1 Create and configure a workspace 14.2 Recommend a development lifecycle strategy 14.3 Assign workspace roles 14.4 Configure and update a workspace app 14.5 Publish, import, or update assets in a workspace 14.6 Apply sensitivity labels to workspace content 14.7 Use deployment pipelines 14.8 Configure subscriptions 14.9 Promote or certify Power BI content Summary