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Exam DA-100 Analyzing Data with Microsoft Power BI (Video)

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Exam DA-100 Analyzing Data with Microsoft Power BI (Video)

By Chris Sorensen
Published Oct 1, 2021 by Microsoft Press.

Online Video

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Description

Sample Content

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Description

Copyright 2022
Edition: 1st

Online Video
ISBN-10: 0-13-757457-6
ISBN-13: 978-0-13-757457-5

8+ Hours of Video Instruction

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.

Skill Level

Intermediate

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

Course Requirements

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

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Sample Content

Introduction

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 relationship's 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

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