Exam DA-100: Analyzing Data with Microsoft Power BI

Course Duration: 24 hours
Course Fee: Contact Us
Training Mode: On-Demand

Data Analysts enable businesses to maximize the value of their data assets by using Microsoft Power BI. As a subject matter expert, data analysts are responsible for designing and building scalable data models, cleaning and transforming data, and enabling advanced analytic capabilities that provide meaningful business value through easy-to-comprehend data visualizations. Data analysts also collaborate with key stakeholders across verticals to deliver relevant insights based on identified business requirements.

The Data Analyst should have a fundamental understanding of data repositories and data processing both on-premises and in the cloud.

Prepare the Data (20-25%)
Get data from different data sources
  • identify and connect to a data source
  • change data source settings
  • select a shared dataset or create a local dataset
  • select a storage mode
  • choose an appropriate query type
  • identify query performance issues
  • use the Common Data Service (CDS)
  • use parameters
Profile the data

identify data anomalies examine data structures interrogate column properties interrogate data statistics

Clean, transform, and load the data
  • resolve inconsistencies, unexpected or null values, and data quality issues
  • apply user-friendly value replacements
  • identify and create appropriate keys for joins
  • evaluate and transform column data types
  • apply data shape transformations to table structures
  • combine queries
  • apply user-friendly naming conventions to columns and queries
  • leverage Advanced Editor to modify Power Query M code
  • configure data loading
  • resolve data import errors
Model the Data (25-30%)
Design a data model
  • define the tables
  • configure table and column properties
  • define quick measures
  • flatten out a parent-child hierarchy
  • define role-playing dimensions
  • define a relationship’s cardinality and cross-filter direction
  • design the data model to meet performance requirements
  • resolve many-to-many relationships
  • create a common date table
  • define the appropriate level of data granularity
Develop a data model
  • apply cross-filter direction and security filtering
  • create calculated tables
  • create hierarchies
  • create calculated columns
  • implement row-level security roles
  • set up the Q&A feature
Create measures by using DAX

use DAX to build complex measures use CALCULATE to manipulate filters implement Time Intelligence using DAX replace numeric columns with measures

  • use basic statistical functions to enhance data
  • create semi-additive measures
Optimize model performance
  • remove unnecessary rows and columns
  • identify poorly performing measures, relationships, and visuals
  • improve cardinality levels by changing data types
  • improve cardinality levels through summarization
  • create and manage aggregations
Visualize the Data (20-25%)
Create reports
  • add visualization items to reports
  • choose an appropriate visualization type
  • format and configure visualizations
  • import a custom visual
  • configure conditional formatting
  • apply slicing and filtering
  • add an R or Python visual
  • configure the report page
  • design and configure for accessibility
Create dashboards
  • set mobile view
  • manage tiles on a dashboard
  • configure data alerts
  • use the Q&A feature
  • add a dashboard theme
  • pin a live report page to a dashboard
  • configure data classification

Enrich reports for usability

  • configure bookmarks
  • create custom tooltips
  • edit and configure interactions between visuals configure navigation for a report apply sorting configure Sync Slicers

use the selection pane

  • use drill through and cross filter
  • drilldown into data using interactive visuals
  • export report data

Analyze the Data (10-15%)

Enhance reports to expose insights

  • apply conditional formatting
  • apply slicers and filters
  • perform top N analysis
  • explore statistical summary
  • use the Q&A visual
  • add a Quick Insights result to a report
  • create reference lines by using Analytics pane
  • use the Play Axis feature of a visualization

Perform advanced analysis

  • identify outliers
  • conduct Time Series analysis
  • use groupings and binnings
  • use the Key Influencers to explore dimensional variances
  • use the decomposition tree visual to break down a measure
  • apply AI Insights

Deploy and Maintain Deliverables (10-15%)

Manage datasets
  • configure a dataset scheduled refresh
  • configure row-level security group membership
  • providing access to datasets
  • configure incremental refresh settings
  • endorse a dataset
Create and manage workspaces
  • create and configure a workspace
  • recommend a development lifecycle strategy assign workspace roles configure and update a workspace app publish, import, or update assets in a workspace
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