Key Learning Outcomes
Upon completion of this program, you will:
1. Master the Power BI Ecosystem: Develop a strong understanding of the Power BI Desktop, Power Query, and Power BI Service to handle data end-to-end.
2. Analyze and Model Data: Gain the ability to clean, model, and structure data effectively to support informed decision-making.
3. Develop Advanced Dashboards: Create professional dashboards with actionable insights, combining visuals, DAX calculations, and dynamic filtering.
4. Automate and Extend Power BI: Automate manual tasks using Power Automate and expand reporting capabilities through integration with external tools like Power Apps and SQL.
5. Prepare for Certification: Be exam-ready for the PL-300 certification, showcasing your Power BI expertise to employers and clients.
Lesson Plan
Module 1: Induction Class
Learn the installation process and interface navigation to start your journey. Then, get a foundational understanding of Power BI, its ecosystem, and its relevance in business analytics.
1.1 Introduction to Power BI
Overview of Power BI ecosystem (Desktop, Service, Mobile).
Understanding the role of Power BI in business analytics.
Key benefits and use cases across industries.
1.2 Downloading Power BI & Adjusting Settings
Step-by-step guide to download and install Power BI Desktop.
Configuring system requirements for optimal performance.
Adjusting settings like regional preferences and enabling preview features.
1.3 Introduction to Curriculum
Overview of the course structure and learning objectives.
Key milestones and project expectations.
Brief on evaluation criteria and real-world applications.
1.4 Exploring the Power BI Desktop Interface & Workflow
Familiarization with the Power BI interface (Home, Report, Data, and Model views).
Understanding workflows: data import, modeling, and visualization.
Exploring the ribbon, panes, and navigation options.
Module 2: Power Query
Dive into the first step of data preparation with Power Query. Learn to connect, clean, and transform raw data for meaningful analysis.
2.1 Data Source Connectivity
Connecting to different data sources: Excel, SQL Server, Web, SharePoint, and APIs.
Using connectors and managing source credentials.
2.2 Data Validation
Techniques for identifying errors in data.
Handling missing values, duplicates, and inconsistent data formats.
2.3 Query Dependencies
Understanding query dependencies and data flow in Power BI.
Managing dependencies for seamless updates.
2.4 Query Parameters
Creating and using parameters for dynamic data queries.
Applying parameters in data filtering and transformations.
2.5 Data Preview
Exploring data using the preview pane.
Profiling data to understand distribution, quality, and structure.
2.6 Load into Power BI
Steps to load transformed data into the Power BI model.
Managing loading options: Direct Query vs. Import mode.
2.7 Data Chasing
Techniques for tracing and fixing errors in query steps.
Monitoring query performance and resolving bottlenecks.
2.8 Data Transformation
Applying essential transformations: merge, append, pivot, and unpivot.
Custom transformations using M language.
Module 3: Data Modeling
Learn the art of efficient data modeling to create logical relationships and optimized datasets for analysis.
3.1 Data Loading
Configuring load settings for large datasets.
Managing incremental data loads.
3.2 Relationship Building
Types of relationships: one-to-one, one-to-many, and many-to-many.
Cardinality and cross-filtering direction explained.
3.3 Creating Data Measures
Introduction to measures and calculated columns.
Building dynamic metrics like average, count, and totals.
3.4 Data Hierarchies
Creating hierarchies for drilling through data (e.g., Year → Quarter → Month).
Benefits of hierarchical views for reporting.
3.5 Data Modeling Best Practices
Tips for maintaining a clean and efficient data model.
Avoiding common pitfalls like circular dependencies and redundant relationships.
3.6 Testing
Validating data models through test queries.
Ensuring accuracy and performance of relationships and measures.
3.7 Semantic Modelling
Adding descriptions, synonyms, and perspectives for enhanced usability.
Preparing models for consumption by business users.
Module 4: DAX Functions
Master Data Analysis Expressions (DAX) for advanced data manipulations and insights.
4.1 Aggregation Functions
Using SUM, AVERAGE, MIN, MAX, and COUNT in reports.
Writing aggregation formulas for custom scenarios.
4.2 Filtering Functions
Applying functions like CALCULATE, FILTER, and ALL.
Building dynamic filters for segmented views.
4.3 Time Intelligence Functions
Creating time-based calculations like year-to-date (YTD), month-to-date (MTD).
Handling fiscal years and custom calendars.
4.4 Date & Time Functions
Manipulating date fields with DATE, WEEKNUM, and NOW functions.
Extracting insights like weekdays and quarters.
4.5 Logical Functions
Writing IF, SWITCH, and nested logical functions.
Managing conditional calculations effectively.
4.6 Ranking Functions
Using RANKX for creating rankings in datasets.
Customizing ranking orders and handling ties.
4.7 Statistical Functions
Performing variance, standard deviation, and other statistical calculations.
Applying statistical insights to business scenarios.
Module 5: Visualizations I
Create fundamental and interactive visuals for actionable insights.
5.1 Charts & Graphs
Building bar, line, and area charts.
Selecting appropriate charts for data representation.
5.2 Tables & Matrices
Using tables for detailed data views.
Creating pivot-style matrices for summaries.
5.3 Cards & KPIs
Displaying single-value metrics like revenue and profit.
Designing KPI visuals with thresholds.
5.4 Maps & Geographic Visualizations
Using map visuals for geographical data.
Integrating Bing Maps for advanced mapping.
5.5 Gauges
Creating speedometer-style visuals for progress tracking.
Setting thresholds for indicators.
5.6 Slicers & Filters
Adding slicers for dynamic data filtering.
Applying page-level and report-level filters.
Module 6: Visualizations II
Explore advanced techniques and customization options for professional dashboards.
6.1 Custom Visuals
Importing and using visuals from AppSource.
Creating bespoke visuals with R and Python.
6.2 Drill through & Drill down
Enabling drill-through for in-depth analysis.
Managing drill-down paths within hierarchies.
6.3 Bookmarks & Interactivity
Creating interactive experiences with bookmarks.
Using buttons and toggles for enhanced navigation.
6.4 Themes & Formatting
Applying and customizing themes for branding.
Formatting reports for presentation.
6.5 Tool Tips
Adding detailed tooltips for additional context.
Creating custom tooltip pages.
6.6 Waterfall Charts & Funnel Charts
Building waterfall visuals for financial analysis.
Using funnel charts to analyze process performance.
Special Class 1: Data Science Theoretical Concepts
Gain foundational knowledge of data science concepts and understand how to apply them in Power BI to derive actionable insights.
Topics Covered
Statistical Analysis:
Understanding key statistical concepts (mean, median, standard deviation).
Visualizing statistical data in Power BI.
Regression Analysis:
Exploring linear and logistic regression models.
Implementing regression models for predictive insights.
Predictive Analysis:
Introduction to predictive modeling techniques.
Forecasting trends using Power BI visuals.
Descriptive Analysis:
Summarizing datasets with descriptive statistics.
Creating dashboards for summarizing key metrics.
Time Series Analysis:
Understanding time-based patterns and seasonality.
Applying forecasting techniques in Power BI.
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Special Class 2: Components of Power BI
Understand the core components of Power BI and how they work together for an end-to-end analytical process.
Topics Covered
Power Query:
Data extraction and transformation techniques.
Power Pivot:
Building efficient data models with calculated fields.
Power BI Desktop:
Designing reports and building relationships.
Power BI Website:
Publishing and sharing dashboards.
Power Apps:
Integrating applications to extend Power BI workflows.
Integrating Artificial Intelligence in Power BI:
Using AI visuals like Key Influencers and Decomposition Tree.
Building predictive models with Azure AI integration.
Power Automate:
Automating tasks like email notifications and data updates.
Special Class 3: Presentation and Inferential Analytics
Master storytelling techniques to effectively communicate insights using dashboards and analytics.
Topics Covered
Visualizations:
Designing visually impactful dashboards for presentations.
Statistical Measures:
Using advanced measures for inferential insights.
Regression Analysis:
Demonstrating relationships and predictions visually.
Forecasting:
Presenting trend forecasts with confidence intervals.
Dashboard Layouts:
Optimizing layouts for readability and stakeholder engagement.
Data Tables:
Summarizing and exporting data for reports.
Export & Sharing:
Publishing dashboards and exporting them to PDF or PowerPoint.
Case Study: Comprehensive Application in HR Analytics
Apply all learned concepts in a practical, real-world scenario.
Scenario
A company’s HR department requires a Power BI dashboard to analyze employee performance, retention rates, and training program effectiveness.
Steps
Data Preparation:
Collect and clean data from HR management systems and Excel files.
Merge data sources using Power Query.
Model Building:
Establish relationships between datasets (e.g., employees, training sessions, and performance reviews).
Build calculated measures for KPIs such as:
Attrition Rate = (Employees Left / Total Employees) * 100.
Average Training Hours per Employee = Total Training Hours / Total Employees.
DAX Implementation:
Create advanced metrics like:
Engagement Score: Weighted index based on survey results.
Training Effectiveness: Ratio of training scores to performance improvements.
Visualization:
Design an interactive dashboard with:
Employee retention and attrition trends.
Engagement scores using KPIs and gauges.
Training program effectiveness using heatmaps.
Sharing & Collaboration:
Publish the dashboard to Power BI Service.
Set up row-level security (RLS) for restricted views based on user roles.
Share links and schedules for automated report delivery to HR stakeholders.
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