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Curriculum
- 25 Sections
- 108 Lessons
- 10 Weeks
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- Introduction to Power BI and Business Intelligence5
- Power BI Desktop Interface3
- Connecting to Data Sources4
- Introduction to Power Query Editor (ETL Basics)5
- Basic Data Visualization5
- 5.1Principles of effective data visualization.
- 5.2Creating basic charts: Bar charts, Column charts, Line charts, Pie charts.
- 5.3Understanding and using cards, tables, and matrices.
- 5.4Adding titles, labels, and legends.
- 5.5Introduction to filtering data in visuals (visual, page, and report level filters).
- First Report Creation & Sharing Basics3
- Data Transformation & Modelling (Intermediate)0
- Advanced Power Query & Data Transformation7
- 8.1Merging queries (Joins): Understanding different join types (Inner, Left, Right, Full, Anti).
- 8.2Appending queries.
- 8.3Grouping and aggregating data.
- 8.4Pivoting and Unpivoting columns.
- 8.5Creating custom columns using M language basics.
- 8.6Conditional columns and indexing.
- 8.7Data profiling and quality checks.
- Data Modelling Fundamentals6
- 9.1Importance of a robust data model.
- 9.2Understanding relationships: One-to-one, One-to-many, Many-to-many.
- 9.3Creating and managing relationships in Power BI.
- 9.4Star Schema vs. Snowflake Schema: Best practices for data modelling.
- 9.5Fact tables and Dimension tables.
- 9.6Optimizing data models for performance.
- Introduction to Data Analysis Expressions (DAX)5
- Intermediate Visualizations & Interactivity6
- 11.1Advanced chart types: Scatter plots, Bubble charts, Area charts, Combo charts.
- 11.2Using Slicers for interactive filtering.
- 11.3Drill-through and Drill-down functionality.
- 11.4Bookmarks for guided storytelling.
- 11.5Conditional formatting for highlighting insights.
- 11.6Customizing themes and report appearance.
- Report Design Best Practices4
- Advanced DAX & Data Storytelling (Advanced)0
- Advanced DAX Concepts7
- 14.1CALCULATE function: The most powerful DAX function.
- 14.2Understanding context transition.
- 14.3Time intelligence functions: TOTALYTD, SAMEPERIODLASTYEAR, DATEADD, DATESBETWEEN.
- 14.4Creating complex measures: % of total, running totals, cumulative sums.
- 14.5Iterating functions: SUMX, AVERAGEX, COUNTX.
- 14.6Variables in DAX.
- 14.7Error handling in DAX.
- Advanced Data Modelling Techniques5
- Data Storytelling with Power BI5
- Power BI Service: Advanced Features & Administration7
- 17.1Workspaces and app creation.
- 17.2Sharing and collaboration: Roles and permissions.
- 17.3Row-Level Security (RLS) implementation for data governance.
- 17.4Data gateways for connecting to on-premises data.
- 17.5Scheduled data refresh.
- 17.6Monitoring usage metrics and performance.
- 17.7Introduction to Power BI Apps and embedding reports.
- Integrating Power BI with Other Tools4
- Real-World Applications & Capstone Project0
- Case Studies & Industry Applications6
- 20.1Analysing sales data for a retail company.
- 20.2Financial reporting and budgeting with Power BI.
- 20.3HR analytics and workforce insights.
- 20.4Marketing campaign performance analysis.
- 20.5Supply chain and operations dashboards.
- 20.6Leveraging Power BI for specific industry problems (e.g., healthcare, manufacturing).
- Performance Tuning and Optimization5
- Capstone Project1
- Project Phases:7
- 23.1Data Acquisition & Understanding: Identifying data sources, understanding business requirements.
- 23.2Data Preparation & Cleaning: Extensive use of Power Query for complex transformations.
- 23.3Data Modelling: Designing an optimized star/snowflake schema, defining relationships.
- 23.4DAX Calculations: Creating a comprehensive set of measures and calculated columns for business insights.
- 23.5Report & Dashboard Development: Designing interactive, insightful, and visually appealing reports and dashboards.
- 23.6Data Storytelling & Presentation: Presenting findings and recommendations to a “stakeholder” audience.
- 23.7Documentation: Documenting the data model, DAX measures, and design choices.
- Preparing for the Certification Exam4
- Career Guidance for Data Analysts in MNCs4
Role of a Data Analyst in an MNC.
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