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- Data Analytics
Curriculum
- 9 Sections
- 64 Lessons
- 10 Weeks
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- Module 1 – Power BI6
- 1.11.1 Foundations of Data Analytics & Power BI – Fundamentals, Storage Systems, Cloud Computing, and Power BI Overview
- 1.21.2 Power BI Connectivity Modes: Import, Direct Query & Live Connections
- 1.31.3 Power BI Components, Data Transformation & Refreshing Techniques
- 1.41.4 Power BI Data Preparation: File Handling, Query Editor, Cleaning, Date Logic & Optimization
- 1.51.5 Power BI Source Fixing, Data Cleaning & Transformation Techniques
- 1.61.6 Power Query Editor Components, Text & Number Transformations in Power BI
- Module 2 – Advanced Transformations in Power BI4
- Module 3 – SQL Functions, Joins & Data ModelingFunctions10
- Module 4 – Advanced SQL, Automation & Data Management8
- Module 5 – Python for Data Analytics6
- Module 6 – Python Data Handling with NumPy, Pandas & Matplotlib6
- Module 8 – Advanced DAX & Business Calculations11
- 7.1Fact vs Dimension table
- 7.2Star vs Snowflake schema
- 7.3Relationships, cardinality, filter direction
- 7.4Model optimization
- 7.5Row vs Filter context
- 7.6Calculated Columns, Measures, Tables
- 7.7Iterators: SUMX, AVERAGEX
- 7.8Filter functions: CALCULATE, ALL, FILTER
- 7.9Relationship functions: RELATED, USERELATIONSHIP
- 7.10Time Intelligence: YTD, QTD, MTD, PY, PM
- 7.11KPI measures & ranking metrics
- Module 9 – Power BI Visuals, Dashboards & Storytelling6
- Module 10 – Power BI Service, Deployment & Security7
1.2 Power BI Connectivity Modes: Import, Direct Query & Live Connections
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