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Power BI Course Syllabus

Install Jenkins and configure Git repository. Then create a job with build triggers and define build steps. Configure post-build actions like deployment using shell scripts or plugins.

Section 1: SQL Introduction

  • Introduction to SQL (Structured Query Language)
  • Advantages of SQL
  • Database
  • Tables
  • SQL Data Types
    • Numeric Types
    • String Types
    • Date & Boolean
  • SQL Commands
    • DDL (Data Definition Language)
    • DML (Data Manipulation Language)
    • DCL (Data Control Language)
    • TCL (Transaction Control Language)
    • DQL (Data Query Language)
  • Data Definition Language (DDL)
    • CREATE
    • ALTER
    • DROP
    • TRUNCATE
  • Data Manipulation Language (DML)
    • INSERT
    • UPDATE
    • DELETE
  • Data Query Language (DQL)
    • SELECT
  • SQL Operator
  • SQL Clauses
    • GROUP BY
    • HAVING
    • ORDER BY
  • SQL JOINS
    • INNER JOIN
    • LEFT JOIN
    • RIGHT JOIN
    • FULL JOIN
  • SQL Keys
    • Primary Key
    • Foreign Key
  • Tables Relations
    • One-to-One
    • One-to-Many
    • Many-to-Many

Section 2: Power BI Fundamentals

  • Overview of Power BI
  • Power BI Components
  • Understanding the Power BI Workflow
  • Installing Power BI Desktop
  • What is Power BI Interface

Section 3: Data Loading

  • Connecting to Data Sources
  • Importing Data from Various Sources – Excel, CSV, PDF, SQL Server, Azure, JSON, Folders, and Web
  • Understanding Data Connectivity Modes – Import Mode
  • Understanding Data Connectivity Modes – Direct Query Mode

Section 4: Data Transformation

  • Data Transformation
  • Data Transformation
  • Power Query Editor
  • Creating Custom Columns in Power Query
  • Managing and Splitting Columns
  • Reducing Rows in Power Query
  • Applied Steps and Error Handling
  • Transforming Unstructured Data
  • Exploring Transform Menu Options
  • Pivoting Data in Power Query
  • Unpivoting Columns
  • Transforming Text, Numbers, Dates, and Times
  • Filtering and Sorting Data in Power Query
  • Grouping Data in Power Query
  • Merging Queries in Power Query
  • Appending Queries in Power Query
  • Removing Duplicate Rows in Power Query
  • Creating Conditional Columns and Custom Logic
  • Using Group By for Data Aggregation
  • Extracting Data from JSON and XML Sources
  • Applying Date and Time Functions in Power Query
  • Creating and Using Query Parameters
  • Data Profiling and Quality Control in Power Query
  • Transforming Data from Web Sources

Section 5: Data Modeling

  • What is Data Modeling and Why is it Important?
  • Key Concepts in Data Modeling – Tables, Relationships, Measures, Columns, and Schema Types
  • Understanding Entities – Dimension Tables & Fact Tables
  • Exploring Data Relationships
  • Creating Relationships (Cardinality) in Power BI with Real-World Examples
  • Establishing a One-to-One Relationship
  • Establishing a One-to-Many Relationship
  • Establishing a Many-to-Many Relationship
  • Cross Filter Direction
  • Best Practices for Managing Relationships
  • Data Models
  • Gaining insight into Flat or Denormalized Structures with a real-life example
  • Exploring the Star Schema with an actual use case
  • Understanding the Snowflake Schema with a real-world illustration
  • Normalization & Denormalization
  • Understanding How to Normalize Real-Time Data

Section 6: DAX – Data Analysis Expressions

  • 6.1: Introduction to DAX (Data Analysis Expressions)
    • Overview of DAX and its role in Power BI, Power Pivot
    • Purpose of DAX for creating custom calculations, aggregations, and enhancing data models.
  • 6.2: DAX Syntax and Functions
    • Structure and syntax of DAX formulas.
    • Commonly used DAX functions and operators.
  • 6.3: Creating Calculated Columns and Measures
    • Differences between Calculated Columns and Measures.
    • How to create Calculated Columns and Measures in Power BI.
  • 6.4: Performing Basic Calculations with DAX
    • Basic arithmetic operations and common aggregation functions: SUM, AVERAGE, MIN, MAX.
    • Calculating totals and averages using DAX.
  • 6.5: Measures vs. Calculated Columns: Key Differences
    • Static calculations in Calculated Columns vs. dynamic, context-based calculations in Measures.
    • When to use one over the other.
  • 6.6: Aggregation Functions in DAX
    • Key aggregation functions: SUM, COUNT, AVERAGE, DISTINCTCOUNT, COUNTROWS.
    • Aggregating data at different levels of detail.
  • 6.7: Logical Functions in DAX
    • Conditional logic with IF, SWITCH.
    • Complex logical expressions and handling multiple conditions.
  • 6.8: Time Intelligence in DAX
    • Year-over-Year (YoY) comparisons, running totals, and other date-based calculations.
    • Time Intelligence functions: SAMEPERIODLASTYEAR, TOTALYTD, DATESYTD, etc.
  • 6.9: Advanced DAX Functions
    • CALCULATE, FILTER, and ALL functions for modifying filter context and performing advanced calculations.
    • Using these functions for complex scenarios and calculations.
  • 6: 10: Context in DAX
    • Row Context: Row-wise calculations and iteration.
    • Filter Context: Impact of filters applied in reports on DAX calculations.
  • 6.11: Iterators in DAX
    • functions like SUMX, AVERAGEX, MINX, MAXX for row-wise calculations
    • Using iterators to perform calculations across tables.

Section 7: Visualizations and Insights

  • Overview of Data Visualization
  • Types of Visuals
  • Bar Charts: Comparing data across different categories.
  • Line Charts: Displaying trends over time.
  • Pie Charts: Representing proportions or percentages of a whole.
  • Column Charts: Displaying data comparisons across categories (vertical bars).
  • Scatter Plots: Showing relationships or correlations between two variables.
  • How Power BI Handles Data Visuals
  • Formatting Visuals
  • On-object Interaction: Using interactive elements like filters and slicers.
  • Font and Font Size: Customizing text appearance in visuals for better readability.
  • Colors and Stylistic Options: Personalizing visuals through color schemes, themes, and styles to enhance user experience.
  • Scatter Charts and Bubble Charts
  • Customizing Visuals
  • Advanced Filtering
  • Hierarchies, Drill-Downs, and Conditional Formatting
  • Matrices and Bar Charts
  • Tree Maps and Funnel Charts
  • Maps and Geo-Data Visualizations
  • Key Performance Indicator (KPI) Dashboard
  • Drill Through and Drill-Down Visualizations
  • Time-Based Visualization
  • AI Visuals in Power BI – Decomposition Tree, Key Influencers Visual & Q&A Visual

Section 8: Interactive Dashboards

  • Designing Dashboards
  • Creating Interactive Dashboards
  • Enhancing User Experience
  • To design for Mobile Devices
  • Incorporating Visual Interactions – Sync Slicers, Selection Controls

Section 9: Power BI Service – Publishing and Sharing Reports

  • To publish Power BI Service
  • Steps to Publish Reports
  • Understanding Workspaces and Apps
  • Overview of Sharing and Collaborating
  • Sharing Reports and Dashboards
  • Collaboration Features in Power BI Service
  • Exporting Reports
  • Embedding Reports
  • Exporting Reports to PDF
  • Exporting Reports to Excel
  • Embedding Power BI Reports in Applications

Section 10: Row-Level Security – RLS

  • Introduction to Data Security
  • To Implement Row-Level Security in Reports
  • What is Role-Based Access Control – RBAC

Section 11: Real-World Applications

  • Industry Use Case
  • To Create Dashboards for Business Insights
  • Create a Real time Sales Dashboard
  • Capstone Project
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