In this module, Ali Noorani will introduce himself and sets the stage for the advanced Power BI course. He will outline the common challenges faced by users transitioning from basic to advanced Power BI, particularly those with a business rather than an IT background.
Topics include:
• Knowledge gaps and how to address them
• The scarcity of specific problem-solving content for Power BI
• The rapid pace of updates and how to keep up
• Emphasizing the importance of mastering 20% of core concepts to solve 80% of Power BI challenges

This session will cover the initial step in the Power BI data cycle: obtaining data. The module highlights the importance of starting with a clear vision and end goals for your report or dashboard.
Key topics include:
• Identifying data sources and their challenges
• Methods for importing data into Power BI
• Using Excel and CSV files
• Leveraging third-party tools and APIs
• Centralized data warehouses for streamlined data management
• Automating data imports with scheduling tools for efficient data management.

This module delves into the critical step of data transformation. Participants will learn about identifying and cleaning raw data to ensure it is useful and structured.
The session covers:
• Common data issues such as inconsistent data formats, duplicates, and missing values
• Techniques for automating data transformation
• Terms associated with data transformation, such as data wrangling and munging

In this module, the focus shifts to data consolidation. Participants will learn the importance of consolidating data from multiple sources into a coherent data model.
The session discusses:
• The drawbacks of data flattening, including data redundancy and scalability issues
• The benefits of data modeling over data flattening
• How data modeling reduces redundancy, improves scalability, and handles large datasets and perform cross-transaction data comparisons

This session explores the final steps of the data cycle: enrichment, visualization, and sharing. Participants will learn how to enhance their data with additional information and insights.
Key topics include:
• Techniques for enriching data
• Creating compelling and informative visualizations in Power BI
• Effective methods for sharing reports and dashboards with stakeholders

This module focuses on the initial steps of data transformation, specifically identifying problems and understanding column profiles.
Participants will learn techniques for detecting issues in their datasets, such as:
• Data type mismatches and inconsistencies
• Using Power BI's data profiling tools to assess data quality
• Identifying and addressing common data problems

In this session, participants delve into the concept of fuzzy matching.
The module explains how fuzzy matching can be used to identify and correct inconsistencies in data entries, such as:
• Variations in spelling or formatting
• Implementing fuzzy matching techniques in Power BI
• Effectively handling common data quality issues

This module builds on the previous session by introducing the concept of a transformation table. Participants learn how to create and use transformation tables in conjunction with fuzzy matching to automate and scale data cleaning processes.
The session covers:
• Best practices for setting up transformation tables
• Ensuring case sensitivity and accurate data mappings
• Integrating transformation tables with fuzzy matching for efficient data cleaning

In this practical session, participants will apply their knowledge of fuzzy matching and transformation tables to real-world scenarios.
The module provides hands-on practice, including:
• Creating and using transformation tables to address data quality issues
• Troubleshooting common problems
• Performing sense checks to ensure data accuracy and efficiency

This module in this series focuses on a specific case study: transforming a city dataset. Participants apply all the techniques learned in previous sessions to clean, consolidate, and transform city-related data.
The session emphasizes:
• Practical application of learned techniques
• Problem-solving in a real-world context
• Reinforcing key concepts through hands-on practice and case study analysis

In this module, participants will focus on completing the transformation of sales data.
The session covers:
• Cleaning and transforming sales data
• Handling errors and missing values
• Making executive decisions on data handling based on error ratios and business context

This session focuses on importing and cleaning product data.
Participants will learn techniques for:
• Standardizing product information
• Dealing with inconsistent data entries
• Ensuring the accuracy and reliability of product data

In this module, participants delve into data model formatting.
The session covers:
• Understanding automatic relationship detection in Power BI
• Deactivating auto-detect for manual relationship management
• Formatting and enriching data for better reporting outcomes

This session introduces the creation of a simple calendar table using DAX.
Key topics include:
• Creating a simple calendar table
• Using DAX for date-related calculations
• Enhancing reports with date intelligence

Building on the previous module, this session focuses on creating a fiscal year calendar table.
Participants will learn:
• Customizing date intelligence for fiscal reporting
• Utilizing DAX for advanced date calculations
• Enhancing reports with fiscal year insights

In this interactive session, participants will have the opportunity to recap previous sessions and address their questions and concerns.
The module includes:
• Practical tips and insights from real-world use cases
• Addressing specific challenges faced by participants
• Sharing best practices and solutions

This module will introduce the concept of fact tables in data modeling.
Participants will learn:
• Understanding fact tables and their characteristics
• The purpose of fact tables in data models
• Creating and managing fact tables in Power BI

In this module, participants will explore dimension tables and the star schema in data modeling.
The session covers:
• Understanding dimension tables and their characteristics
• Implementing the star schema in data modeling
• Best practices for organizing and managing dimension tables

This session focuses on cardinality and cross-filter direction in relationships.
Participants will learn:
• Understanding different types of cardinality in relationships
• Managing cross-filter direction for optimal data model performance
• Best practices for relationship management in Power BI

In this module, participants will explore advanced data modeling techniques, including merging tables and creating role-playing dimensions.
The session covers:
• Merging tables for optimized data models
• Creating and managing role-playing dimensions
• Advanced techniques for complex data modeling scenarios

This module covers the theoretical concepts of comparing two fact tables.
Participants will learn about:
• The importance of understanding common grains in data
• The implications of comparing data with different grains
• Strategies for managing data discrepancies and ensuring accurate comparisons

In this practical session, participants will apply their theoretical knowledge to compare two fact tables.
The module covers:
• Hands-on practice with many-to-many relationships
• Techniques for creating accurate comparisons
• Best practices for ensuring data integrity

This module focuses on comparing sales and inventory data.
Participants will learn about:
• The challenges and implications of comparing sales and inventory quantities
• Techniques for managing data discrepancies and ensuring accurate reporting
• Best practices for creating effective and insightful reports

In this session, participants recap key concepts covered in the course. The module also introduces data enrichment techniques using custom columns.
Key topics include:
• Practical examples and hands-on exercises for data enrichment
• Techniques for creating custom columns in Power BI
• Best practices for enhancing data and creating meaningful insights

This module covers the comparison of different date fields, specifically order date and ship date.
Participants will learn about:
• Techniques for comparing date fields and handling discrepancies
• Creating meaningful insights from date comparisons
• Practical examples and hands-on exercises for date field comparisons

This session introduces the concept of comparing target sales versus actual sales.
Participants will learn about:
• Setting up the data model for target vs actual sales comparison
• Creating relationships and necessary calculations
• Practical examples and hands-on exercises for setting up comparisons

Building on the previous session, this module will cover advanced techniques for comparing target sales versus actual sales.
Key topics include:
• Handling complex data models and relationships
• Best practices for ensuring accurate comparisons and reporting
• Practical examples and hands-on exercises for advanced comparisons

This module focuses on implementing role-level security in Power BI.
Participants will learn about:
• Techniques for managing user access and permissions
• Best practices for ensuring secure data models
• Practical examples and hands-on exercises for implementing role-level security

In this module, participants will learn the process of normalizing a flat file. Key topics include:
• Introduction to the concept of normalization
• Step-by-step process for creating dimension tables from a flat file
• Best practices for efficient data modeling, including:
o Removing unnecessary columns
o Creating primary keys
o Using merges to bring in relevant data
• Practical exercises to reinforce the concepts and techniques discussed

In the final session of the course, Ali will recap the entire course and have the opportunity to ask final questions. The module includes:
• A recap of key concepts covered in the course
• Addressing final questions and concerns from participants
• Providing feedback and discussing next steps for continued learning and application

The course manual serves as a comprehensive, step-by-step guide to the entire training. This material is designed to be taken home and used as a reference tool to help students navigate the course content.

Power BI Advanced Dataset
The dataset contains the following sheets:
1) Sales_Data:
This sheet contains detailed sales data, including various metrics related to sales transactions.

a. Columns in Sales Table
• Transaction Date: Date of the sales transaction.
• Retailer City: City where the retailer is located.
• Order Method Type: Method used for placing the order (e.g., E-mail).
• user: Identifier for the user.
• Urgent: Indicates if the order was marked as urgent.
• Retailer Type: Type of retailer (e.g., Outdoors Shop).
• Product Code: Code identifying the product.
• Price: Price of the product.
• Quantity Sold: Quantity of the product sold.
• Sales Revenue: Revenue generated from the sale.
• Column1: A placeholder or extra column; needs to be checked for specific data.
• User Rating: Rating given by the user, possibly related to the product or service.

b. Columns in Product Table
• Product Code: Code identifying the product.
• Product Line: Category or line of the product.
• Product Type: Type of product.
• Product: Name or description of the product.
• Product Cost: Cost of producing or procuring the product.
• List Price: Listed price for the product.

c. Columns in Countries Table
• Country: Country where the sale occurred.
• City: City where the sale occurred.

d. Columns in Retailer_City_w.r.t_Order_Method_Type Table
• Retailer City: Another instance of the retailer's city, different context.
• E-mail: Number of orders placed via email.
• Web: Number of orders placed via the web.
• Sales Visit: Number of orders placed through sales visits.
• Telephone: Number of orders placed via telephone.
• Mail: Number of orders placed via mail.

e. Columns in Berlin Table
• Transaction Date: Date of the sales transaction.
• Product Code: Code identifying the product.
• Retailer City: City where the retailer is located.

f. Columns in Chicago Table
• Transaction Date: Date of the sales transaction.
• Retailer Type: Type of retailer (e.g., Outdoors Shop).
• Product Code: Code identifying the product.
• Retailer City: City where the retailer is located.

g. Columns in Sales_by_Country Table
• Product Line: Category or line of the product.
• Country: Country where the sale occurred.
• Sales: Total sales amount.
• Year: Year of the transaction.

h. Columns in Goal Table
• Year: Year of the transaction.
• Month: Month of the transaction.
• Product line: Category or line of the product.
• Target: Sales target for the product.

Download Exercise Files from the about link.