Welcome to our Skills Builder Series.

Practical Projects to Boost Your Data Analysis Expertise
Hands-on, step-by-step projects designed to help you enhance your Power BI and data analysis skills, turning knowledge into real-world expertise.

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Why Join Our Skills Builder Series?

Join hundreds of learners improving their data analysis skills with structured, step-by-step guides designed for real-world success. Our hands-on projects help you apply Power BI concepts in practical scenarios, ensuring you gain the confidence and expertise needed to turn data into impactful insights.

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What You’ll Gain from Our Skills Builder Series

Our structured, step-by-step guides help you build real-world Power BI and data analysis expertise. Here’s what you’ll gain along the way

Learn by Doing

Apply data analysis concepts with real projects, reinforcing knowledge through hands-on experience.

Structured Step-by-Step Guidance

Follow expert-led walkthroughs that simplify complex topics, making learning intuitive and effective.

Practical Business Scenarios

Work with datasets and challenges that mirror real-world business needs, preparing you for actual projects.

Community & Expert Support

Engage with fellow learners and industry professionals to exchange insights and refine your skills.

Skills Builder Series 1

From Flat Table to Star Schema

The Scenario

Your company’s campaign data is poised for rapid growth, and designing a scalable data model is critical for efficient reporting. While the current dataset contains three months of performance metrics, your task is to transform the data into a star schema in Power BI, ensuring it can handle increasing volumes while maintaining optimal performance and usability.

The Criteria

Prepare the provided dataset by loading, cleaning, and structuring the data into a star schema. This includes creating dimension tables with surrogate keys and linking them to a centralized fact table containing measurable metrics.

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Deliverable 1

Import the dataset into Power Query and conduct an initial exploration. Confirm that all columns are loaded correctly, assess the data types, and review the structure. Identify attributes that will form dimension tables (e.g., date) and metrics that should remain in the fact table (e.g., clicks).

Deliverable 2

Generate clean dimension tables by extracting unique records for the attributes identified in Deliverable 1. Add surrogate keys to each table and prepare them for integration into the star schema.

Deliverable 3

Integrate the dimension tables into the fact table by merging queries on shared attributes. Assign the corresponding surrogate keys from each dimension to the fact table. Ensure the fact table retains only the necessary metrics and foreign key references for scalability.

Deliverable 4

Load the dimension and fact tables into the data model and establish the appropriate relationships. Create a date dimension and validate the schema by running sample queries to confirm accurate relationships and consistent data.

Deliverable 1

Use Power Query to thoroughly examine your dataset structure by leveraging Table.Schema to validate column names and data types. Additionally, profile your data with Table.Profile to identify potential issues such as missing values or outliers and inform your binning and transformation strategies.

Deliverable 2

In Power Query, create a custom column categorizing customer interactions based on their conversion targets—such as "Pre-sales," "Sales," and "Retaining Customer." Alternatively, replicate this grouping directly within the Power BI report view to adjust or enhance grouping logic as needed quickly.

Deliverable 3

Classify country data into meaningful regional groups, such as North America, Central America, Europe, and others. Consider leveraging tools such as ChatGPT to quickly identify accurate and comprehensive regional groupings—saving time and ensuring consistency, especially when working with extensive lists of countries.

Deliverable 4

Replace hard-coded zeros with nulls in Power Query for cleaner visualizations and fewer misleading blanks. Validate bins, groups, and sorting logic in Power BI visuals to ensure accuracy and usability.

Skills Builder Series 2

Data Exploration

The Scenario

In the previous installment of the Skills Builder series, you created a data model from your company's email campaign data. Now, in this second installment, you'll expand upon that foundation by leveraging Power Query and DAX for advanced data exploration, binning, and sorting.

The Criteria

Prepare and enhance your existing data model by using Power Query and DAX to explore, group, bin, and structure your data. These strategies will support more insightful visualizations and scalable reporting.

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