Meet Your Trainer & Mentor
Hi, my name is Adewale Yusuf.
Nine years ago, I learned Data Analytics — and it completely changed the trajectory of my career and my life. If you had told that boy from Ibadan, Nigeria, that he would one day become an Analytics Associate Manager at Accenture, Dublin, and the Founder of Chelify Group, he would have laughed. But I did it — and now I’m showing others how to do the same.
I am a 5-time Microsoft Most Valuable Professional, have over 9 years of experience in Business Intelligence, and a corporate trainer who has trained over 15,000 people across industries.
You’ve seen people build global careers, earn in dollars, pounds, and naira, and you’ve probably asked yourself:
“When will it be my turn?”
“How did they even do it?”
This training is not just about technical skills. I will personally mentor you and share the exact strategy I used to grow — including:
I can’t wait to see you in class starting March 2025. P.S. Only 20 people will be admitted into this cohort.
Training Fee
Fee: €250
Early Bird: €200 (ends February 15)Amount in Naira: N330,000
Amount in USD: 235
Only 20 people will be admitted into the March cohort.
Delivery Format:
Self-paced learning modules
Live virtual sessions (weekends)
Start Date: March 2025
Venue: Online
What you’ll learn:
Power BI Dashboards & DAX
1.What is Power BI? Use cases & architecture
– Power BI Desktop, Service, Mobile overview
– Power BI workflow (Data → Model → Visualize → Share)
– Installing & navigating Power BI Desktop
2. Data Preparation and Modeling for Dashboards
– Connecting to data sources
– Data cleaning and shaping (Power Query overview)
– Star schema and data modeling best practices
– Creating and managing relationships
3. DAX Fundamentals
– Understanding DAX syntax and concepts
– Measures vs calculated columns
– Basic DAX functions: SUM, COUNT, AVERAGE IF, SWITCH
– Understanding row context and filter context
4. Core DAX Functions for Analytics
– CALCULATE and filter manipulation
– Time intelligence functions
– TOTALYTD, SAMEPERIODLASTYEAR, DATEADD
– Working with filters ALL, ALLEXCEPT, FILTER
– Handling blanks and errors
5. Advanced DAX Concepts
– Iterators (SUMX, AVERAGEX, COUNTX)
– Variables (VAR, RETURN)
– Ranking and segmentation
– What-If parameters
– Optimizing DAX performance
6. Dashboard Design and Visualization
– Selecting appropriate visuals
– KPI, cards, tables, and charts
– Using slicers and filters effectively
– Formatting and layout best practices
– Color themes and accessibility
7. Interactive Dashboard Features
– Drill-down and drill-through
– Tooltips and report page tooltips
– Bookmarks and navigation
– Dynamic titles using DAX
8. Power BI Dashboards in the Service
– Publishing reports
– Creating dashboards in Power BI Service
– Pinning visuals and live tiles
– Data refresh and scheduling
9. Security and Performance
– Row-Level Security (RLS) with DAX
– Performance optimization techniques
– Reducing visual and model complexity
10. Hands-On Project
– Building an end-to-end Power BI dashboard
– Writing DAX measures for KPIs
– Applying interactivity and navigation
– Dashboard presentation and review
11. Best Practices and Wrap-Up
– Dashboard design best practices
– Common DAX mistakes and how to avoid them
Review and Q&A
– Next steps and learning resources
SQL for Data Analysis
1. Introduction to SQL and Data Analysis
– What is SQL and why it’s used for data analysis
– Overview of relational databases
– SQL vs Excel / BI tools
– Understanding tables, rows, and columns
2. Getting Started with SQL
– SQL syntax and query structure
– SELECT statement basics
– Filtering data with WHERE
– Sorting results using ORDER BY
– Limiting results (LIMIT / TOP)
3. Working with Data
– Handling NULL values
– Using aliases
– Data types and conversions
– Basic string, numeric, and date functions
4. Aggregations and Grouping
– Aggregate functions (COUNT, SUM, AVG, MIN, MAX)
– GROUP BY clause
– HAVING vs WHERE
– Common aggregation use cases
5. Joining Tables
– Understanding table relationships
– INNER JOIN, LEFT, RIGHT, and FULL JOIN
– Self joins
– Best practices for joins
6. Subqueries and Common Table Expressions (CTEs)
– Writing subqueries
– Correlated subqueries
– Introduction to CTEs (WITH clause)
– Improving query readability and performance
7. Window Functions (Analytical Functions)
– Overview of window functions
– ROW_NUMBER, RANK, DENSE_RANK
– Running totals and moving averages
– PARTITION BY and ORDER BY
8. Data Analysis Use Cases
– Sales and revenue analysis
– Customer and product analysis
– Time-based analysis
– KPI calculations
9. Data Cleaning and Transformation in SQL
– Removing duplicates
– Handling missing or inconsistent data
– CASE statements
– Data standardization techniques
10. Performance Optimization Basics
– Indexes and query performance
– Writing efficient queries
– Avoiding common SQL pitfalls
11. SQL for Reporting and BI Tools
– Preparing datasets for dashboards
– SQL views
– Integrating SQL with Power BI
12. Hands-On Exercises / Mini Project
– Solving real-world data analysis problems
– Writing end-to-end analytical queries
– Query review and optimization
13. Best Practices and Wrap-Up
– SQL coding standards
– Common mistakes to avoid
Review and Q&A
– Next steps for advanced SQL
Analytics Engineering with Microsoft Fabric
1. Introduction to Microsoft Fabric
– Overview of Microsoft Fabric
– Fabric vs traditional data platforms
– OneLake concept and unified analytics
– Fabric personas: Data Engineer, Data Analyst, Data Scientist
2. Microsoft Fabric Architecture
– OneLake and data storage concepts
– Workspaces and capacities
– Integration with Power BI and Azure
– Security and governance overview
3. Data Ingestion and Integration
– Data ingestion methods in Fabric
– Using Data Factory pipelines
– Connecting to multiple data sources
– Batch vs real-time ingestion
4. Data Engineering with Fabric
– Lakehouse architecture
– Working with Delta tables
– Data transformation using Dataflows Gen2
– Introduction to Spark and notebooks
– Data engineering best practices
5. Data Warehousing in Microsoft Fabric
– Fabric Data Warehouse concepts
– Loading and managing data
– SQL analytics in Fabric
– Comparing Lakehouse vs Warehouse
6. Analytics and Reporting with Power BI in Fabric
– Power BI integration with Fabric
– Semantic models and datasets
– Building reports directly on OneLake data
– Direct Lake mode overview
7. Data Analysis and Exploration
– Using SQL, notebooks, and Power BI for analysis
– Ad-hoc data exploration
– Time-series and trend analysis
– Business analytics use cases
8. Real-Time Analytics
– Introduction to Real-Time Analytics in Fabric
– Event streams and KQL databases
– Streaming data ingestion
– Building real-time dashboards
9. Data Science and Advanced Analytics
– Machine learning in Microsoft Fabric
– Using notebooks for data science
– Model training and evaluation
– Integrating ML results into Power BI
10. Security, Governance, and Monitoring
– Data access control and permissions
– Sensitivity labels and data protection
– Monitoring Fabric workloads
– Cost and capacity management
11. Performance Optimization and Best Practices
– Optimizing Lakehouse and Warehouse performance
– Query optimization techniques
– Best practices for scalable analytics
12. Hands-On Project / Capstone
– End-to-end analytics solution using Microsoft Fabric
– Data ingestion → transformation → analytics → reporting
– Project review and discussion
13. Wrap-Up and Next Steps
– Common implementation challenges
– Learning paths and certification guidance
– Q&A session