Get introduced to the fundamentals of data analysis, essential analysis tools, and how AI can help you work with data faster and more efficiently.

Data Cleaning & Preparation

What you’ll learn:

1. Remove duplicate records and handle missing data.
2. Standardize dates, numbers, text, and other data formats.
3. Identify and correct errors and inconsistencies.
4. Split, merge, and organize columns for analysis.
5. Use AI-assisted techniques to identify and clean common data issues.

Text Functions & Data Transformation

What you’ll learn:

1. Use essential text functions such as TRIM, LEFT, RIGHT, SUBSTITUTE, and more.
2. Clean and transform text-based data for analysis.
3. Apply text functions to real-world datasets.
4. Use AI to assist with data transformation and formula generation.

Essential Data Analysis Functions

What you’ll learn:

1. Master key functions including SUM, AVERAGE, MIN, MAX, and more.
2. Perform basic calculations and summarize datasets.
3. Apply functions to practical business datasets.
4. Use AI to generate, explain, and optimize data analysis formulas.

Background

Netflix relies on accurate, well-structured data to understand content performance, improve recommendations, and deliver better experiences to its users. In this project, you’ll work with a Netflix content dataset containing information such as titles, genres, release dates, ratings, countries, and other content-related metrics.

You’ll apply data cleaning techniques while using AI tools to identify patterns, detect inconsistencies, generate formulas, and support data preparation.

Task Overview

As a data analyst supporting Netflix, your task is to clean, validate, and prepare the dataset for analysis. You’ll identify data quality issues, correct inconsistencies, and use AI-assisted techniques to make the cleaning process more efficient.

Your Responsibilities

1. Identify columns containing missing or incomplete data.
2. Determine the most appropriate method for handling missing values, including removal, replacement, or AI-assisted suggestions.
3. Detect and remove duplicate records to maintain data accuracy.
4. Identify and correct inconsistent data formats, including dates, ratings, and numerical values.
5. Standardize categorical data such as genres, countries, content types, and ratings.
6. Check numerical fields for invalid or unusual values and ensure they fall within reasonable ranges.
7. Validate dates and other time-based information for logical inconsistencies.
8. Use AI tools to assist with data cleaning, formula generation, error detection, and interpretation of data quality issues.
9. Document the cleaning steps taken and explain how each change improves the quality of the dataset.

Basic Logical Functions

What you’ll learn:

• Understand the IF function and apply conditional logic to datasets.
• Create multiple conditions using nested IF functions.
• Use the IFS function to simplify complex conditional analysis.
• Use AI to generate, explain, troubleshoot, and improve logical formulas.

Advanced Logical & Conditional Functions

What you’ll learn:

• Apply SUMIF, COUNTIF, and AVERAGEIF to analyze data based on specific conditions.
• Work with multiple criteria using SUMIFS, COUNTIFS, and AVERAGEIFS.
• Analyze and summarize business data using conditional calculations.
• Use AI to help create complex formulas and identify the right function for different analytical tasks.
• Apply logical and conditional functions to real-world datasets and extract meaningful insights.

Background

A global retail company wants to improve its marketing performance, increase sales, and better understand customer behavior. The company has collected data from marketing campaigns throughout the year, including customer information, purchase history, campaign details, discounts, regions, and sales channels.

As a data analyst, you’ll use logical and conditional functions alongside AI-assisted analysis to identify customer segments, evaluate campaign performance, and uncover patterns that can support better marketing decisions.

Your Responsibilities

1. Segment customers based on criteria such as age group, purchase value, loyalty membership, and engagement level.
2. Identify high-value and highly engaged customers using conditional logic.
3. Compare marketing campaigns using total sales, average purchase value, and transaction volume.
4. Analyze campaign performance based on region, discounts, customer type, and sales channels.
5. Identify patterns in sales data, including popular product categories, purchasing periods, and regional preferences.
6. Compare the purchasing behavior and engagement of loyalty program members with non-members.
7. Use AI to help identify patterns, generate analytical formulas, and interpret customer and campaign data.
8. Use your findings to develop data-driven recommendations for improving customer targeting, promotional strategies, and campaign performance.

What You’ll Learn

• Master VLOOKUP to retrieve and connect information across datasets.
• Apply VLOOKUP to real-world data using practical exercises.
• Combine VLOOKUP with other functions to perform more advanced analysis.
• Explore powerful alternatives such as INDEX and MATCH for flexible data retrieval.
• Combine INDEX + MATCH to efficiently analyze and connect datasets.
• Understand when to use different lookup functions based on the structure of your data.
• Use AI tools to generate, explain, troubleshoot, and optimize lookup formulas.
• Apply lookup functions and AI-assisted techniques to consolidate data and uncover useful business insights.

Background

A retail company stores its sales, customer, and product information in separate datasets. The company wants to combine these datasets to create a more complete view of its sales performance and understand which products, customers, and regions are driving revenue.

As a data analyst, you’ll use lookup functions and AI-assisted techniques to connect multiple datasets, build a consolidated sales report, and uncover valuable business insights.

Your Responsibilities

1. Use VLOOKUP, INDEX, and MATCH to connect information across sales, customer, and product datasets.
2. Build a consolidated dataset containing customer names, products, categories, regions, loyalty status, and transaction details.
3. Use lookup functions to categorize and organize sales by product type and category.
4. Segment sales data by customer region, loyalty membership, and other relevant attributes.
5. Analyze revenue contributions across regions and compare purchasing patterns between loyalty members and non-members.
6. Use lookup functions to connect products with supplier information and analyze supplier-related data.
7. Calculate key sales metrics, including total revenue, total units sold, average order value, and revenue per customer.
8. Use AI to assist with formula creation, data interpretation, anomaly detection, and generating analytical questions from the dataset.
9. Present your findings and develop data-driven recommendations for customer targeting, inventory planning, product performance, and supplier management.

What You’ll Learn:

• Design effective dashboard layouts and organize data for clear visualization.
• Use PivotTables, Pivot Charts, and Slicers to explore and interact with data.
• Create and present Key Performance Indicators (KPIs) and important business metrics.
• Use AI tools to assist with identifying insights, selecting suitable visualizations, and improving dashboard presentation.

Presentation:

• Work in teams to prepare and deliver a data analysis presentation.
• Practice presenting findings clearly and confidently.
• Gather feedback from peers and instructors.
• Refine the presentation based on feedback and improve the clarity of your insights.

Background:

A leading bike retailer wants to improve its business strategy by gaining deeper insights from its sales data. The company aims to understand sales trends, identify key customer segments, and improve inventory planning.

As a data analyst, your task is to analyze the bike sales dataset, build an interactive dashboard, and use AI-assisted analysis to generate meaningful insights and data-driven recommendations for management.

Your Responsibilities:

1. Analyze sales trends over time, including peak sales periods and seasonal patterns.
2. Evaluate the performance of different bike types based on sales volume and revenue generated.
3. Segment customers by demographics such as age, gender, and region to understand purchasing behavior.
4. Create an interactive dashboard that clearly presents the key findings from your analysis.
5. Include important KPIs such as total sales, average order value, top-selling bike types, customer demographics, and regional sales performance.
6. Add interactive filters that allow users to explore specific time periods, regions, and customer segments.
7. Use appropriate visualizations, including bar charts, line graphs, pie charts, and heat maps, to communicate findings effectively.
8. Use AI tools to assist with identifying trends, interpreting patterns, and generating insights from the analyzed data.
9. Based on the analysis, provide data-driven recommendations on bike types that could be prioritized for marketing and inventory planning.

Data Collection & Importation

What You’ll Learn:

• Identify and work with different data sources.
• Import data into Power BI from sources such as Excel, CSV files, and databases.
• Understand how different data sources can be combined for analysis.
• Use AI-assisted features to support data exploration and preparation.

Data Cleaning & Transformation

What You’ll Learn:

• Use Power Query to clean and transform datasets.
• Apply appropriate data types to ensure accurate analysis.
• Identify and correct common data quality issues during the transformation process.

Data Modeling

What You’ll Learn:

• Create relationships between different tables.
• Understand and define primary keys and foreign keys.
• Organize data into a structured model for effective analysis and reporting.

Background:

Choprite, a retail chain with multiple locations, has accumulated a large volume of sales data over the past year. However, the dataset contains missing values, duplicates, inconsistencies, and other data quality issues.

Management wants to use this data to understand sales performance, identify trends, and make informed business decisions. As a data analyst, your task is to clean, transform, model, and analyze the sales data using Power BI, with AI-assisted techniques where appropriate.

Your Responsiblities:

1. Identify missing values and determine appropriate ways to handle them, such as using mean/median values, filling missing records, or removing severely incomplete data.
2. Identify and remove duplicate records to ensure each transaction is unique.
3. Validate that the Total Sale Amount matches the expected calculation based on Quantity Sold and Unit Price, and investigate discrepancies.
4. Group related products into broader categories where necessary and create a summary of sales performance by category.
5. Analyze sales trends over time, including peak sales periods, seasonal patterns, and performance across different store locations.
6. Analyze customer purchasing behavior, identify high-value customers, and examine how discounts affect sales.
7. Use Power BI visualizations and AI-assisted analysis to identify patterns, trends, and useful insights within the dataset.
8. Recommend products or categories that should be prioritized based on sales performance and customer demand.

Data Visualization

Activities:

• Create basic visualizations such as bar charts, line charts, pie charts, and more.
• Use visualizations to identify data distributions, patterns, and trends.
• Select appropriate visualizations based on the type of data and analysis.

DAX Formulas

Activities:

• Create summary statistics using DAX measures.
• Use DAX to create calculated columns and more advanced measures.
• Apply the CALCULATE function to perform more advanced data analysis.
• Use AI tools to assist with creating, explaining, and troubleshooting DAX formulas.

Dashboard Creation

Activities:

• Build interactive Power BI dashboards.
• Create and organize key visualizations to communicate important insights.
• Add interactivity using slicers, filters, and other dashboard features.
• Use AI-assisted insights to support data exploration and identify meaningful patterns.

Background:

A retail analytics firm has collected a comprehensive dataset from a large online retailer to understand customer shopping behavior and emerging purchasing trends. The firm wants to use these insights to improve marketing strategies, product offerings, and customer experience.

As a data analyst, your task is to analyze the shopping trends dataset using Power BI and AI-assisted analysis, uncover meaningful patterns, and provide data-driven recommendations.

Your Responsibilities

1. Analyze customer demographics and segment shoppers by age, gender, income level, and region. Identify valuable customer segments based on purchase frequency and total spending.
2. Determine which product categories are most popular among different customer segments and identify emerging product preferences.
3. Analyze purchase timing to identify peak shopping days, times, and seasonal trends.
4. Analyze device usage across mobile, desktop, and tablet, and compare purchasing behavior across different devices.
5. Analyze payment method preferences across customer segments and identify notable patterns across different periods.
6. Identify products that are frequently purchased together and explore potential cross-selling and product bundling opportunities.
7. Analyze product affinities across customer segments to determine which products are commonly purchased by similar groups of customers.
8. Use AI-assisted analysis to help identify patterns, generate insights, and explore potential explanations for observed shopping behaviors.
9. Create interactive Power BI visualizations and dashboards that highlight important shopping trends and allow stakeholders to explore the data.
10. Provide data-driven recommendations for targeted marketing campaigns based on customer segments, device usage, product preferences, and purchasing patterns.

Generative AI is transforming how businesses and industries work with data. In this unit, you’ll learn how to integrate Generative AI tools into data analytics workflows to improve data quality, accelerate analysis, uncover valuable insights, automate repetitive tasks, and support more informed, data-driven decision-making.

Build practical experience through 20+ hands-on Data Analysis and AI projects, including multiple mini-projects and 3 comprehensive Capstone Projects.

Work with real-world datasets, apply industry-relevant analytical techniques, use AI tools to enhance your workflow, and build a project portfolio that demonstrates your skills and prepares you for opportunities in Data Analysis

Prepare for your next Data Analysis career opportunity with practical guidance for technical and HR interviews. You’ll learn how to create a professional, results-focused CV, optimize your LinkedIn profile, showcase your projects effectively, and confidently present your Data Analysis + AI skills to potential employers.

Gain valuable industry insights through an exclusive Ask-Me-Anything (AMA) session with an experienced Data Analyst. Get practical guidance, ask career and technical questions, and learn from real-world experience in the data analytics field.

Learn how to pursue remote income opportunities as a Data Analyst, even if you’re new to remote work. In this session, an experienced freelancer will share practical strategies for finding clients, building your profile, showcasing your skills, pricing your services, and securing international Data Analysis opportunities.

Learn about UK immigration pathways available to eligible Nigerian tech professionals seeking to work and build a career in the UK. This guide covers the Skilled Worker visa, including eligibility, sponsorship requirements, application steps, and key considerations for pursuing UK-based tech opportunities.

You’ll also learn about relevant UK talent and tech-focused visa pathways, their requirements, and how to prepare for the application process.

Get access to a FREE professional workspace with high-speed internet to support your learning, projects, and remote work.

Study, practice, complete your Data Analysis projects, and work online from a dedicated workspace without worrying about your internet connection.