This Data Analysis course is designed to help students, business professionals, and analysts gain the skills needed to make data-driven decisions. Learners will master the entire data analysis pipeline � from data collection and cleaning to analysis, visualization, and interpretation � using industry-standard tools.

The course is beginner-friendly, project-based, and focuses on real-world datasets to build confidence and prepare learners for careers in business analysis, research, reporting, or freelancing.

Module 1: Introduction to Data Analysis

What is Data Analysis?

Types of Data & Data Sources

The Data Analysis Lifecycle

Tools for Data Analysis: Excel, SQL, Power BI

Understanding KPIs, Metrics, and Dashboards

Module 2: Microsoft Excel for Data Analysis

Excel Interface & Essential Functions

Data Cleaning Techniques

Working with Tables, Filters, and Conditional Formatting

Lookup Functions (VLOOKUP, INDEX/MATCH, XLOOKUP)

Pivot Tables & Pivot Charts

Descriptive Statistics in Excel

Real-World Project: Analyzing Sales or Survey Data

Module 3: Power BI � Data Visualization & Dashboards

Introduction to Power BI Interface

Connecting & Importing Data

Data Modeling and Relationships

DAX Basics (Calculated Columns, Measures)

Creating Reports and Interactive Dashboards

Publishing and Sharing Dashboards

Real-World Project: Interactive Sales or HR Dashboard

Module 4: SQL for Data Retrieval

Introduction to Relational Databases

Basic SQL Syntax: SELECT, WHERE, ORDER BY

Filtering and Aggregating Data (GROUP BY, COUNT, SUM)

JOINS (INNER, LEFT, RIGHT, FULL)

Subqueries and Aliases

Real-World Project: Querying a Business Database

Module 5: Data Cleaning & Preparation Techniques

Identifying and Handling Missing Data

Dealing with Outliers and Duplicates

Data Transformation and Normalization

Date, Time, and Text Processing

Combining Data from Multiple Sources

Module 6: Interpreting and Communicating Insights

Data Storytelling Principles

Charts Selection (When to use what)

Creating Insightful Reports

Making Recommendations from Data

Presenting Data Analysis Projects

Module 7: Capstone Project

Choose a Dataset (Business, Healthcare, Education, etc.)

Apply Excel, Power BI, and SQL for full analysis

Create a professional dashboard

Write an executive summary of insights

Present and Defend Your Work

? Additional Features:

Real-world datasets for practice

Assignments and quizzes for each module

Peer review and feedback on final project

Certificate of Completion

Tips for getting freelance or remote jobs in data

Amoo Sunday

Data Analyst

Course Preview

Price N100000
Mode regular
Instructor Amoo Sunday
Duration 30 days
Attendance Offline (In-person) & Online (Remote)

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Data Analysis

Module 1: Introduction to Data Analysis

What is Data Analysis?

Types of Data & Data Sources

The Data Analysis Lifecycle

Tools for Data Analysis: Excel, SQL, Power BI

Understanding KPIs, Metrics, and Dashboards

Module 2: Microsoft Excel for Data Analysis

Excel Interface & Essential Functions

Data Cleaning Techniques

Working with Tables, Filters, and Conditional Formatting

Lookup Functions (VLOOKUP, INDEX/MATCH, XLOOKUP)

Pivot Tables & Pivot Charts

Descriptive Statistics in Excel

Real-World Project: Analyzing Sales or Survey Data

Module 3: Power BI � Data Visualization & Dashboards

Introduction to Power BI Interface

Connecting & Importing Data

Data Modeling and Relationships

DAX Basics (Calculated Columns, Measures)

Creating Reports and Interactive Dashboards

Publishing and Sharing Dashboards

Real-World Project: Interactive Sales or HR Dashboard

Module 4: SQL for Data Retrieval

Introduction to Relational Databases

Basic SQL Syntax: SELECT, WHERE, ORDER BY

Filtering and Aggregating Data (GROUP BY, COUNT, SUM)

JOINS (INNER, LEFT, RIGHT, FULL)

Subqueries and Aliases

Real-World Project: Querying a Business Database

Module 5: Data Cleaning & Preparation Techniques

Identifying and Handling Missing Data

Dealing with Outliers and Duplicates

Data Transformation and Normalization

Date, Time, and Text Processing

Combining Data from Multiple Sources

Module 6: Interpreting and Communicating Insights

Data Storytelling Principles

Charts Selection (When to use what)

Creating Insightful Reports

Making Recommendations from Data

Presenting Data Analysis Projects

Module 7: Capstone Project

Choose a Dataset (Business, Healthcare, Education, etc.)

Apply Excel, Power BI, and SQL for full analysis

Create a professional dashboard

Write an executive summary of insights

Present and Defend Your Work

? Additional Features:

Real-world datasets for practice

Assignments and quizzes for each module

Peer review and feedback on final project

Certificate of Completion

Tips for getting freelance or remote jobs in data

Amoo Sunday

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Data Analysis

Module 1: Introduction to Data Analysis

What is Data Analysis? Types of Data & Data Sources The Data Analysis Lifecycle Tools for Data Analysis: Excel, SQL, Power BI Understanding KPIs, Metrics, and Dashboards

Module 2: Microsoft Excel for Data Analysis

Excel Interface & Essential Functions Data Cleaning Techniques Working with Tables, Filters, and Conditional Formatting Lookup Functions (VLOOKUP, INDEX/MATCH, XLOOKUP) Pivot Tables & Pivot Charts Descriptive Statistics in Excel Real-World Project: Analyzing Sales or Survey Data

Module 3: Power BI � Data Visualization & Dashboards

Introduction to Power BI Interface Connecting & Importing Data Data Modeling and Relationships DAX Basics (Calculated Columns, Measures) Creating Reports and Interactive Dashboards Publishing and Sharing Dashboards Real-World Project: Interactive Sales or HR Dashboard

Module 4: SQL for Data Retrieval

Introduction to Relational Databases Basic SQL Syntax: SELECT, WHERE, ORDER BY Filtering and Aggregating Data (GROUP BY, COUNT, SUM) JOINS (INNER, LEFT, RIGHT, FULL) Subqueries and Aliases Real-World Project: Querying a Business Database

Module 5: Data Cleaning & Preparation Techniques

Identifying and Handling Missing Data Dealing with Outliers and Duplicates Data Transformation and Normalization Date, Time, and Text Processing Combining Data from Multiple Sources

Module 6: Interpreting and Communicating Insights

Data Storytelling Principles Charts Selection (When to use what) Creating Insightful Reports Making Recommendations from Data Presenting Data Analysis Projects

Module 7: Capstone Project

Choose a Dataset (Business, Healthcare, Education, etc.) Apply Excel, Power BI, and SQL for full analysis Create a professional dashboard Write an executive summary of insights Present and Defend Your Work

? Additional Features:

Real-world datasets for practice Assignments and quizzes for each module Peer review and feedback on final project Certificate of Completion Tips for getting freelance or remote jobs in data

Amoo Sunday

More Courses You Might Like

Web Design (Scholarship)

Data Analytics Webinar

Product Design & Development

What is Data Analysis?

Types of Data & Data Sources

The Data Analysis Lifecycle

Tools for Data Analysis: Excel, SQL, Power BI

Understanding KPIs, Metrics, and Dashboards

Excel Interface & Essential Functions

Data Cleaning Techniques

Working with Tables, Filters, and Conditional Formatting

Lookup Functions (VLOOKUP, INDEX/MATCH, XLOOKUP)

Pivot Tables & Pivot Charts

Descriptive Statistics in Excel

Real-World Project: Analyzing Sales or Survey Data

Introduction to Power BI Interface

Connecting & Importing Data

Data Modeling and Relationships

DAX Basics (Calculated Columns, Measures)

Creating Reports and Interactive Dashboards

Publishing and Sharing Dashboards

Real-World Project: Interactive Sales or HR Dashboard

Introduction to Relational Databases

Basic SQL Syntax: SELECT, WHERE, ORDER BY

Filtering and Aggregating Data (GROUP BY, COUNT, SUM)

JOINS (INNER, LEFT, RIGHT, FULL)

Subqueries and Aliases

Real-World Project: Querying a Business Database

Identifying and Handling Missing Data

Dealing with Outliers and Duplicates

Data Transformation and Normalization

Date, Time, and Text Processing

Combining Data from Multiple Sources

Data Storytelling Principles

Charts Selection (When to use what)

Creating Insightful Reports

Making Recommendations from Data

Presenting Data Analysis Projects

Choose a Dataset (Business, Healthcare, Education, etc.)

Apply Excel, Power BI, and SQL for full analysis

Create a professional dashboard

Write an executive summary of insights

Present and Defend Your Work

Real-world datasets for practice

Assignments and quizzes for each module

Peer review and feedback on final project

Certificate of Completion

Tips for getting freelance or remote jobs in data