Data
Data Analytics
Turn data into insight and decisions businesses act on. structured around current industry expectations for Data Analysts, covering the core workflow: data collection → cleaning → analysis → visualization → reporting → business decision-making.
12 weeks Beginner to Job-ready Online, live + self-paced
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Course overview
Learn to collect, clean, analyze, and visualize data to drive decisions. Covers Excel, SQL, data visualization (Power BI / Tableau), and analytics storytelling — with real datasets.
Learning outcomes
- Understand the role and responsibilities of a Data Analyst
- Collect, clean, and prepare datasets for analysis
- Perform advanced analysis using Excel and SQL
- Use Python for data manipulation and automation
- Apply statistical techniques to business problems
- Build interactive dashboards using Power BI
- Create professional data visualizations
- Generate insights and recommendations from data
- Build a portfolio-ready analytics project
- Use AI tools to improve productivity as a Data Analyst
Career opportunities
Data Analyst Business Intelligence Analyst Reporting Analyst Operations Analyst Freelance Data Analyst
Career pathway
- Start: Junior Data Analyst
- Grow: Senior Analyst / BI Analyst
- Lead: Analytics Lead / Data Scientist track
- Alt: Freelance / consultant
Course modules
1 Introduction to Data Analytics & Analyst Workflow
Introduction to Data Analytics
• What is Data Analytics?
• Difference between:
o Data Analyst
o Data Scientist
o Business Analyst
o Data Engineer
• Importance of data-driven decision making
• Types of analytics:
o Descriptive Analytics
o Diagnostic Analytics
o Predictive Analytics
o Prescriptive Analytics
• Real-world applications:
o Banking
o Healthcare
o Marketing
o E-commerce
o Finance
Data Analyst Career & Workflow
• Data Analyst responsibilities
• Data analysis lifecycle:
1. Define business problem
2. Collect data
3. Clean data
4. Analyze data
5. Visualize findings
6. Present insights
• Types of datasets:
o Structured data
o Semi-structured data
o Unstructured data
• What is Data Analytics?
• Difference between:
o Data Analyst
o Data Scientist
o Business Analyst
o Data Engineer
• Importance of data-driven decision making
• Types of analytics:
o Descriptive Analytics
o Diagnostic Analytics
o Predictive Analytics
o Prescriptive Analytics
• Real-world applications:
o Banking
o Healthcare
o Marketing
o E-commerce
o Finance
Data Analyst Career & Workflow
• Data Analyst responsibilities
• Data analysis lifecycle:
1. Define business problem
2. Collect data
3. Clean data
4. Analyze data
5. Visualize findings
6. Present insights
• Types of datasets:
o Structured data
o Semi-structured data
o Unstructured data
2 Microsoft Excel for Data Analysis
Excel Fundamentals for Analysts
• Spreadsheet structure
• Data formatting
• Sorting and filtering
• Data validation
• Removing duplicates
• Data cleaning techniques
Functions:
• SUM
• COUNT
• AVERAGE
• IF
• COUNTIF
• SUMIF
• INDEX/MATCH
• XLOOKUP
Advanced Excel Analytics
• Pivot Tables
• Pivot Charts
• Conditional formatting
• Excel dashboards
• Power Query introduction
• Data transformation
• Spreadsheet structure
• Data formatting
• Sorting and filtering
• Data validation
• Removing duplicates
• Data cleaning techniques
Functions:
• SUM
• COUNT
• AVERAGE
• IF
• COUNTIF
• SUMIF
• INDEX/MATCH
• XLOOKUP
Advanced Excel Analytics
• Pivot Tables
• Pivot Charts
• Conditional formatting
• Excel dashboards
• Power Query introduction
• Data transformation
3 Statistics for Data Analysis
Statistics Fundamentals
• Importance of statistics in analytics
• Population vs Sample
• Measures of central tendency:
o Mean
o Median
o Mode
• Measures of dispersion:
o Range
o Variance
o Standard deviation
Statistical Analysis & Business Insights
• Probability basics
• Correlation
• Regression introduction
• Hypothesis testing
• A/B testing concepts
• Understanding business metrics
• Importance of statistics in analytics
• Population vs Sample
• Measures of central tendency:
o Mean
o Median
o Mode
• Measures of dispersion:
o Range
o Variance
o Standard deviation
Statistical Analysis & Business Insights
• Probability basics
• Correlation
• Regression introduction
• Hypothesis testing
• A/B testing concepts
• Understanding business metrics
4 SQL Database Fundamentals
Introduction to SQL
• Understanding databases
• Relational database concepts
• Tables, rows, columns
• Primary keys
• Foreign keys
• Database relationships
SQL Commands:
• CREATE
• SELECT
• INSERT
• UPDATE
• DELETE
SQL Data Querying
• Filtering data
• Sorting records
• Aggregate functions:
o COUNT()
o SUM()
o AVG()
o MIN()
o MAX()
• GROUP BY
• HAVING
• Understanding databases
• Relational database concepts
• Tables, rows, columns
• Primary keys
• Foreign keys
• Database relationships
SQL Commands:
• CREATE
• SELECT
• INSERT
• UPDATE
• DELETE
SQL Data Querying
• Filtering data
• Sorting records
• Aggregate functions:
o COUNT()
o SUM()
o AVG()
o MIN()
o MAX()
• GROUP BY
• HAVING
5 Advanced SQL for Analysts
SQL Joins & Data Relationships
• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• FULL JOIN
• Multiple table relationships
Advanced SQL Queries
• Subqueries
• Common Table Expressions (CTEs)
• CASE statements
• Window functions:
o ROW_NUMBER()
o RANK()
o LEAD()
o LAG()
• Query optimization basics
• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• FULL JOIN
• Multiple table relationships
Advanced SQL Queries
• Subqueries
• Common Table Expressions (CTEs)
• CASE statements
• Window functions:
o ROW_NUMBER()
o RANK()
o LEAD()
o LAG()
• Query optimization basics
6 Python Programming for Data Analysis
Python Fundamentals
• Installing Python environment
• Jupyter Notebook
• Variables
• Data types
• Operators
• Conditions
• Loops
• Functions
Python Libraries for Analytics
Introduction to:
NumPy
• Arrays
• Mathematical operations
Pandas
• DataFrames
• Reading CSV/Excel files
• Data filtering
• Sorting
• Grouping
• Installing Python environment
• Jupyter Notebook
• Variables
• Data types
• Operators
• Conditions
• Loops
• Functions
Python Libraries for Analytics
Introduction to:
NumPy
• Arrays
• Mathematical operations
Pandas
• DataFrames
• Reading CSV/Excel files
• Data filtering
• Sorting
• Grouping
7 Data Cleaning & Exploratory Data Analysis
Data Preparation Using Python
• Missing data handling
• Duplicate removal
• Data formatting
• Data transformation
• Data quality checks
Exploratory Data Analysis (EDA)
• Understanding dataset patterns
• Finding trends
• Detecting outliers
• Data relationships
Visualization:
• Matplotlib
• Seaborn
• Missing data handling
• Duplicate removal
• Data formatting
• Data transformation
• Data quality checks
Exploratory Data Analysis (EDA)
• Understanding dataset patterns
• Finding trends
• Detecting outliers
• Data relationships
Visualization:
• Matplotlib
• Seaborn
8 Data Visualization & Storytelling
Visualization Principles
• Importance of visualization
• Choosing the right chart
• Avoiding misleading visuals
Charts:
• Bar charts
• Line charts
• Scatter plots
• Histograms
• Heatmaps
Data Storytelling
• Presenting insights
• Building executive reports
• Turning numbers into decisions
• Communicating with stakeholders
• Importance of visualization
• Choosing the right chart
• Avoiding misleading visuals
Charts:
• Bar charts
• Line charts
• Scatter plots
• Histograms
• Heatmaps
Data Storytelling
• Presenting insights
• Building executive reports
• Turning numbers into decisions
• Communicating with stakeholders
9 Power BI for Business Intelligence
Power BI Fundamentals
• Introduction to Power BI
• Power BI interface
• Connecting data sources
• Importing Excel/CSV/database data
• Power Query cleaning
Power BI Data Modeling
• Relationships
• Data models
• Star schema
• Calculated columns
• Measures
Introduction to DAX:
• SUM
• COUNT
• CALCULATE
• FILTER
• Introduction to Power BI
• Power BI interface
• Connecting data sources
• Importing Excel/CSV/database data
• Power Query cleaning
Power BI Data Modeling
• Relationships
• Data models
• Star schema
• Calculated columns
• Measures
Introduction to DAX:
• SUM
• COUNT
• CALCULATE
• FILTER
10 Advanced Power BI & Tableau
Advanced Power BI Dashboard Development
• Interactive dashboards
• KPIs
• Drill-through reports
• Filters and slicers
• Time intelligence
• Dashboard design principles
Tableau / Looker Studio Introduction
• Tableau interface
• Connecting datasets
• Creating dashboards
• Sharing reports
Comparison:
• Power BI vs Tableau vs Looker Studio
• Interactive dashboards
• KPIs
• Drill-through reports
• Filters and slicers
• Time intelligence
• Dashboard design principles
Tableau / Looker Studio Introduction
• Tableau interface
• Connecting datasets
• Creating dashboards
• Sharing reports
Comparison:
• Power BI vs Tableau vs Looker Studio
11 AI, Automation & Modern Data Analytics
AI Tools for Data Analysts
• Using ChatGPT for analytics
• AI-assisted SQL writing
• AI-assisted Python coding
• Generating insights
• Data summarization
• Automated reporting
Analytics Automation
• Introduction to automation
• Automated reports
• Data refresh processes
• APIs and data collection basics
• Introduction to cloud analytics concepts
Tools:
• Power BI Service
• Google Sheets Automation
• Zapier / Make basics
• Using ChatGPT for analytics
• AI-assisted SQL writing
• AI-assisted Python coding
• Generating insights
• Data summarization
• Automated reporting
Analytics Automation
• Introduction to automation
• Automated reports
• Data refresh processes
• APIs and data collection basics
• Introduction to cloud analytics concepts
Tools:
• Power BI Service
• Google Sheets Automation
• Zapier / Make basics
12 Capstone Project & Career Preparation
Salary trend
Entry level₦200k–₦400k/mo or more
Mid level₦450k–₦800k/mo or more
Senior level₦1M+/mo or more
Estimated Nigerian market ranges; varies by role, company, and experience.
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