Understand how data is used to solve real business problems.
School of Data & AIData & AIBeginnerIncluded in a Professional Diploma
Data Foundations
Start your data career with clarity, confidence, and the right foundation.
Understand how data works, how businesses use it, what different data roles do, and how tools like Excel, SQL, Python, Power BI, AI, and data engineering fit into a serious data career.
Duration
2 weeks - 4–6 hours/week
Project
Understand how data is used to solve real business problems.
Support
Pricing and enrolment are handled through the Professional Diploma
Overview
A practical Short Course built around a visible project.
Build the essential foundation for working with data, understanding business problems, and preparing for tools like Excel, SQL, Python, Power BI, machine learning, AI, and data engineering.
Explain the difference between data, reports, dashboards, insights, and decisions.
Understand the major roles across analytics, data science, AI, and data engineering.
Identify common tools used by modern data teams.
Recognize data sources, file formats, and common data quality issues.
Ask better analytical questions before jumping into tools.
Understand how sales, finance, operations, marketing, product, and customer teams use data.
Build the confidence to progress into Excel, SQL, Python, Power BI, AI, and data engineering workflows.
Course roadmap
What you will work through.
The sequence below is specific to this course. It shows the phases, modules, lessons, and page outlines that move you toward Understand how data is used to solve real business problems..
1Phase 1 - Introduction to Data & AIIntroduce data, organizational data use, and the modern data ecosystem.1 modules3 lessons1 week
Module 1: The World of DataUnderstand what data is, how organizations use it, and how the modern data ecosystem works.3 lessons
Lesson 1: What Is Data?Understand data as recorded facts, observations, events, and signals that can be interpreted to support decisions.75 minarticle5 pages
Welcome and Learning Objectives
Start the lesson and understand the purpose of learning data foundations.
8 min
Data in Plain English
Explain what data is and why context matters.
15 min
Structured, Semi-Structured, and Unstructured Data
Classify the main forms of data students will meet in analytics, data science, AI, and data engineering.
18 min
Data Sources in Everyday Life
Help students see that data is created constantly by normal activities.
15 min
Exercise - Daily Data Source Audit
Students identify real data sources around them and classify them.
19 min
Lesson 2: How Organizations Use DataExplore how businesses and institutions use data for decisions, reporting, forecasting, optimization, and automation.80 minarticle5 pages
Welcome and Learning Objectives
Introduce organizational data use.
8 min
The Five Common Uses of Data
Explain common ways data supports organizations.
22 min
Case Study - Netflix Recommendations
Introduce recommendations as a beginner-friendly example of organizational data use.
20 min
Data Usage Map
Show how one organization can use many kinds of data.
15 min
Exercise - Company Data Usage Map
Students map how data may be used in a real organization.
15 min
Lesson 3: The Modern Data EcosystemUnderstand how data moves from sources into databases, warehouses, dashboards, models, AI systems, and decisions.80 minarticle5 pages
Welcome and Learning Objectives
Introduce the modern data ecosystem.
8 min
The Main Components
Explain core components in the ecosystem.
22 min
Complete Data Flow Example
Show a realistic beginner-friendly data flow.
20 min
Diagram Exercise - Draw Complete Data Flow
Students create a data ecosystem diagram.
20 min
Module Summary
Summarize the first module and prepare students for data roles.
10 min
2Phase 2 - Data Careers & RolesHelp students understand the major data and AI career paths before choosing a specialization.1 modules5 lessons1 week
Module 1: Understanding the Data ProfessionCompare the responsibilities, deliverables, tools, and thinking patterns across modern data roles.5 lessons
Lesson 1: Data AnalystUnderstand what data analysts do, the problems they solve, and the deliverables they create.45 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
22 min
Practice Activity
Apply the concept through a guided activity.
15 min
Lesson 2: Data ScientistUnderstand how data scientists use statistics, programming, and models to explore patterns and make predictions.45 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
22 min
Practice Activity
Apply the concept through a guided activity.
15 min
Lesson 3: Data EngineerUnderstand how data engineers build pipelines, warehouses, and infrastructure that make data usable.45 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
22 min
Practice Activity
Apply the concept through a guided activity.
15 min
Lesson 4: AI EngineerUnderstand AI engineering at a high level, including LLMs, RAG, agents, and AI product systems.45 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
22 min
Practice Activity
Apply the concept through a guided activity.
15 min
Lesson 5: Choosing Your PathUse a practical decision framework to choose a data or AI learning path.60 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
30 min
Practice Activity
Apply the concept through a guided activity.
22 min
3Phase 3 - Data ThinkingTeach students how to ask better questions, define useful metrics, investigate causes, and communicate insights.1 modules4 lessons1 week
Module 1: Analytical ThinkingBuild the thinking habits that separate tool users from real data professionals.4 lessons
Lesson 1: Questions Before AnswersLearn to define problems and ask useful analytical questions before touching tools.50 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
25 min
Practice Activity
Apply the concept through a guided activity.
17 min
Lesson 2: Metrics and KPIsUnderstand metrics, KPIs, vanity metrics, and actionable measures.55 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
27 min
Practice Activity
Apply the concept through a guided activity.
20 min
Lesson 3: Root Cause AnalysisLearn how to investigate business problems instead of jumping to shallow conclusions.55 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
27 min
Practice Activity
Apply the concept through a guided activity.
20 min
Lesson 4: Data StorytellingLearn how to communicate insights through clear narratives and decision-focused reporting.55 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
27 min
Practice Activity
Apply the concept through a guided activity.
20 min
4Phase 4 - Working with DataDevelop core data literacy: data types, data quality, cleaning concepts, and exploratory analysis.1 modules4 lessons1 week
Module 1: Data LiteracyUnderstand datasets, quality issues, cleaning concepts, and beginner exploration.4 lessons
Lesson 1: Data TypesClassify numeric, categorical, time-series, and text data.45 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
22 min
Practice Activity
Apply the concept through a guided activity.
15 min
Lesson 2: Data QualityIdentify missing values, duplicates, inconsistencies, and their business impact.50 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
25 min
Practice Activity
Apply the concept through a guided activity.
17 min
Lesson 3: Data Cleaning ConceptsUnderstand validation, standardization, and transformation at a beginner level.50 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
25 min
Practice Activity
Apply the concept through a guided activity.
17 min
Lesson 4: Exploratory AnalysisLearn how to explore patterns, trends, outliers, and initial questions in a dataset.55 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
27 min
Practice Activity
Apply the concept through a guided activity.
20 min
5Phase 5 - Statistics for Decision MakingTeach practical, business-focused statistics without heavy mathematics.1 modules4 lessons1 week
Module 1: Practical StatisticsUse basic statistics to summarize data and support better decisions.4 lessons
Lesson 1: Descriptive StatisticsUnderstand mean, median, mode, variance, and how they describe business data.50 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
25 min
Practice Activity
Apply the concept through a guided activity.
17 min
Lesson 2: Probability ConceptsUnderstand uncertainty, risk, and likelihood using business examples.45 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
22 min
Practice Activity
Apply the concept through a guided activity.
15 min
Lesson 3: Correlation vs CausationAvoid common mistakes and misleading conclusions when variables move together.50 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
25 min
Practice Activity
Apply the concept through a guided activity.
17 min
Lesson 4: Making Decisions with DataUse confidence, evidence, and tradeoffs to make better business decisions.55 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
27 min
Practice Activity
Apply the concept through a guided activity.
20 min
6Phase 6 - Data, AI & EthicsIntroduce responsible data use, privacy, bias, AI ethics, and the future of data work.2 modules8 lessons1 week
Module 1: Responsible Data UseBuild responsible habits around privacy, bias, fairness, transparency, and AI accountability.4 lessons
Lesson 1: Data PrivacyUnderstand personal data, consent, and compliance concepts at a beginner level.45 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
22 min
Practice Activity
Apply the concept through a guided activity.
15 min
Lesson 2: Bias in DataUnderstand how bias, fairness, and representation affect data conclusions.50 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
25 min
Practice Activity
Apply the concept through a guided activity.
17 min
Lesson 3: AI EthicsUnderstand hallucinations, transparency, accountability, and safe AI usage.55 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
27 min
Practice Activity
Apply the concept through a guided activity.
20 min
Lesson 4: Future of Data and AIExplore AI transformation, automation, emerging careers, and personal roadmap planning.50 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
25 min
Practice Activity
Apply the concept through a guided activity.
17 min
Module 2: Foundations Projects and GraduationPackage learning into mini projects, a final foundations project, and graduation requirements.4 lessons
Lesson 1: Mini Project 1 - Business Metrics AnalysisAnalyze a business domain and design practical metrics and recommendations.60 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
30 min
Practice Activity
Apply the concept through a guided activity.
22 min
Lesson 2: Mini Project 2 - Data Quality AuditAudit a messy dataset and produce a quality report.60 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
30 min
Practice Activity
Apply the concept through a guided activity.
22 min
Lesson 3: Final Foundations Project - Data-Driven Business AnalysisAnalyze a real company or product from a data perspective and present recommendations.90 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
45 min
Practice Activity
Apply the concept through a guided activity.
37 min
Lesson 4: Graduation Requirements and Portfolio OutcomeClarify what students must complete and what they should have in their portfolio.40 minarticle1 pages
Requirements and Portfolio Checklist
Summarize graduation requirements.
40 min
Tools and skills
Build skill with the tools used in the work.
Understand how data is used to solve real business problems.Explain the difference between data, reports, dashboards, insights, and decisions.Understand the major roles across analytics, data science, AI, and data engineering.Identify common tools used by modern data teams.Recognize data sources, file formats, and common data quality issues.Ask better analytical questions before jumping into tools.Understand how sales, finance, operations, marketing, product, and customer teams use data.Build the confidence to progress into Excel, SQL, Python, Power BI, AI, and data engineering workflows.
Projects and exercises
- Understand how data is used to solve real business problems.
- Structured exercises
- Portfolio practice
Resources included
- Course resources
- Project guidance
- Learners building practical tech skills
- A willingness to practice consistently
Career relevance
Data Foundations supports practical career readiness.
Related Professional Diploma
View Professional DiplomaData Analytics
Become a practical data analyst who can clean, analyze, visualize, and communicate business data using Excel, SQL, Python, Power BI, and real-world analytics projects.
FAQ
Questions about this Short Course.
Short Course answers about scope, projects, support, and next steps.
Yes. This course is designed for learners who are new to data and want to understand the field before learning specific tools.
Related Short Courses
View all Short CoursesContinue building connected skills.
School of Data & AIData & AIBeginner to Intermediate
Excel for Data Analytics
Turn raw spreadsheets into clean analysis, useful reports, and business-ready insights.
Master the Excel skills used by data analysts to clean, organize, calculate, summarize, visualize, and report business data with confidence.
From₦50,000
6 weeks - 5–8 hours /week
Clean and organize messy spreadsheet data.
Project included
Mentor review available
School of Data & AIData AnalyticsIntermediate
Power BI for Business Intelligence
Build interactive dashboards and business reports that make performance clear.
Learn to connect, clean, model, measure, visualize, and present business data using Power BI.
From₦85,000
8 weeks - 6-8 hours/week
Connect Power BI to different data sources.
Project included
Mentor review available
School of Data & AIData AnalyticsBeginner to Intermediate
SQL for Data Analytics
Query databases, join tables, summarize records, and uncover business insights with SQL.
Learn the SQL skills data analysts use to extract, filter, join, group, and analyze data from relational databases.
From₦65,000
7 weeks - 6-8 hours/week
Understand tables, columns, rows, keys, and relationships.
Project included
Mentor review available
Related Professional Diploma
Data Engineering
Professional Diploma application
Continue through Data Analytics.
This course is included in a Professional Diploma, so tuition enrollment is handled after the diploma application flow.