Data & Analytics
Frame business questions that analysis can actually answer
Data & Analytics teaches freelancers, analysts, operators, and African SME teams how to turn messy business data into honest, decision-ready recommendations.
LEARN to frame answerable questions, profile and clean datasets, analyse with spreadsheets and SQL, choose charts that support decisions, and define metrics for real operating contexts. BUILD a Business Question & Dataset Diagnostic and a Business Dataset Analysis Evidence Pack. GROW the discipline to document assumptions, limitations, and reproducibility before sharing results.
This is an analyst-oriented foundations programme — not a full data science or data engineering degree. Completing it earns an Africoders Certificate of Completion. It is not vendor accreditation or an employment guarantee. Paid specialist courses may supplement learning — they are not required.
What you will learn
- Frame business questions that analysis can actually answer
- Profile, clean, and validate datasets before drawing conclusions
- Analyse with spreadsheets and write readable SQL for common joins and aggregates
- Summarise distributions, comparisons, and uncertainty honestly
- Choose charts and dashboards that support decisions, not decoration
- Define business metrics, funnels, and guardrails for African operating contexts
Who this course is for
- Freelancers and operators producing reports, dashboards, or recommendations
- Career switchers comfortable with spreadsheets who want structured analytics practice
- Product-adjacent builders who need metrics and evidence-led decisions
- African SME teams turning operational data into actionable insight
Skills you will develop
Analytical thinking · Data cleaning and validation · Spreadsheets for analysis · SQL for analysts · Descriptive statistics · Data visualisation · Dashboards · Business metrics · Python for analytics basics · Data storytelling · Relational databases and SQL
Prerequisites
- Comfort with basic arithmetic and reading tables
- Ability to use a computer spreadsheet (Excel, Google Sheets, or LibreOffice)
- Willingness to document assumptions and sources
- No prior programming or statistics degree required
Who this is for
- Freelancers and operators producing reports, dashboards, or recommendations
- Career switchers comfortable with spreadsheets who want structured analytics practice
- Product-adjacent builders who need metrics and evidence-led decisions
- African SME teams turning operational data into actionable insight
Prerequisites
- Comfort with basic arithmetic and reading tables
- Ability to use a computer spreadsheet (Excel, Google Sheets, or LibreOffice)
- Willingness to document assumptions and sources
- No prior programming or statistics degree required
Skills you’ll develop
Course completion demonstrates these skills. Verification requires assessment or reviewed challenge work.
What you will build and prove
- Practice: Practice: Framing questions that analysis can answer
- Project: Capstone: Business Dataset Analysis Evidence Pack
- Final assessment: Data Analytics Foundations assessment (pass 70%)
Course journey
Learn → Practice → Build → Prove. Lessons teach the idea. Practice applies it. The project is what you can submit as evidence.
- Analytical Thinking
- Data Cleaning
- Spreadsheets for Analysis
- SQL for Analysts
- Descriptive Statistics
- Data Visualisation
- Dashboards
- Business Metrics
- Python for Analytics Basics
- Data Storytelling
- Validation and Quality
- Portfolio Project
Practice
1 optional practice exercise — apply what you learned. Practice is not required to complete the course.
Assessment
- Data Analytics Foundations assessment · pass mark 70%
Prove
Course completion means finishing required lessons , required assignments , the course project , and the course assessment . That grants a certificate of completion — not a professional certification. Optional practice does not block completion.
Builder Passport evidence is awarded for the completed project, not for watching lessons or passing a quiz alone.
Curriculum
Week 1: Analytical Thinking
- Framing questions that analysis can answer Preview Practice 30min
- Evidence versus anecdote and gut feel 30min
- Decision-ready analysis planning 32min
Week 2: Data Cleaning
- Profiling datasets before you trust them 30min
- Handling missing values, duplicates, and outliers 32min
- Validation rules and cleaning logs 30min
Week 3: Spreadsheets for Analysis
- Formulas, lookups, and structured tables 32min
- Pivot tables and grouped summaries 30min
- Spreadsheet hygiene and collaboration 28min
Week 4: SQL for Analysts
- Selecting, filtering, and reading query results 32min
- Joins that respect business relationships 34min
- Aggregations and readable analytical SQL 32min
Week 5: Descriptive Statistics
- Distributions, centre, and shape 30min
- Variability, ranges, and honest uncertainty 30min
- Comparing groups without false causation 32min
Week 6: Data Visualisation
- Choosing charts for the question 30min
- Decluttering and labelling for decision makers 28min
- Avoiding misleading visualisations 30min
Week 7: Dashboards
- Audience, decisions, and dashboard scope 30min
- Layout, hierarchy, and annotation 30min
- Refresh, ownership, and trust 28min
Week 8: Business Metrics
- North-star thinking and input metrics 30min
- Funnels, conversion, and cohorts 32min
- KPI sets for African SME operations 30min
Week 9: Python for Analytics Basics
- Notebooks, environments, and dataframe thinking 32min
- Cleaning and transforming with code 34min
- Simple analysis scripts and outputs 30min
Week 10: Data Storytelling
- From findings to insight narrative 30min
- Briefing operators versus executives 28min
- Recommendations, caveats, and next measurements 30min
Week 11: Validation and Quality
- QA checks before you share 30min
- Reproducibility and analysis handoff 30min
- Peer review for analytical work 28min
Week 12: Portfolio Project
- Scoping a portfolio-worthy analysis 30min
- Building the analysis evidence pack 34min
- Presenting findings and ethical claims 30min
Free to enroll — no purchase required.
Instructor: Africoders
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