Excel / Google Sheets
Cleaning, pivots, and quick analysis.
Skills you'll build
Excel: formulas, pivots, lookupsSQL: joins, aggregations, window functionsData cleaning & preparationPython for analysis (pandas)Descriptive statisticsA/B testing basicsPower BI & DAXTableau dashboardsData storytelling & presentationKPI design & reporting
Who this is for
Beginners, graduates, and professionals in finance, marketing, operations, or admin who want to work with data and move into analyst roles.
What you'll need
Everything to have in place before day one.
Prerequisites
- Comfort with basic maths (percentages, averages)
- Basic spreadsheet use
- No coding experience required
Equipment & time
- A laptop (Windows, macOS, or Linux) with at least 8 GB RAM
- Stable internet for live sessions and labs
- 8–10 hours per week for classes, labs, and projects
- Free accounts we set up together in week one (no paid software needed to start)
Tools you'll use
The same tools used by teams in the industry today.
SQL (PostgreSQL / BigQuery)
Query and join business data.
Power BI
Interactive dashboards and DAX measures.
Tableau
Visual analytics and storytelling.
Python (pandas, Jupyter)
Repeatable analysis and automation.
GitHub
Host and share your portfolio projects.
Course outline & syllabus
Six phases, taken in order. Each ends with a practical lab so you apply what you learn straight away.
Phase 1: Data Foundations & Excel
Beginner- The analyst role: questions, data, insights, decisions
- Data types, tables, and tidy data principles
- Excel formulas, XLOOKUP, conditional logic, and pivots
- Cleaning messy data: duplicates, blanks, formats
- Charts that communicate clearly
Phase 2: SQL for Analysis
Essential- SELECT, WHERE, ORDER BY, and aggregations
- JOINs across multiple business tables
- CTEs, subqueries, and window functions
- Date handling, cohorts, and running totals
- Querying a cloud warehouse (BigQuery)
Phase 3: Statistics & Python
Core- Descriptive statistics: distributions, mean, median, variance
- Correlation vs causation and common analysis pitfalls
- A/B testing and significance basics
- Python with pandas: load, clean, group, and merge data
- Visualisation with matplotlib and seaborn
Phase 4: Business Intelligence
BI- Power BI: data modelling, relationships, and DAX measures
- Tableau: calculated fields, filters, and dashboards
- Designing KPIs and metric definitions
- Dashboard UX: layout, drill-downs, and performance
- Publishing, sharing, and refresh schedules
Phase 5: Storytelling & Stakeholders
Advanced- Framing the business question before analysing
- Turning findings into recommendations
- Presenting to non-technical audiences
- Writing analysis reports and executive summaries
- Data ethics, privacy, and GDPR basics
Phase 6: Capstone & Career
Career- End-to-end project on a real public dataset
- SQL analysis, Python notebook, and a published dashboard
- Present insights and recommendations to a panel
- Certification paths: Microsoft PL-300, Google Data Analytics, Tableau
- Job search: analyst CV, portfolio, and technical interview practice
Portfolio projects
Build work you can show hiring managers, not just theory.
Sales performance dashboard
- Power BI model with DAX measures
- Drill-down by region, product, and time
Customer cohort analysis
- SQL window functions for retention
- Recommendations to reduce churn
Python exploratory analysis
- Clean and explore a messy dataset
- Notebook with clear findings
A/B test readout
- Test design and significance check
- One-page decision memo
Keep learning after the course
Your ongoing syllabus: what to study next as you grow in the role.
Level up technically
- Advanced SQL performance and query optimisation
- dbt for analytics engineering
- Python automation of recurring reports
Grow toward new roles
- Analytics engineering (see Data Engineering)
- Statistics and machine learning (see Data Science)
- Product analytics: funnels, retention, experimentation
Stay current
- New Power BI and Tableau features each release
- AI-assisted analysis tools and their limits
- Domain metrics in your industry
Certifications & resources
Ready to start Data Analyst?
Register your interest and we'll recommend the right starting phase for your background and goals.