Learning pathway
Foundations of Data Analytics
From a question to a defensible answer: querying, cleaning, analysing and presenting.
Potential prerequisites
- Comfort with spreadsheets
- No programming experience assumed
Intended audience
Who this is for
- People who already produce reports and want to work from the source data
- Career changers from operations, finance or support roles
- Students who want evidence of analytical skill before applying
Learning objectives
What you should be able to do
- 01Write SQL that answers a real question, including joins and aggregation
- 02Clean and validate a dataset, and document what you changed
- 03Choose a chart that represents the data without distorting it
- 04Write up findings for a reader who will not inspect your work
- 05State clearly what your analysis cannot conclude
Suggested modules
A sequence that builds on itself
Each module assumes the one before it. Skipping ahead usually costs more time than it saves.
- Module 01
Framing the question
Turning a vague request into a question data can answer, and agreeing what would count as an answer.
- Stakeholder interviewing
- Scope definition
- Module 02
SQL for analysis
Selecting, filtering, joining and aggregating. Enough to get your own data without waiting for someone else.
- SQL
- Relational models
- Module 03
Cleaning and validation
Missing values, duplicates, inconsistent categories — and recording every decision so the work is reproducible.
- Data cleaning
- Documentation
- Module 04
Descriptive statistics
Distributions, averages that mislead, variation, and the traps that produce confident wrong answers.
- Statistics fundamentals
- Sanity-checking
- Module 05
Visualisation and dashboards
Chart choice, hierarchy, honest axes, and building something people will return to.
- Visual communication
- Dashboard tools
- Module 06
Communicating findings
Structuring a written analysis so the conclusion arrives before the method, with limits stated.
- Written explanation
- Presenting
Practical exercises and projects
What you build along the way
The projects are the point. They're what you can show someone afterwards.
- Project 01
Public dataset investigation
Pick an open dataset, state a question before you look, and write a short report answering it — including what you could not determine.
- Project 02
Rebuild a manual report
Take a recurring spreadsheet from your own work or a volunteer organisation and rebuild it as a query-backed dashboard.
- Project 03
Metric definitions document
Write definitions for five metrics an organisation disagrees about. This is unglamorous and unusually persuasive to hiring managers.
Suggested progression
Where it leads next
- Deepen into analytics engineering, warehousing and modelling
- Move toward experimentation and causal analysis
- Specialise in a domain — product, finance, operations, health
Related career directions
What this pathway serves
- Data and AnalyticsTurn messy records into answers a team can act on — and explain the answer clearly.
- Finance and Business AnalysisSit between a business problem and the people who will build or fund the answer.
- Project and Product ManagementDecide what gets built and keep the work moving — mostly through influence, rarely through authority.
This pathway is a conceptual curriculum, not an enrolled course. Career Mine does not provide accreditation or certification, does not host course delivery, and makes no claim about employment outcomes from following it.
Before you start
Check this is aimed at the right place.
A pathway is only useful if the destination still appeals once you know what the work involves.