Technology & security
Data Analyst Career Path
Learn how analysts turn messy workplace data into clear, responsible decisions.
Learn the practical workflow behind turning messy workplace data into clear, useful decisions.
About this course
A job-focused introduction to data analysis for aspiring analysts, administrators and operations staff. Build a dependable process from question to clean data, analysis, visualisation and an evidence-led recommendation.
Career relevance
Where this can take you
Useful for junior data analyst, reporting administrator, operations analyst and business-support roles.
Portfolio project: Create a portfolio-style analysis case study from question and data cleaning through findings and recommendation.
Skills you will gain
Practical skills, not just theory
What you will learn
Frame an analysis around a clear business question.
Clean, validate and document a small dataset.
Use descriptive statistics, filters and comparisons responsibly.
Create a clear visual summary and recommendation without overstating the evidence.
Objective map
A numbered, measurable syllabus
Every lesson, knowledge check and practical activity maps to the objectives below. Domain weights show how much of the programme each area represents.
Domain 1
What a data analyst actually does to Spreadsheet analysis foundations
- 1.1Demonstrate what a data analyst actually does in a realistic data analyst career path context: start with a decision question, stakeholder need and practical definition of success.
- 1.2Demonstrate data quality and cleaning in a realistic data analyst career path context: find missing values, duplicates, inconsistent formats and source limitations.
- 1.3Analyse and evaluate spreadsheet analysis foundations in a realistic data analyst career path context: use formulas, filters, lookups and checks to analyse data accurately.
Domain 2
SQL thinking and relational data to Dashboards and data storytelling
- 2.1Demonstrate sql thinking and relational data in a realistic data analyst career path context: understand tables, keys, joins and safe query logic through practical examples.
- 2.2Demonstrate statistics and interpretation in a realistic data analyst career path context: use averages, distributions and comparisons without misleading a reader.
- 2.3Demonstrate dashboards and data storytelling in a realistic data analyst career path context: choose useful visuals and communicate a concise, evidence-led recommendation.
Domain 3
Capstone: analyst portfolio case study
- 3.1Complete and defend capstone: analyst portfolio case study in a realistic data analyst career path context: turn a raw operational dataset into a documented analysis and presentation.
How the programme works
Learn, practise, explain and prove it
This is a long-form, eight-week guided programme rather than a stack of short articles. Each module contains six substantial lessons that move from concepts and methods into a worked case, a quality clinic, a workplace lab and assessed reflection.
1. Guided learning
Study the purpose, process, risks and professional standard through structured readings and active notes.
2. Worked cases
Compare plausible options, expose missing evidence and defend a decision under realistic constraints.
3. Quality clinics and labs
Diagnose weak work, revise it against a rubric and create portfolio evidence with a full audit trail.
4. Hard assessment
Pass applied checks and the final assessment at 85%, with no unresolved critical-error questions.
Curriculum
A complete programme, module by module
The curriculum below shows the actual learning pathway, objective and evidence for every module. Full lessons remain locked until enrolment.
Build the base
Standards, vocabulary and the core method you will use throughout the programme.
01What a data analyst actually doesStart with a decision question, stakeholder need and practical definition of success.+
Measured objectiveDemonstrate what a data analyst actually does in a realistic data analyst career path context: start with a decision question, stakeholder need and practical definition of success.
Evidence you will produceA checked working paper, analysis or report for what a data analyst actually does, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
02Data quality and cleaningFind missing values, duplicates, inconsistent formats and source limitations.+
Measured objectiveDemonstrate data quality and cleaning in a realistic data analyst career path context: find missing values, duplicates, inconsistent formats and source limitations.
Evidence you will produceA checked working paper, analysis or report for data quality and cleaning, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
Milestone: You can explain the fundamentals and complete a first guided task.
Develop core skills
The working skills and tools of the role, practised one at a time.
03Spreadsheet analysis foundationsUse formulas, filters, lookups and checks to analyse data accurately.+
Measured objectiveAnalyse and evaluate spreadsheet analysis foundations in a realistic data analyst career path context: use formulas, filters, lookups and checks to analyse data accurately.
Evidence you will produceA checked working paper, analysis or report for spreadsheet analysis foundations, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
04SQL thinking and relational dataUnderstand tables, keys, joins and safe query logic through practical examples.+
Measured objectiveDemonstrate sql thinking and relational data in a realistic data analyst career path context: understand tables, keys, joins and safe query logic through practical examples.
Evidence you will produceA checked working paper, analysis or report for sql thinking and relational data, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
Milestone: You can carry out routine work independently and check it properly.
Apply it in realistic scenarios
Case studies and simulations with competing priorities and incomplete information.
05Statistics and interpretationUse averages, distributions and comparisons without misleading a reader.+
Measured objectiveDemonstrate statistics and interpretation in a realistic data analyst career path context: use averages, distributions and comparisons without misleading a reader.
Evidence you will produceA checked working paper, analysis or report for statistics and interpretation, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
06Dashboards and data storytellingChoose useful visuals and communicate a concise, evidence-led recommendation.+
Measured objectiveDemonstrate dashboards and data storytelling in a realistic data analyst career path context: choose useful visuals and communicate a concise, evidence-led recommendation.
Evidence you will produceA checked working paper, analysis or report for dashboards and data storytelling, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
Milestone: You can make and justify decisions in realistic situations.
Prove it and plan your next step
Portfolio evidence, final review and a development plan you can show an employer.
07Capstone: analyst portfolio case studyTurn a raw operational dataset into a documented analysis and presentation.+
Measured objectiveComplete and defend capstone: analyst portfolio case study in a realistic data analyst career path context: turn a raw operational dataset into a documented analysis and presentation.
Evidence you will produceA checked working paper, analysis or report for capstone: analyst portfolio case study, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
Milestone: You have portfolio-ready evidence and a clear next step.
Suggested pace
Eight-week study plan
Plan for roughly 5–9 hours each week across readings, worked cases, practice, portfolio production and assessment remediation. Repeat a week when the knowledge checks show a gap.
Week 1
What a data analyst actually does
- •Complete the six guided lessons for What a data analyst actually does and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the what a data analyst actually does portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 2
Data quality and cleaning
- •Complete the six guided lessons for Data quality and cleaning and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the data quality and cleaning portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 3
Spreadsheet analysis foundations
- •Complete the six guided lessons for Spreadsheet analysis foundations and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the spreadsheet analysis foundations portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 4
SQL thinking and relational data
- •Complete the six guided lessons for SQL thinking and relational data and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the sql thinking and relational data portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 5
Statistics and interpretation
- •Complete the six guided lessons for Statistics and interpretation and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the statistics and interpretation portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 6
Dashboards and data storytelling
- •Complete the six guided lessons for Dashboards and data storytelling and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the dashboards and data storytelling portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 7
Capstone: analyst portfolio case study
- •Complete the six guided lessons for Capstone: analyst portfolio case study and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the capstone: analyst portfolio case study portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 8
Review and consolidation
- •Complete a cumulative, closed-notes retrieval review across all objectives studied so far.
- •Repair incomplete portfolio evidence and repeat the two lowest-scoring knowledge checks.
- •Ask for structured feedback, record the feedback and revise one artefact to professional standard.
Checkpoint: Submit the capstone evidence pack, defend the key decision, remediate every weak objective and pass the final assessment at 85% with no critical misses.
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Assessment and certificate
A portfolio-style data case and knowledge assessment. Completion confirms course work only; it is not an industry certification.
Who this is for
- ✓Aspiring data analysts and reporting administrators.
- ✓Graduates building a portfolio for analytical roles.
- ✓Professionals who need to make better use of spreadsheets and operational data.