Professional Certificate in Data Analysis
Data analysis turns business questions into evidence that people can review and use. This practical online course develops the complete working cycle: framing the decision, defining measures, preparing trustworthy data, analyzing with spreadsheets and SQL, applying proportionate statistics, creating accessible visuals and delivering a reproducible recommendation.
Every lesson develops one bounded capability and one reusable workplace artifact. A connected fictional retail case shows how the parts fit together, while standalone practice scenarios help you transfer the method. The applied capstone places you in a new café decision and asks for one coherent Data Analysis Decision Pack.
Who this course is for
The course is designed for:
- aspiring and early-career data analysts;
- reporting and business-intelligence professionals;
- business and operations analysts who prepare decision support;
- finance, marketing, product, customer and service specialists who work with data;
- spreadsheet users moving toward more controlled analytical work;
- professionals beginning to use SQL, statistics and visualization; and
- career changers building a practical data-analysis portfolio.
The lessons explain the work from first principles and use plain professional English. Previous analytical experience is useful but optional.
What you will be able to do
By the end of the course, you will be able to:
- turn an ambiguous request into one answerable analytical decision question;
- define metrics, grain, populations, periods and fair comparison rules;
- map sources, keys, access routes and responsible-use controls;
- profile data, diagnose quality problems and record material limitations;
- transform and reconcile data into a reviewable analysis-ready dataset;
- build controlled spreadsheet analyses and validated SQL queries;
- join tables safely, segment KPIs and specify reusable analysis tables;
- describe variation, compare groups and communicate uncertainty;
- investigate trends, anomalies, funnels, cohorts and operational changes;
- design accessible visuals, dashboards, data stories and recommendations; and
- hand off analysis through a reproducible process another analyst can run.
Applied learning and professional artifacts
The course contains twenty substantial lessons. Each lesson combines theory, a practical method, a connected case, a blank template, a completed example, guided AI practice, verification controls and a workplace-transfer assignment.
You will learn how to create and use these twenty artifacts:
- Analysis Request and Decision Brief;
- Metric and Comparison Definition Sheet;
- Source, Grain and Access Map;
- Data Profile and Quality Findings Log;
- Transformation and Reconciliation Record;
- Controlled Spreadsheet Analysis Workbook;
- SQL Analysis and Validation Sheet;
- Join Design and Row-Control Record;
- Segmented KPI Analysis Sheet;
- Reusable Analysis Table Specification;
- Distribution and Variation Brief;
- Group Comparison and Uncertainty Note;
- Trend and Root-Cause Investigation Log;
- Funnel and Cohort Analysis Sheet;
- Experiment and Change Evaluation Readout;
- Accessible Visual Evidence Specification;
- Dashboard and Scorecard Design Brief;
- Data Story Claim and Evidence Map;
- Decision Insight and Recommendation Memo; and
- Reproducible Analysis Handoff Runbook.
Completed examples show how an analyst records assumptions, definitions, denominators, calculations, validation checks, evidence strength, uncertainty and decision ownership.
Curriculum
Module 1 — Frame the Question and Prepare Trustworthy Data
- Turn a Business Request into an Analytical Decision Question — create an Analysis Request and Decision Brief.
- Define Metrics, Grain and Fair Comparisons — create a Metric and Comparison Definition Sheet.
- Map Sources, Keys and Responsible Data Use — create a Source, Grain and Access Map.
- Profile Data and Diagnose Quality Problems — create a Data Profile and Quality Findings Log.
- Prepare and Reconcile an Analysis-Ready Dataset — create a Transformation and Reconciliation Record.
Module 2 — Analyze with Spreadsheets and SQL
- Build a Controlled Spreadsheet Analysis — create a Controlled Spreadsheet Analysis Workbook.
- Query and Validate a Single Table with SQL — create a SQL Analysis and Validation Sheet.
- Join Tables without Multiplying or Losing Records — create a Join Design and Row-Control Record.
- Segment KPIs and Answer Ad Hoc Questions — create a Segmented KPI Analysis Sheet.
- Design a Reusable Analysis Table — create a Reusable Analysis Table Specification.
Module 3 — Apply Statistics and Investigate Patterns
- Describe Distributions and Practical Variation — create a Distribution and Variation Brief.
- Compare Groups and Communicate Uncertainty — create a Group Comparison and Uncertainty Note.
- Diagnose Trends, Anomalies and Candidate Causes — create a Trend and Root-Cause Investigation Log.
- Analyze Funnels, Cohorts and Retention Patterns — create a Funnel and Cohort Analysis Sheet.
- Evaluate an Experiment or Operational Change — create an Experiment and Change Evaluation Readout.
Module 4 — Visualize, Communicate and Hand Off Decisions
- Design Accurate and Accessible Visual Evidence — create an Accessible Visual Evidence Specification.
- Specify a Decision-Focused Dashboard or Scorecard — create a Dashboard and Scorecard Design Brief.
- Build a Data Story from Claims and Supporting Analysis — create a Data Story Claim and Evidence Map.
- Present Findings, Options and a Recommendation — create a Decision Insight and Recommendation Memo.
- Deliver a Reproducible Handoff and Recurring Analysis Process — create a Reproducible Analysis Handoff Runbook.
Applied Capstone — Café Closing-Time Decision Analysis
The capstone places you in the role of analyst for Cedar Lane Cafés, a fictional six-location operator considering later closing at two sites. You receive synthetic transactions, staffing, refunds, promotions and hourly footfall, including a partial counter outage and an overlapping promotion.
Your task is to advise the operations director whether to run a four-week pilot at zero, one or both sites. You will prepare one Data Analysis Decision Pack containing the decision question, measures, preparation and quality note, reproducible calculations, proportionate statistical interpretation, accessible visuals, options, recommendation and follow-up evidence.
How the course works
The course is online and self-paced. A typical learner can complete it within one month, depending on study pace and the depth of practical work. The English-language lessons can be studied in sequence, and the twenty artifacts can be adapted to another permitted workplace or fictional context.
The connected Maple Street Markets case lets you practise a full analytical workflow with consistent synthetic facts. Standalone transfer scenarios then test whether you can apply each method without relying on the earlier case narrative.
AI-supported practice
Every lesson contains model-agnostic AI practice. Prompts help surface ambiguity, structure supplied evidence, critique formulas or queries, test claims and improve an artifact draft. Each workflow keeps the original sources, calculations and human review visible.
You remain responsible for the analysis. Follow your organisation's approved-tool, privacy, confidentiality and information-security rules. Verify definitions, dates, denominators, transformations, calculations, statistical interpretations and recommendations against authorized evidence before retaining them.
Certificate
The final Moodle section provides access to the course certificate and MTF Student ID. The certificate carries the exact title Professional Certificate in Data Analysis.
Evidence behind the course
The curriculum is grounded in structured analysis of 126 current public vacancies and a separate review of the modern data-analysis working cycle:
- Data Analysis Work in 2026: Evidence from 126 Vacancies examines recurring employer expectations across analytical framing, data preparation, spreadsheets, SQL, statistics, visualization, communication and reproducibility.
- The Data Analyst's 2026 Question-to-Decision Working Cycle connects those expectations to practical analytical work, responsible AI assistance and decision-ready delivery.
The supporting research dataset and method are preserved in the published Zenodo record.
Tuition and access
Tuition is €10, including applicable taxes. Enrollment is completed through the secure embedded checkout, and course access is provided through the MTF learning platform after successful enrollment.
The course is general professional education. It does not authorize architecture, security, privacy, legal, financial or production-release decisions and does not guarantee employment, promotion, salary or operational outcomes.
Start the course
Build a practical data-analysis workflow that turns a real question into evidence colleagues can review and use.