Data Analytics Curriculum

Data & Analytics

A structured, level-by-level path through Data Analytics. Start with Foundations of Metrics & KPI design and Core methods in SQL for analysis at the Intermediate level, then build toward advanced practice and expert-level mastery — with hands-on projects and quizzes at every stage.

3 levels 9 modules 27 topics 6 weeks · about 24 hours · 8 weeks · about 32 hours · 10 weeks · about 40 hours
Intermediate

Intermediate Data Analytics

6 weeks · about 24 hours

3 modules

Advanced

Advanced Data Analytics

8 weeks · about 32 hours

3 modules

Expert

Expert Data Analytics

10 weeks · about 40 hours

3 modules

Data Analytics Course Curriculum

9 modules across 3 levels.

Intermediate

Intermediate Data Analytics

Start with Metrics & KPI design, SQL for analysis and Spreadsheets & BI tools. Each module explains the idea, shows a worked example and ends with a short project, so Data Analytics becomes something you can do rather than something you have only read about.

6 weeks · about 24 hours 3 modules

Topics

Project

Write a one-page explanation of Metrics & KPI design in your own words, with an example you chose yourself, and test it against the module checklist.

Learning Path

1

Foundations of Metrics & KPI design

Work through the three topics in this module, complete the project, then answer the self-check questions before moving on.

2

Core methods in SQL for analysis

Work through the three topics in this module, complete the project, then answer the self-check questions before moving on.

Prerequisites: Foundations of Metrics & KPI design

3

Applying Spreadsheets & BI tools

Work through the three topics in this module, complete the project, then answer the self-check questions before moving on.

Prerequisites: Core methods in SQL for analysis

Practice Problems

EasyExplain Metrics & KPI design to someone who has never studied the subject, using one worked example.
Solution: A strong answer defines metrics & kpi design precisely, gives one concrete example, and names the limitation it runs into.
MediumExplain SQL for analysis to someone who has never studied the subject, using one worked example.
Solution: A strong answer defines sql for analysis precisely, gives one concrete example, and names the limitation it runs into.
HardExplain Spreadsheets & BI tools to someone who has never studied the subject, using one worked example.
Solution: A strong answer defines spreadsheets & bi tools precisely, gives one concrete example, and names the limitation it runs into.

Glossary

Metrics & KPI design

A central idea in the intermediate Data Analytics path, covered in the module of the same name.

Example: apply Metrics & KPI design to a small, familiar case step by step, then check your reasoning against the walkthrough.

SQL for analysis

A central idea in the intermediate Data Analytics path, covered in the module of the same name.

Example: apply SQL for analysis to a small, familiar case step by step, then check your reasoning against the walkthrough.

Spreadsheets & BI tools

A central idea in the intermediate Data Analytics path, covered in the module of the same name.

Example: apply Spreadsheets & BI tools to a small, familiar case step by step, then check your reasoning against the walkthrough.

About the Data Analytics curriculum

The Data Analytics curriculum is a complete, self-paced course in data & analytics, organised into 3 progressive levels and 9 modules. It opens with Foundations of Metrics & KPI design and Core methods in SQL for analysis, moves through Applying Spreadsheets & BI tools and finishes with expert-level work on Leading practice in Forecasting basics and Mastering Communicating findings.

Across the whole path you work through 27 individual topics — from Metrics & KPI design and Putting metrics & kpi design to work at the start to Putting communicating findings to work and Common pitfalls in communicating findings by the end. The levels are estimated at 6 weeks · about 24 hours, 8 weeks · about 32 hours and 10 weeks · about 40 hours. Each level assumes the one before it, so you can work straight through from Intermediate to Expert, or start at the level that matches what you already know.

It suits analysts, researchers and students who need to reason with numbers and defend a conclusion, not just run a tool. The emphasis is on interpretation: every technique is introduced with the question it answers, the assumption it needs and the way it fails, so you can apply it to unfamiliar data.

Every module pairs a written summary with a worked example, a short quiz to check the idea landed, and a project brief drawn from real practice. You are never asked to take a concept on trust: the reasoning, the common mistakes and the situations where the approach stops working are all laid out, so you finish a module able to explain the idea rather than only recognise it.

What you’ll be able to do

By the end of the Data Analytics path you should be able to work confidently with:

Metrics & KPI design

Putting metrics & kpi design to work

Checking your understanding of metrics & kpi design

SQL for analysis

Hands-on: sql for analysis

Checking your understanding of sql for analysis

Spreadsheets & BI tools

Putting spreadsheets & bi tools to work

How the Data Analytics curriculum is structured

Intermediate 6 weeks · about 24 hours 3 modules 9 topics

Intermediate Data Analytics

Start with Metrics & KPI design, SQL for analysis and Spreadsheets & BI tools. Each module explains the idea, shows a worked example and ends with a short project, so Data Analytics becomes something you can do rather than something you have only read about.

Modules: Foundations of Metrics & KPI design · Core methods in SQL for analysis · Applying Spreadsheets & BI tools

  • Metrics & KPI design: This module treats Metrics & KPI design as a tool: the question it answers, what it assumes, and what a correct use of it looks like in Data Analytics.
  • Putting metrics & kpi design to work: Metrics & KPI design is introduced from first principles, with the few terms you actually need and an example that shows how the idea behaves in practice.
  • Checking your understanding of metrics & kpi design: This module treats Metrics & KPI design as a tool: the question it answers, what it assumes, and what a correct use of it looks like in Data Analytics.

Project: Write a one-page explanation of Metrics & KPI design in your own words, with an example you chose yourself, and test it against the module checklist.

Advanced 8 weeks · about 32 hours 3 modules 9 topics

Advanced Data Analytics

Build on the intermediate level through Dashboard design, Cohort & funnel analysis and Experiment analysis. The emphasis shifts from knowing the ideas to choosing between them — trade-offs, failure modes and the reasoning behind each decision.

Modules: In depth Dashboard design · Patterns and trade-offs in Cohort & funnel analysis · Scaling Experiment analysis

  • Dashboard design: Dashboard design is pushed past the textbook case: competing approaches, their trade-offs and the situations where each one breaks down.
  • Putting dashboard design to work: The module examines Dashboard design closely, including the assumptions beneath it and what changes when those assumptions stop holding.
  • Common pitfalls in dashboard design: Dashboard design is pushed past the textbook case: competing approaches, their trade-offs and the situations where each one breaks down.

Project: Produce a short case analysis in which Dashboard design drives the recommendation, including the alternatives you rejected and why.

Expert 10 weeks · about 40 hours 3 modules 9 topics

Expert Data Analytics

Work at the edge of Data Analytics through Data cleaning, Forecasting basics and Communicating findings. The modules assume fluency and ask for judgement: designing, critiquing and defending work at a professional standard.

Modules: Frontiers of Data cleaning · Leading practice in Forecasting basics · Mastering Communicating findings

  • Data cleaning: At expert level Data cleaning is treated as a field of judgement: current practice, contested positions and the evidence behind each.
  • Putting data cleaning to work: Data cleaning is examined at the frontier, with an emphasis on open problems, rigorous argument and decisions you can defend.
  • Common pitfalls in data cleaning: At expert level Data cleaning is treated as a field of judgement: current practice, contested positions and the evidence behind each.

Project: Write a professional-standard brief on Data cleaning — argument, evidence, limitations — plus a plan for taking the work further.

How to study Data Analytics, phase by phase

  1. 1

    Phase 1 — Intermediate

    Set aside around 6 weeks · about 24 hours. Work through 3 modules, starting with Foundations of Metrics & KPI design and Core methods in SQL for analysis. Finish the phase by completing the project brief: Write a one-page explanation of Metrics & KPI design in your own words, with an example you chose yourself, and test it against the module checklist.

  2. 2

    Phase 2 — Advanced

    Set aside around 8 weeks · about 32 hours. Work through 3 modules, starting with In depth Dashboard design and Patterns and trade-offs in Cohort & funnel analysis. Finish the phase by completing the project brief: Produce a short case analysis in which Dashboard design drives the recommendation, including the alternatives you rejected and why.

  3. 3

    Phase 3 — Expert

    Set aside around 10 weeks · about 40 hours. Work through 3 modules, starting with Frontiers of Data cleaning and Leading practice in Forecasting basics. Finish the phase by completing the project brief: Write a professional-standard brief on Data cleaning — argument, evidence, limitations — plus a plan for taking the work further.

  4. 4

    Phase 4 — Consolidate

    Return to the sections that felt weakest, redo their quizzes from memory, and rebuild the Write a one-page explanation of Metrics & KPI design in your own words, with an example you chose yourself, and test it against the module checklist. without following along. Explaining Data Analytics to someone else in plain language is the clearest signal that the Expert level has landed.

Once you have finished, the Data Analytics path is a reasonable foundation for analytics, data science, research and quantitative roles. Because the curriculum is generated and kept as a structured outline rather than fixed videos, you can revisit any module, regenerate the outline at a different depth, or save it to your library and work through it over weeks rather than in one sitting.

Data Analytics — Frequently Asked Questions

Common questions about studying Data Analytics on LearnAnyCourse.

What does the Data Analytics curriculum cover?

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The Data Analytics curriculum is organised into 3 levels (Intermediate, Advanced and Expert) and 9 modules, including Foundations of Metrics & KPI design, Core methods in SQL for analysis, Applying Spreadsheets & BI tools, In depth Dashboard design, Patterns and trade-offs in Cohort & funnel analysis and Scaling Experiment analysis.

How long does it take to learn Data Analytics?

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Each level is designed to take about 6 weeks · about 24 hours, 8 weeks · about 32 hours and 10 weeks · about 40 hours. Most learners work through one level at a time at their own pace.

Is the Data Analytics curriculum free?

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Yes. Every Data Analytics curriculum on LearnAnyCourse is free to read and study — create an account only if you want to save curricula to your personal library.

Do I need prior experience to study Data Analytics?

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The curriculum starts at the intermediate level and builds upward, so you can begin with the fundamentals and progress to expert-level Data Analytics topics without jumping ahead.

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