Data Science Curriculum

Data & Analytics

A structured, level-by-level path through Data Science. Start with Foundations of Data wrangling and Core methods in Exploratory 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 Science

6 weeks · about 24 hours

3 modules

Advanced

Advanced Data Science

8 weeks · about 32 hours

3 modules

Expert

Expert Data Science

10 weeks · about 40 hours

3 modules

Data Science Course Curriculum

9 modules across 3 levels.

Intermediate

Intermediate Data Science

Start with Data wrangling, Exploratory analysis and Statistical inference. Each module explains the idea, shows a worked example and ends with a short project, so Data Science 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 Data wrangling in your own words, with an example you chose yourself, and test it against the module checklist.

Learning Path

1

Foundations of Data wrangling

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

2

Core methods in Exploratory analysis

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

Prerequisites: Foundations of Data wrangling

3

Applying Statistical inference

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

Prerequisites: Core methods in Exploratory analysis

Practice Problems

EasyExplain Data wrangling to someone who has never studied the subject, using one worked example.
Solution: A strong answer defines data wrangling precisely, gives one concrete example, and names the limitation it runs into.
MediumExplain Exploratory analysis to someone who has never studied the subject, using one worked example.
Solution: A strong answer defines exploratory analysis precisely, gives one concrete example, and names the limitation it runs into.
HardExplain Statistical inference to someone who has never studied the subject, using one worked example.
Solution: A strong answer defines statistical inference precisely, gives one concrete example, and names the limitation it runs into.

Glossary

Data wrangling

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

Example: apply Data wrangling to a small, familiar case step by step, then check your reasoning against the walkthrough.

Exploratory analysis

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

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

Statistical inference

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

Example: apply Statistical inference to a small, familiar case step by step, then check your reasoning against the walkthrough.

About the Data Science curriculum

The Data Science curriculum is a complete, self-paced course in data & analytics, organised into 3 progressive levels and 9 modules. It opens with Foundations of Data wrangling and Core methods in Exploratory analysis, moves through Applying Statistical inference and finishes with expert-level work on Leading practice in Experiment design and Mastering Reproducible workflows.

Across the whole path you work through 27 individual topics — from Data wrangling and Hands-on: data wrangling at the start to Hands-on: reproducible workflows and Common pitfalls in reproducible workflows 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 Science path you should be able to work confidently with:

Data wrangling

Hands-on: data wrangling

Common pitfalls in data wrangling

Exploratory analysis

Putting exploratory analysis to work

Common pitfalls in exploratory analysis

Statistical inference

Hands-on: statistical inference

How the Data Science curriculum is structured

Intermediate 6 weeks · about 24 hours 3 modules 9 topics

Intermediate Data Science

Start with Data wrangling, Exploratory analysis and Statistical inference. Each module explains the idea, shows a worked example and ends with a short project, so Data Science becomes something you can do rather than something you have only read about.

Modules: Foundations of Data wrangling · Core methods in Exploratory analysis · Applying Statistical inference

  • Data wrangling: Data wrangling is introduced from first principles, with the few terms you actually need and an example that shows how the idea behaves in practice.
  • Hands-on: data wrangling: Data wrangling is where many Data Science learners first get stuck, so the module slows down, isolates the difficult step and rebuilds it with you.
  • Common pitfalls in data wrangling: Data wrangling is introduced from first principles, with the few terms you actually need and an example that shows how the idea behaves in practice.

Project: Write a one-page explanation of Data wrangling 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 Science

Build on the intermediate level through Regression models, Classification & metrics and Time series. The emphasis shifts from knowing the ideas to choosing between them — trade-offs, failure modes and the reasoning behind each decision.

Modules: In depth Regression models · Patterns and trade-offs in Classification & metrics · Scaling Time series

  • Regression models: Here Regression models becomes a design decision. You compare alternatives, justify a choice and test it against a harder problem.
  • Putting regression models to work: Regression models is pushed past the textbook case: competing approaches, their trade-offs and the situations where each one breaks down.
  • Checking your understanding of regression models: Here Regression models becomes a design decision. You compare alternatives, justify a choice and test it against a harder problem.

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

Expert 10 weeks · about 40 hours 3 modules 9 topics

Expert Data Science

Work at the edge of Data Science through Visualisation & storytelling, Experiment design and Reproducible workflows. The modules assume fluency and ask for judgement: designing, critiquing and defending work at a professional standard.

Modules: Frontiers of Visualisation & storytelling · Leading practice in Experiment design · Mastering Reproducible workflows

  • Visualisation & storytelling: At expert level Visualisation & storytelling is treated as a field of judgement: current practice, contested positions and the evidence behind each.
  • Hands-on: visualisation & storytelling: Visualisation & storytelling is examined at the frontier, with an emphasis on open problems, rigorous argument and decisions you can defend.
  • Common pitfalls in visualisation & storytelling: At expert level Visualisation & storytelling is treated as a field of judgement: current practice, contested positions and the evidence behind each.

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

How to study Data Science, phase by phase

  1. 1

    Phase 1 — Intermediate

    Set aside around 6 weeks · about 24 hours. Work through 3 modules, starting with Foundations of Data wrangling and Core methods in Exploratory analysis. Finish the phase by completing the project brief: Write a one-page explanation of Data wrangling 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 Regression models and Patterns and trade-offs in Classification & metrics. Finish the phase by completing the project brief: Produce a short case analysis in which Regression models 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 Visualisation & storytelling and Leading practice in Experiment design. Finish the phase by completing the project brief: Write a professional-standard brief on Visualisation & storytelling — 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 Data wrangling in your own words, with an example you chose yourself, and test it against the module checklist. without following along. Explaining Data Science to someone else in plain language is the clearest signal that the Expert level has landed.

Once you have finished, the Data Science 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 Science — Frequently Asked Questions

Common questions about studying Data Science on LearnAnyCourse.

What does the Data Science curriculum cover?

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The Data Science curriculum is organised into 3 levels (Intermediate, Advanced and Expert) and 9 modules, including Foundations of Data wrangling, Core methods in Exploratory analysis, Applying Statistical inference, In depth Regression models, Patterns and trade-offs in Classification & metrics and Scaling Time series.

How long does it take to learn Data Science?

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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 Science curriculum free?

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Yes. Every Data Science 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 Science?

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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 Science topics without jumping ahead.

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