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Data · Beginner → Expert

📊 R Programming and Statistical Computing

Use R for data analysis, visualization, statistical modeling, reproducible research, reporting, packages, performance, and production analytical workflows.

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Course roadmap

Pass each module exam to unlock the next module.

Module 1 · Beginner

R Environment, Projects and Package Management

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Module 2 · Beginner

Vectors, Types, Objects and Indexing

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Module 3 · Beginner

Matrices, Lists, Factors and Data Frames

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Module 4 · Beginner

Functions, Scope and Functional Programming

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Module 5 · Intermediate

Data Import, Export and Tidy Data Concepts

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Module 6 · Intermediate

Data Transformation with Modern R Workflows

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Module 7 · Intermediate

Exploratory Data Analysis and Summary Statistics

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Module 8 · Intermediate

Visualization with Grammar-of-Graphics Concepts

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Module 9 · Advanced

Probability Distributions and Simulation

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Module 10 · Advanced

Hypothesis Testing and Confidence Intervals

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Module 11 · Advanced

Linear Models and Regression Diagnostics

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Module 12 · Advanced

Classification, Generalized Models and Model Evaluation

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Module 13 · Advanced

Time Series and Repeated Data Concepts

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Module 14 · Expert

Reproducible Reports, Notebooks and Parameterized Analysis

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Module 15 · Expert

Testing, Package Development and Documentation

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Module 16 · Expert

Performance, Vectorization and Profiling

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Module 17 · Expert

Expert Capstone: Reproducible Statistical Analysis Project

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Certification gate

Final course exam

Pass mark: 80%. Time limit: 360 minutes. All module exams must be passed first.

Locked until modules are passed