Statistical Methods
Learn practical statistics and RStudio skills for applied analysis
Statistical Methods is a short, applied course that introduces statistical thinking and practical data visualisation using the R programming environment and RStudio. It emphasises statistical models, exploratory data analysis, and simulation-based reasoning delivered through hands-on RStudio exercises.
Designed for professionals and students seeking an accessible foundation in statistics, the course focuses on measurable skills such as cleaning datasets, producing numerical and graphical summaries, and interpreting results from simulations. Learners preparing for applied data roles or further study in areas like genomic medicine will find it a practical, skills-focused entry point into data analysis.
At a Glance
Statistical Methods is a short course from the University of Leeds that introduces statistical thinking and practical data visualisation using the R programming environment.
It covers statistical models, exploratory data analysis, and computer simulations through hands-on RStudio activities and real-life examples.
| Level | Beginner to Intermediate |
| Rating | 4.6 out of 5 |
| Duration | 3+ weeks |
| Languages | English |
| Certificate | Certificate available |
| Access | Free to join with optional paid upgrade for extended access and certificate |
| Course includes |
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| Price | Free to join with optional paid upgrade for certificate and extended access |
What This Course Teaches
The course frames outcomes as measurable competencies that emphasise statistical reasoning, practical RStudio skills, and experimental validation through simulations. By the end of the course learners will be able to explain statistical models for inference, apply RStudio to produce and interpret numerical and graphical summaries, investigate frequency stability with computer simulations, and apply peer review to improve data analyses.
How the Course Is Structured
Statistical Methods is organised as a short, week-by-week programme that groups focused activities and reflective summaries under three weekly themes.
The syllabus lists 16 sections in total across three weeks, combining short topical steps, hands-on RStudio activities, and end-of-week quizzes.
Curriculum overview
01The role of statistical models in data analysis▾
02Welcome to the course▾
Introduces the course aims and the context for building statistics and probability expertise with practical R activities.
03Activity 1: The role of statistics in data analysis▾
Explores how statistical thinking transforms raw data into information and supports data-driven decision making.
04Activity 2: Statistical inference and probability▾
Covers probability models used for statistical inference to handle variability and uncertainty in data.
05Activity 3: Data exploration and reflection▾
Focuses on exploratory approaches and discusses data privacy, security, and governance considerations for analysis.
06Week 1: Summary and quiz▾
Consolidates Week 1 topics and checks understanding with formative assessment.
- Quiz: Week 1
- Exercise solutions
07The basics of exploratory data analysis▾
08Activity 1: Data summaries▾
Introduces descriptive statistics and common tools for summarising numerical and categorical data.
09Activity 2: RStudio for data, graphical, and numerical summaries▾
Demonstrates how to use RStudio to create both graphical and numerical summaries for exploratory analysis.
10Activity 3: Practising data summaries▾
Hands-on RStudio exercises to explore datasets, modify example code, and identify key data features.
11Week 2: Summary and quiz▾
Reviews the week’s exploratory analysis techniques and verifies comprehension.
- Quiz: Week 2
12Explore and reflect: Random experiments and computer simulations▾
13Activity 1: Computer simulations▾
Introduces simulation techniques that build intuition about data-generating processes and summary statistics.
14Activity 2: Long simulations, measuring probability, and margin of error▾
Examines how increasing simulated instances affects estimated probabilities and empirical margin of error.
15Activity 3: Practising random experiments▾
Final practical exercises using R to simulate experiments and solidify simulation-based reasoning.
16Week 3: Summary and quiz▾
Wraps up the course and points learners to further study options in data science and genomics.
- Quiz: Week 3
Audience & Requirements
Statistical Methods targets professionals and students who want practical statistical reasoning and data-visualisation skills in R, with particular relevance to data analysis roles, health-data or bioinformatics work, and learners preparing for further study in genomic medicine.
It also suits early-career analysts and career changers seeking hands-on RStudio practice without prior R experience.
- Students or professionals building foundational skills in statistics and data visualisation.
- Early-career data analysts or career changers preparing for applied data roles.
- Health-data and bioinformatics practitioners who need basic statistical tools for genomic or clinical datasets.
- Learners considering postgraduate study such as an online MSc in Genomic Medicine with Data Science.
- RStudio installed and available for hands-on exercises.
- A basic understanding of statistics and probability is useful.
- No prior R experience required; willingness to run and modify R code.
Final Verdict
Statistical Methods is a practical, low-risk introduction to statistical thinking and hands-on RStudio work that is well suited for learners who want a grounded, applied entry into data analysis. Given its strong platform rating and a free-to-join model with an optional paid certificate, it offers good value for anyone seeking a concise, practical taster without a large financial commitment.
The course is especially worthwhile for learners preparing for further study or for early-career analysts who need focused R practice; it is not a substitute for a full statistics degree but serves as an effective stepping stone. If you want a short, applied course to build confidence with R and core inference concepts, this is a sensible, evidence-backed choice.
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