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University of Leeds

Statistical Methods

University of Leeds via FutureLearn

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Overview

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Build your skills in R, statistics, and data visualisation for future study

Statistical analysis is vital for understanding and interpreting complex data in healthcare, science, and many other fields. In this course, you’ll explore the role of statistical methods in data analysis and begin developing essential data science skills using the R programming language.

Use real-life examples to explore data

You’ll examine the difference between data and information and understand the importance of statistical models in drawing meaningful conclusions. You’ll explore examples of data bias and misrepresentation to develop good statistical practices and critical thinking.

Get hands-on with RStudio for data visualisation

Learn to create numerical and graphical summaries of data using RStudio. You’ll clean data sets, identify outliers, and practise generating visualisations — all essential steps in effective exploratory data analysis.

Build skills to support further study in data science and genomics

This course is an ideal introduction if you’re interested in applying to the Online MSc Genomic Medicine with Data Science at the University of Leeds. It provides a practical taster of the statistical and analytical thinking you’ll build upon in the full Master’s programme.

This course is designed for professionals or students interested in applying statistics and data visualisation in real-world contexts.

It’s especially relevant for those exploring careers in data analysis, and fields such as bioinformatics, or health data science. It also offers a strong foundation for learners considering postgraduate study, such as the Online MSc Genomic Medicine with Data Science at the University of Leeds. While no prior experience with R is required, a basic understanding of statistics will be useful.

RStudio is need for this course. This can be downloaded at: https://posit.co/downloads/

Syllabus

  • The role of statistical models in data analysis
    • Welcome to the course
    • Activity 1: The role of statistics in data analysis
    • Activity 2: Statistical inference and probability
    • Activity 3: Data exploration and reflection
    • Week 1: Summary and quiz
  • The basics of exploratory data analysis
    • Activity 1: Data summaries
    • Activity 2: RStudio for data, graphical, and numerical summaries
    • Activity 3: Practising data summaries
    • Week 2: Summary and quiz
  • Explore and reflect: Random experiments and computer simulations
    • Activity 1: Computer simulations
    • Activity 2: Long simulations, measuring probability, and margin of error
    • Activity 3: Practising random experiments
    • Week 3: Summary and quiz

Taught by

Aysha Divan

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