Statistical Methods
Learn key statistical methods to confidently analyse and visualise data, and unlock insights to support data-driven decisions.
Duration
3 weeksWeekly study
3 hoursLanguages
English, Italiano, Español, Français, PortuguêsEnglish, Italiano, Español100% online
How it worksDigital upgrade
Free
Statistical Methods
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.
Syllabus
Week 1
The role of statistical models in data analysis
Welcome to the course
Build your statistics and probability expertise with this short course from the University of Leeds.
Activity 1: The role of statistics in data analysis
The role of statistics in data analysis
Activity 2: Statistical inference and probability
Learn how probability models for statistical inference help you to deal with data variability and uncertainty.
Activity 3: Data exploration and reflection
Data privacy, security, data-driven decisions and legislation for these issues.
Week 1: Summary and quiz
This section includes a quiz to check your understanding , the exercise solutions for the week, and provides an overview of what you learned in Week 1.
Week 2
The basics of exploratory data analysis
Activity 1: Data summaries
Explore the basics of descriptive statistics and tools for summarising data.
Activity 2: RStudio for data, graphical, and numerical summaries
How to use RStudio for data, graphical, and numerical summaries.
Activity 3: Practising data summaries
In this RStudio activity, you'll gain practical R skills by exploring datasets and creating numerical and graphical summaries. You'll use statistical tools and code blocks to modify commands; to identify key features in the data
Week 2: Summary and quiz
Week 2: Summary and quiz
Week 3
Explore and reflect: Random experiments and computer simulations
Activity 1: Computer simulations
This activity will provide you with key background knowledge of data and data summaries.
Activity 2: Long simulations, measuring probability, and margin of error
What will happen if we increase the number of simulated instances of a random experiment? How can we use simulations to 'measure' probability and margin of error?
Activity 3: Practising random experiments
In this final activity of the course, you focus on practising your R skills, making simulations of random experiments.
Week 3: Summary and quiz
Discover how you can further develop your knowledge and skills in data science by enrolling in the University of Leeds' online MSc Data Science (Statistics) programme.
When would you like to start?
Start straight away and join a global classroom of learners. If the course hasn’t started yet you’ll see the future date listed below.
Available now
Learning on this course
On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.
What will you achieve?
By the end of the course, you‘ll be able to...
- Explain the role of statistical models in inference from data.
- Apply appropriate tools for numerical and graphical summaries using RStudio, and interpret the results.
- Investigate the stability of frequencies in computer simulations through experimental justification and 'measurement' of probability.
- Improve your data analysis skills by engaging with peer review as a learning activity.
Who is the course for?
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.
What software or tools do you need?
RStudio is need for this course. This can be downloaded at: https://posit.co/downloads/
Who will you learn with?
Who developed the course?
University of Leeds
As one of the UK’s largest research-based universities, the University of Leeds is a member of the prestigious Russell Group and a centre of excellence for teaching.
What's included?
University of Leeds are offering everyone who joins this course a free digital upgrade, so that you can experience the full benefits of studying online for free. This means that you get:
Learning on FutureLearn
Your learning, your rules
- Courses are split into weeks, activities, and steps to help you keep track of your learning
- Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities
- Stay motivated by using the Progress page to keep track of your step completion and assessment scores
Join a global classroom
- Experience the power of social learning, and get inspired by an international network of learners
- Share ideas with your peers and course educators on every step of the course
- Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others
Map your progress
- As you work through the course, use notifications and the Progress page to guide your learning
- Whenever you’re ready, mark each step as complete, you’re in control
- Complete 90% of course steps and all of the assessments to earn your certificate
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