We offer a hands-on introduction to machine learning and data analysis in R for participants from diverse fields. You will learn advanced supervised and unsupervised methods and visualisation tools to analyse data, uncover patterns and make predictions. Working with real-life datasets, you will focus on implementing models, interpreting and evaluating their results properly.
Which treatment offers the best recovery chances at the lowest cost? Which pricing strategy keeps a business profitable without losing customers? Are some patients over- or under-insured given their health risks? How quickly is a new infectious disease likely to spread? What puts former criminals back on the right track? Can social media anticipate election outcomes? How many social classes does 21st-century society really have?
Questions like these can be tackled with statistical and machine learning approaches. They are the art and science of tackling questions like these by analyzing data. Just as cartographers create maps to reveal the shape of a landscape, data analysts use visualizations to uncover patterns and hidden structures within complex datasets. And just as doctors diagnose illnesses and guide patients toward better health, data analysts anticipate the consequences of actions and events so that informed decisions can be made. Methods from statistics, data mining and machine learning form the foundation of this process, enabling us to extract meaningful insights from data in a rigorous and systematic way.
In this course, you learn how to analyze data, make informative visualizations, uncover hidden structure and predict the consequences of actions and events. We use methods from statistics, data mining and machine learning, but the emphasis is on intuition and sound principles rather than mathematical derivations. The course has a strong practical character: you will work with real datasets and learn how to choose appropriate methods, implement them, and interpret and critically evaluate the results in real life.
Prerequisites
Important Notice
Applied researchers and master students from applied fields such as sociology, psychology, education, political science, public policy, quantitative criminology, human development, marketing, management, biology, medicine, computational linguistics, communication sciences. A maximum of 30 participants will be allowed in this course. Please note that the selection for this course will be done on a first-come-first-served basis.
For an overview of all our summer school courses offered by the Department of Methodology and Statistics please click here.
We also offer tailor-made M&S courses and in-house M&S training. If you want to look at the possibilities, please contact Dr. Laurence Frank at pe.dsai@uu.nl.
This course aims to provide you with hands-on experience applying classical as well as modern statistical and machine learning techniques using R for analysing data. More broadly, the course introduces participants to the core ideas, methods and workflows of statistical and machine learning, enabling them to confidently analyse real-world data, build and evaluate predictive models, discover meaningful structure in complex datasets and communicate their findings through clear visualisations.
After completing this course, participants will be able to:
Five full days. A typical course day starts at 9:00 hours and ends at 17:00 hours, with breaks for coffee and tea, lunch and sodas.
You will receive a certificate upon course completion. Please be aware that this course does not include graded activities, and therefore we cannot provide a transcript of grades.
This course has the following fee options, depending on your status:
Please make sure to include which price is applicable when registering for this course. This information can be added in the “Comment” field during the registration process.
For PhD students from the FSBS at UU:
As a PhD student from the Faculty of Social and Behavioural Sciences (FSBS) at Utrecht University, you can attend up to three Winter or Summer School courses funded by the Graduate School of Social and Behavioural Sciences. Of course, you may choose to take as many other courses as you wish at your own expense, using your personal budget.
When registering, please indicate in the “Comment” field that you are a PhD candidate from the FSBS at UU, so that the course fee can be waived.
There are no scholarships available for this course.
The housing costs do not include a Utrecht Summer School sleeping bag. This is a separate product on the invoice. If you wish to bring your own bedding, please deselect or remove the sleeping bag from your order.