The course Data science: Data Analysis offers a range of techniques and algorithms from statistics, machine learning and data mining to make predictions about future events and to uncover hidden structures in data.
NOTE: this course is almost fully booked!
The course covers both classical and modern topics in data analysis, such as regularization, bagging, boosting, support vector machines, clustering and principal components analysis. The course has a strong practical focus: participants actively learn how to apply these techniques to real data and how to interpret their results.
Just as cartographers make maps to see what a country looks like, data analysts make graphics that reveal hidden structures in the data. And just as doctors diagnose sick patients and advise healthy ones on how to stay healthy, data analysts predict the consequences of actions and/or events so we can act on that knowledge. Methods from statistics, data mining, and machine learning play an important part in this process.
The course has a strong practical character: the focus is not on the mathematics behind the methods but on the principles that make them work. Participants learn how to apply these methods to real data and how to interpret the results. The course covers both classical and modern topics in data analysis.
Prerequisities:
Basic knowledge of the statistical software programme R is required (e.g. of the level of the Summer School Data Science: Statistical Programming with R or the online e-book R for Data Science by Hadley Wickham).
Participants are requested to bring their own laptop computer. Software will be available online.
This course can be taken separately, but is also part of a series of 8 courses in the Summer School Data Science specialisation taught by UU’s department of Methodology & Statistics:
Upon completing, within 5 years, 3 out of 8 courses in the Summer School Data Science specialisation (no more than one text mining course), students can obtain a certificate.
Please see here for more information about the full specialisation.
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 60 participants will be admitted to 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 courses offered by the Department of Methodology and Statistics please click here.
This course aims to provide you with hands-on experience applying classical as well as modern statistical learning techniques, using R.
Learning goals:
- Knowledge of the available techniques
- A basic understanding of how they work
- Knowing how to apply them in practice
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.
PhD students from the Faculty of Social and Behavioural Sciences at Utrecht University have the opportunity to attend three Winter/Summer School courses funded by the Graduate School of Social and Behavioural Sciences. Additionally, they may choose to take as many courses as they wish at their own expense from their personal budget.
There are no scholarships available for this course.
We also offer tailor-made M&S courses and in-house M&S training. If you want to check out the possibilities, please contact us at ms.summerschool@uu.nl
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.
