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. The course has a strong practical focus; participants actively learn how to apply these techniques to real data and how to interpret their results. The course covers both classical and modern topics in data analysis.
What puts former criminals on the right track? How can we prevent heart disease? Can Twitter predict election outcomes? What does a violent brain look like? How many social classes does 21st century society have? Are hospitals spending too much on health care, or too little?
Statistical learning is the art and science of tackling questions like these by analysing data. 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.
Basic knowledge of the statistical software program 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 7 courses in the Summer School Data Science specialisation taught by UU’s department of Methodology & Statistics:
- Data Science: Programming with Python (Course code S17, 3-7 July 2023)
- Data Science: Statistical Programming with R (Course code S24, 3-7 July 2023)
- Data Science: Multiple Imputation in Practice (Course code S28, will not be available in 2023)
- Data Science: Data Analysis (This course)
- Data Science: Network Science (Course code S37, 10-14 July 2023)
- Data Science: Introduction to Text Mining with R (Course code S41, 10-13 July 2023)
- Data Science: Applied Text Mining (Course code S42, 17-21 July 2023)
Upon completing, within 5 years, 3 out of 7 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 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.
Aim of the course
This course aims to provide you with hands-on experience applying classical as well as modern statistical learning techniques, using R.
Five full days. A typical course day starts at 9.00 and ends at 17.00 with breaks for coffee, lunch and tea.
Please note that there are no graded activities included in this course. Therefore, we are not able to provide students with a transcript of grades. You will obtain a certificate upon completion of this course.
Tuition fee for PhD students from the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by the Graduate School of Social and Behavioural Sciences.
Utrecht Summer School does not offer scholarships for this course. This course is suitable for the STAP budget; if you are accepted to this course, you can request the correct STAP form via email@example.com.
Team M&S Summer School | E: firstname.lastname@example.org