Data Science: Data Analysis

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, such as regularization, bagging, boosting, support vector machines, clustering and principal components analysis.

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:

Data Science: Programming with Python (Course code S17, 8-12 July 2024) Data Science: Statistical Programming with R (Course code S24, 8-12 July 2024)

Data Science: Multiple Imputation in Practice (Course code S28, 8-11 July 2024)

Data Science: Data Analysis (Course code S31, 15-19 July 2024)

Data Science: Network Science (Course code S37, 15-19 July 2024)

Data Science: Applied Text Mining (this course)

Data Science: Machine Learning with Python (Course code S70, 22-26 July 2024)

Data Science: Text Mining with R (Course code S41, 19-22 August 2024)

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.

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.

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