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? When is a series of spikes in hundreds of website logfiles an operational problem?
Data analysis is the art and science of tackling questions like these by looking at data. Just as cartographers make maps to see what a country looks like, data analysts explore the hidden structures of data by creating informative pictures and summarizing relationships among variables. And just as doctors diagnose sick patients and advise healthy ones on how to stay healthy, data analysts predict important events and variables so we can act on this knowledge. Methods from statistics, machine learning, and data mining play an important part in this process, as well as visualizations that allow the analyst and other humans to better understand what we can conclude from the available facts.
During this course, participants will actively learn how to apply the main statistical methods in data analysis and how to use machine learning algorithms and visualizing techniques. The course has a strongly practical, hands-on focus: rather than focusing on the mathematics and background of the discussed techniques, you will gain hands-on experience in using them on real data during the course and interpreting the results.
This course covers both classical and modern topics in data analysis and visualization:
- Exploratory data analysis (EDA);
- Supervised machine learning and statistical learning;
- Unsupervised learning and data mining techniques;
- Visualization (throughout the course).
This course is essential as a basis for each track of the Master of Applied Data Science. If you want to register for this course, please also register
for the Applied Data Science profile. Students that need to follow this course mandatory for the ADS profile need to enroll themselves before the end of September (information on how and where will be provided within the profile). Other interested students can enroll themselves during the FSW Elective enrollment in the beginning of October, depending on the still available place in the course. Further information on this procedure can be found on the website
of one of the academic masters of the faculty of social and behavioural sciences.
Note also that if you are not an FSW student, the registration period may differ from your habitual one.
Please take notice: 7,5 EC instead of 5 EC.