The course deals with mining very large datasets, analysing them to make some descriptive summary of their content, test hypothesis, and extract valuable knowledge from them. Differently from other data mining courses, in this one we deal with datasets that for their large size, fast speed of updating, and variety of content (the so called Big Data) cannot be mined with standard techniques. Hence, the course will deal with topics such as: similarity measures for very large datasets and data streams, link analysis, clustering, recommender systems, MapReduce, etc. The course is also complemented by a practical part where, using statistical packages for Python (language that I will assume students already know), we will also learn how to perform practical analysis of large datasets and interpret, visualise, and diagnose results and potential problems of your data analysis.
- Required: Jure Leskovec, Anand Rajaraman and Jeffrey David Ullman. Mining of Massive Datasets (2nd edition). Cambridge University Press, 2014.
Master of Science in Informatics, Elective course, Lecture, 1st year
Master of Science in Informatics, Elective course, Lecture, 2nd year
PhD programme of the Faculty of Informatics, Elective course, Lecture, 1st year (4 ECTS)
PhD programme of the Faculty of Informatics, Elective course, Lecture, 2nd year (4 ECTS)