Course title: 
Programming Exercises for Data Science
Primary programme: 
Fizikus mérnök BSc
ECTS credits: 
Course type: 
Number of lectures per week: 
Number of practices per week: 
Number of laboratory exercises per week: 
Further knowledge transfer methods: 
Coursework grade
Special grading methods: 
Introduction to Data Science (may be in the same semester)
Responsible lecturer: 
Dr. Roland Molontay, assistant professor, PhD
Lecturers and instructors: 
Course description: 
The aim of the course is to introduce data science concepts and algorithms, which are less covered in the Introduction to Data Science course, in a practical approach based on the mathematical knowledge acquired previously. Subjects: Data manipulations Predictiv analysis Visualization with real datasets Python packages: pandas, scikit-learn, matplotlib, ggplot Introduction to R and other data sciece related tools Bayesian network Collective methods for clustering: random forest bagging, boosting Clustering: DBSCAN, EM algorithms Recommendation systems Assiciation rules Anomalies (outlier) detection Large scale case studies
Reading materials: 
Tan, Pang-Ning, Michael Steinbach, and Vipin Kumar. Introduction to data mining. 2005. Leskovec, Jure, Anand Rajaraman, and Jeffrey David Ullman. Mining of massive datasets. Cambridge University Press, 2014.
List of competences: 
Please find the detailed list, as quoted from the Hungarian training and outcome requirements of the Physicist Engineer program, in the Hungarian version of the course description.