Course title:
Programming Exercises for Data Science
Primary programme:
Fizikus mérnök BSc
ECTS credits:
2
Course type:
compulsory
Number of lectures per week:
0
Number of practices per week:
1
Number of laboratory exercises per week:
0
Further knowledge transfer methods:
Grading:
Coursework grade
Special grading methods:
Semester:
6
Prerequisites:
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.
https://www.w3schools.com/python/pandas/default.asp
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.