Introduction to Data Science
Fizikus mérnök BSc
Number of lectures per week:
Number of practices per week:
Number of laboratory exercises per week:
Further knowledge transfer methods:
Special grading methods:
Introduction to Experimental Data Handling, Introduction to Machine Learning, Complex Networks
Dr. Roland Molontay, assistant professor, PhD
Lecturers and instructors:
The aim of the course is to introduce the basic concepts of data science in a practical approach, building on previously acquired mathematical knowledge. From the very beginning, students will experience the knowledge through real-life application examples, in a spiral They will acquire precise theoretical knowledge and at the same time practical hands-on knowledge in a progressively deeper and deeper way. The theoretical part of the course focuses on machine learning algorithms, while the practical exercises build on the knowledge of the Python language.
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.