This course provides an introduction to the data science as a profession and focuses on the theoretical methodologies of the most widely applied machine learning models. The main topics covered include: data preparation (data mining, cleaning and exploring strategies), statistical modeling with the application of appropriate machine learning methodologies (data segmentation, predictive analytics, recommendation systems), and mathematical evaluation.
The objective of this course is to present and elucidate the basic concepts of Data Science. It can be best seen as a course that provides the foundations of machine learning and therefore heavily relies on econometrics and data management, and opens the door to other econometric courses, both applied and theoretical.
Prerequisites: Statistics and Econometrics at introductory levels, experience with at least one programming language for statistical data analytics (Python, R, Stata or others).
Main instructor of the course is Prague based Vahan Sargsyan, Ph.D. while students of the Faculty of Economics, Business and Tourism, Split will be co-instructed by Tea Kalinić, M.A. from the Faculty of Economics, Business and Tourism, Split. The classes will be followed in the classroom at the Faculty of Economics, Business and Tourism, Split along with local instructor of the individual course.
Classes will start on April 25 and will last till June 3, 2022.
- Lectures: Monday 7.00-8.30 a.m. CEST, Wednesday 7.00-7.45 a.m. CEST.
- Exercise session: Wednesday 7.45-8.30 a.m. CEST
Take a look at the detailed syllabus of the course.
To take part at this course please fill in the electronic application (opens in new window) ! by February 4th, 2022 by 12:00 h (noon).
For additional information contact us at email@example.com or call +385 21 430 706.