2022/2023
Как победить в соревновании по анализу данных: учимся у лучших на платформе Kaggle
Статус:
Маго-лего
Когда читается:
2 модуль
Онлайн-часы:
40
Охват аудитории:
для всех кампусов НИУ ВШЭ
Преподаватели:
Развенская Ольга Олеговна
Язык:
английский
Кредиты:
3
Контактные часы:
6
Course Syllabus
Abstract
The study of this discipline is based on the following courses: • Machine learning • Data analysis methods To master the discipline, students must possess the following knowledge and competencies: • Programming method • Linear algebra Probability and statistics The main provisions of the discipline can be used in their professional activities. https://www.coursera.org/learn/competitive-data-science
Learning Objectives
- The purpose of the discipline is to get acquainted with modern methods of data analysis and ma-chine learning and their use in data analysis competitions
Expected Learning Outcomes
- Be able to choose the method of data processing and perform the data processing by the selected method
- Be able to choose the method of cross validation and evaluate the quality of the selected method of data processing
- Be able to solve the problems of data analysis competitions.
Bibliography
Recommended Core Bibliography
- Muller, A. C., & Guido, S. (2017). Introduction to machine learning with Python: a guide for data scientists. O’Reilly Media. (HSE access: http://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=4698164)
Recommended Additional Bibliography
- Witten, I. H. et al. Data Mining: Practical machine learning tools and techniques. – Morgan Kaufmann, 2017. – 654 pp.