How to Win a Data Science Competition: Learn from Top Kagglers

2024/2025

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of Applied Mathematics and Informatics (Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod))

Course type:

Compulsory course

When:

2 year, 2 module

Instructor

Миленькин Александр Анатольевич

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.

Course Contents

• Data processing
• Methodology of cross validation
• Completions in data analysis

Assessment Elements

• Экзамен
• Соревнование на Kaggle

Interim Assessment

• 2024/2025 2nd module
  0.4 * Соревнование на Kaggle + 0.6 * Экзамен

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.

Authors

• Milenkin Aleksandr Anatolevich