Time Series Analysis with Python

2022/2023

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:

1 year, 3 module

Instructor

Razvenskaya, Olga

Course Syllabus

Abstract

The study of this discipline is based on the following courses: • Block of mathematical disciplines; • Block of programming discipline. To master the discipline, students must possess the following knowledge and competencies: • Programming skills; • Calculus, Linear algebra, Probability and Statistics. The main provisions of the discipline can be used in their professional activities. As a result of mastering the discipline the student will be able to (results): - use Python for data forecasting with different regression models; - use Python for classification and clustering with different algorithms; - use Python for recommendation systems algorithms. https://www.coursera.org/learn/competitive-data-science

Learning Objectives

• The purpose of the discipline is to get acquainted with programming tool Python for modern methods of data analysis and machine learning

Expected Learning Outcomes

• Be able to choose the method of data processing and perform the data processing by the selected method
• Be able to solve the problems of data analysis competitions
• Be able to use Python for recommendation systems algorithms

Course Contents

• Module 1 - Introduction to Machine Learning. Regression
• Module 2 - Classification and clustering
• Module 3 - Recommender Systems

Assessment Elements

• Итоговый тест
  Итоговый тест на знание основных терминов и понятий из курса
• Отчет

Interim Assessment

• 2022/2023 3rd module
  0.5 * Отчет + 0.5 * Итоговый тест

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

• Sarkar, D., Bali, R., & Sharma, T. (2018). Practical Machine Learning with Python : A Problem-Solver’s Guide to Building Real-World Intelligent Systems. [United States]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1667293

Authors

• BATSYN MIKHAIL VLADIMIROVICH
• RAZVENSKAYA OLGA OLEGOVNA
• KALYAGIN VALERIY ALEKSANDROVICH