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Time Series Analysis with Python

2022/2023
Учебный год
ENG
Обучение ведется на английском языке
3
Кредиты

Преподаватель


Развенская Ольга Олеговна

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

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

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

Course Contents

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

Assessment Elements

  • non-blocking Итоговый тест
    Итоговый тест на знание основных терминов и понятий из курса
  • non-blocking Отчет
Interim Assessment

Interim Assessment

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

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