Time Series Analysis with Python

2022/2023

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Кафедра прикладной математики и информатики (Нижний Новгород) (Факультет информатики, математики и компьютерных наук (Нижний Новгород))

Статус:

Маго-лего

Когда читается:

3 модуль

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

• 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