Магистратура
2022/2023
Анализ временных рядов в Python
Статус:
Курс обязательный (Интеллектуальный анализ данных)
Направление:
01.04.02. Прикладная математика и информатика
Когда читается:
1-й курс, 3 модуль
Формат изучения:
с онлайн-курсом
Онлайн-часы:
40
Охват аудитории:
для всех кампусов НИУ ВШЭ
Преподаватели:
Развенская Ольга Олеговна
Прогр. обучения:
Интеллектуальный анализ данных
Язык:
английский
Кредиты:
3
Контактные часы:
6
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
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