Бакалавриат 2023/2024

Современные технологии обработки статистических данных

Статус: Курс по выбору (Экономика и статистика)

Направление: 38.03.01. Экономика

Кто читает: Департамент статистики и анализа данных

Где читается: Факультет экономических наук

Когда читается: 4-й курс, 1 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для своего кампуса

Преподаватели: Кузин Сергей Сергеевич

Язык: английский

Кредиты: 3

Контактные часы: 28

Course Syllabus

Abstract

Data Mining is the creation of new knowledge to solve business problems, by using business knowledge to discover and interpret patterns in data. The course contents both methodology of the data mining process and practical guide to data preparation, modeling, and deployment analytics into business processes. It covers the following main types of modelling techniques: classification, regression, clustering, anomaly detection and association rules. Special attention will be given to the hands-on data analysis using available software tools.

Learning Objectives

• To introduce students to the concept of data mining process aimed at solving business problems. To provide knowledge of the basic data mining techniques. To gain practical skills in in data analysis, building models and evaluating their quality.

Expected Learning Outcomes

• Mastering clustering methods
• Mastering data preparation
• Mastering Regression.....
• Mastering the classification
• Mastering the detection of outliers
• Mastering the process of data mining
• Mastering the rules of the association

Course Contents

• 1. Data Mining process
• 2. Data preparation
• 3. Classification
• 4. Regression
• 5. Clustering techniques
• 6. Outlier detection. Feature selection and dimensionality reduction
• 7. Association rules

Assessment Elements

• Домашняя работа
• Домашняя работа
• Контрольная работа

Interim Assessment

• 2023/2024 учебный год 1 модуль
  0.33 * Домашняя работа + 0.33 * Домашняя работа + 0.34 * Контрольная работа

Bibliography

Recommended Core Bibliography

• Han, J., Kamber, M., Pei, J. Data Mining: Concepts and Techniques, Third Edition. – Morgan Kaufmann Publishers, 2011. – 740 pp.
• Robert Nisbet, John Elder, & Gary D. Miner. (2009). Handbook of Statistical Analysis and Data Mining Applications. Academic Press.

Recommended Additional Bibliography

• Toomey, D. (2014). R for Data Science. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=933765