Магистратура
2026/2027
Интеллектуальный анализ данных
Статус:
Курс обязательный (Когнитивные науки и технологии: от нейрона к познанию)
Кто читает:
Институт когнитивных нейронаук
Где читается:
Институт когнитивных нейронаук
Когда читается:
1-й курс, 1, 2 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Тюкина Татьяна Александровна
Язык:
английский
Кредиты:
6
Контактные часы:
56
Course Syllabus
Abstract
The course “Introduction to Data Mining” aims to introduce masters graduate students to main topics of data analysis (classification, clustering, regression, dimensionality reduction), key algorithms (such as k Nearest Neighbours, Decision Trees, k means, Principal Component Analysis, Time Series analysis etc.),
Materials discussed in the lectures will be accompanied by the hands-on practical sessions. Particular attention is paid to the modern data analysis libraries of Python programming language.
Learning Objectives
- Gain an understanding of the fundamental algorithms of Data Mining: classification, clustering, time series analysis, neural networks, dimensionality reduction
- Learn about different methods for data preprocessing, and analysis of algorithms performance
- Develop programming skills using Python libraries for data analysis and visualisation, such as numpy, sklearn, pandas, matplotlib and seaborn
Expected Learning Outcomes
- Gain an understanding of the fundamental algorithms of Data Mining: classification, regression, clustering, time series analysis, neural networks, dimensionality reduction;
- Learn about different methods for data preprocessing, and analysis of algorithms performance
- Be able to build a pipeline for data analysis using Python.
- Know main challenges and best practices in Data Mining.
Course Contents
- Introduction - Введение
- Preprocessing - Методы предобработки данных
- Classification algorithms - Методы классификации.
- Regression algorithms - Алгоритмы регрессионного анализа
- Clustering Algorithms - Алгоритмы Кластеризации
- Dimensionality Reduction - Методы уменьшения размерности
- Time Series Analysis - Методы обработки временных рядов
- Challenges and best practices - Вызовы и передовой опыт
Assessment Elements
- Attendance and Engagementattendance and engagement - for a full mark students should attend at least 80% of the sessions
- Homeworksan average score on the homeworks (HWs) - a portfolio of homeworks consists of 8 hand-on coding assignments, the final score will be calculated as an averages of the best 6 marks.
- Final Examthe final exam is a written exam consisting of 2-3 theoretical questions
Interim Assessment
- 2026/2027 2nd module0.5 * Final Exam + 0.1 * Attendance and Engagement + 0.15 * Homeworks
Bibliography
Recommended Core Bibliography
- 9781787129566 - Layton, Robert - Learning Data Mining with Python, Second Edition - 2017 - Packt Publishing - http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1534825 - nlebk - 1534825
- C.R. Rao. (2005). Data Mining and Data Visualization: Vol. 1st ed. North Holland.
- Chu, W. W. (2013). Data Mining and Knowledge Discovery for Big Data : Methodologies, Challenge and Opportunities. Heidelberg: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=643546
- Kantardzic, M., & Recorded Books, I. (2019). Data Mining : Concepts, Models, Methods, and Algorithms (Vol. Third edition). [Place of publication not identified]: Wiley-IEEE Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2282578
- King, R. S. (2015). Cluster Analysis and Data Mining : An Introduction. Mercury Learning & Information.
- Larose, D. T., & Larose, C. D. (2015). Data Mining and Predictive Analytics. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=958471
- Larose, D. T., Larose, C. D. Discovering knowledge in data: an introduction to data mining. – John Wiley & Sons, 2014. – 336 pp.
- Myatt, G. J., & Johnson, W. P. (2014). Making Sense of Data I : A Practical Guide to Exploratory Data Analysis and Data Mining (Vol. Second edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=809795
- Nisbet, R., Miner, G., & Yale, K. (2017). Handbook of Statistical Analysis and Data Mining Applications: Vol. Second edition. Academic Press.
- Simoff, S. J., Böhlen, M. H., & Mazeika, A. (2008). Assisting Human Cognition in Visual Data Mining. Springer. https://doi.org/10.5167/uzh-56371
- Witten, I. H. et al. Data Mining: Practical machine learning tools and techniques. – Morgan Kaufmann, 2017. – 654 pp.
- Yang, X.-S. (2019). Introduction to Algorithms for Data Mining and Machine Learning. Academic Press.
Recommended Additional Bibliography
- The elements of statistical learning : data mining, inference, and prediction, Hastie, T., 2017