Data Mining and Data Analysis Systems | Faculty of Technical Sciences

Subject: Data Mining and Data Analysis Systems (17.E2503)

Native organizations units: Sub-department for Applied Computer Science and Informatics

Type of studies	Title
Master Academic Studies	Information and Analytics Engineering (Year: 1, Semester: Winter)
Master Academic Studies	Software Engineering and Information Technologies (Year: 1, Semester: Winter)
Master Academic Studies	Computing and Control Engineering (Year: 1, Semester: Winter)
Master Academic Studies	Artificial Intelligence and Machine Learning (Year: 1, Semester: Winter)
Master Academic Studies	Information Engineering (Year: 1, Semester: Winter)

General information:

Category	Theoretical-methodological
Scientific or art field	Applied Computer Science and Informatics
ECTS	6

The aims of the course are: provide students with the knowledge of important concepts and techniques of data mining; make students capable of applying of data mining methods, tools and techniques.

Students are acquainted with the important concepts and techniques of data mining and capable of data analysis, predictive model creation, development and maintenance of data mining systems.

Basic concepts and overview of the field of DM. Exploratory data analysis and visualization. Basic techniques of classification: decision trees, naive Bayes method, k-nearest neighbors and support vector machines. Advanced classification techniques: the classifier ensembles, bagging, boosting, semi-supervised learning. Classifier evaluation, automatic determination of the parameter values ??and selection of attributes. Clustering techniques: k-means, hierarchical clustering, dbscan algorithm. Discovering association rules: apriori and FP-growth algorithm. Review of the application of data mining: analysis of business data, web data analysis, recommendation systems (films, books, etc.), predictions in sport.

Teaching methods include lectures, laboratory classes, homework assignments, and consultations. Lectures involve presenting the course materials using the necessary didactic tools while encouraging the students to participate actively. Laboratory classes (exercises) are realized through assignments that can be done independently or with the help of teaching assistants, as well as through homework assignments.

Authors	Title	Year	Publisher	Language
Berman, J., J.	Data Simplification	2016	Elsevier	English
Overton, J.	Going Pro in Data Science	2016	O Reilly	English
Whitney, H.	Data Insights	2012	Elsevier	English
Marz, N., Warren, J.	Big Data : Principles and best practices of scalable realtime data systems	2015	Manning Publications, New York	English
Talia, D., Trunfio, D., Marozzo, F.	Data Analysis in the Cloud	2015	Elsevier	English
Hogarth, M.	Data Clean-Up and Management	2012	Elsevier	English
Daniel T. Larose	Data Mining Methods and Models	2006	Wiley / IEEE Press	English
Elston, S. E.	Data Science in the Cloud	2016	O Reilly	English
Tan, P.N., Steinbach, M., Kumar, V.	Introduction to Data Mining	2006	Pearson, Boston	English
Provost, F., Fawcett, T.	Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking about Data Mining and Data-Analytic Thinking	2013	O’Reilly Media, Sebastopol	English