| Course Title | Data Mining |
| Course Code | M-100-04 |
| Level | Master |
| ECTS Credits | 6.00 |
| Faculty/unit | Engineering Faculty |
| Field | Computer and information sciences |
| Course Type | Standard |
| Course Leader | Jackiva Irina - Dr. sc. ing. professor |
| Annotation | This course introduces the core data mining concepts, techniques, algorithms, research issues and practical skills for applying data mining techniques to solve real-world problems. Topics include data understanding and visual data exploration, data preprocessing and transforming, data classification, data clustering etc.
Students will understand principles and concepts in data mining and get insight into data mining techniques and algorithms. Students will study the major data mining problems as different types of computational tasks (prediction, classification, clustering, etc.) and the algorithms appropriate for addressing these tasks. Will learn how to analyze data through statistical and graphical summarization, supervised and unsupervised learning algorithms. Students learn how systematically evaluate data mining algorithms and understand how to choose algorithms for different analysis tasks. Students are expected to do independent reading of research papers and to do critical review. |
| Aim | To provide students with practical and applied knowledge of how to conduct data mining activities. This includes key concepts in data mining as well as the statistical and modelling techniques necessary to analyse large data sets to generate meaningful intelligence |
| LO1 | Able to conduct a research in the IT domain, analyse data, state hypothesis, and make well-grounded conclusions and generalizations |
| LO2 | Apply effectively appropriate data analytics and statistical techniques on available data to discover new relations and deliver insights into research problem or organisational processes and support decision-making |
| LO3 | Verify data quality and veracity, to recognise value of data, to apply analytics and statistics methods for data preparation, pre-processing |
| LO4 | Use effective visualization and storytelling methods to create data analytics reports |
| Required Literature | - The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition (corrected) by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (January 2017, 764 pages).
- M.A. Bramer. (2016). Principles of Data Mining. A practical perspective. London, Springer https://link.springer.com/book/10.1007%2F978-1-4471-7307-6 - available in e-library of TSI
- Aggarwal, Charu C. (2015) Data Mining, Springer-Verlag. 734pp https://web.stanford.edu/~hastie/Papers/ESLII.pdf
- Intelligent Data Analysis : From Data Gathering to Data Comprehension, edited by Deepak Gupta, et al., John Wiley & Sons, Incorporated, 2020. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=6177671.
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| Assessment Method | There are two assessment elements, final examination and set of practical assignments. The practical assignments should be completed individually by each student. |
| Independent study | Independent study is organised around preparing Written Report and Presentation – students will be provided with a list of topics for individual student, they are required to produce an essay and presentation on their selected topic. |
| Full-time |
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| First Sit Elements | Element Weighting, % | Group Work | Links to Results |
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| Reports | 50 | | LO1, LO2, LO3, LO4 | | Presentations | 50 | | LO1, LO3, LO4 |
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