456-0921/01 – Data Analysis Methods (MAD)
Gurantor department | Department of Computer Science | Credits | 0 |
Subject guarantor | doc. RNDr. Jana Šarmanová, CSc. | Subject version guarantor | doc. RNDr. Jana Šarmanová, CSc. |
Study level | postgraduate | Requirement | Choice-compulsory |
Year | | Semester | winter + summer |
| | Study language | Czech |
Year of introduction | 1997/1998 | Year of cancellation | 2010/2011 |
Intended for the faculties | FEI, HGF | Intended for study types | Doctoral |
Subject aims expressed by acquired skills and competences
Graduate Course
knows the basic theory, methods, types of data mining,
can be practically applied to real data from structured databases,
can analyze data from the sociological surveys, scientific experimental research,
can analyze data from data warehouses.
Teaching methods
Lectures
Individual consultations
Project work
Summary
Evaluation and interpretation of information obtained from the measured and recorded data from the practice. Methods of data mining, mathematical, statistical and logical methods for solving this class of research and practical problems. Methods of searching for associations, clustering methods, classification methods and some other methods.
Compulsory literature:
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition. Elsevier Inc., 2006, 770 p., ISBN 1-55860-901-3.
Recommended literature:
Dunham, M.H.: Data Mining. Introductory and Advanced Topics. Pearson Education, Inc., 2003, 315 p.
Way of continuous check of knowledge in the course of semester
Continuous assessment:
Treatment of the subject mining of the data.
Terms of the credit:
Meeting all three points of the follow-up studies, each at least 10 points.
E-learning
Other requirements
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
Lectures:
Defining the problem of multivariate data analysis.
Methods of data analysis: mathematical statistics and exploratory data analysis. The input data types of formal and semantic aspects. Filtration,
missing data, dichotomize, categorization
Preprocessing, transformation. Normalization and standardization. Principal components.
Cluster analysis, non-hierarchical methods, hierarchical methods, presentation and interpretation of results.
Finding associations, automatic creation of hypotheses, presentation and interpretation of results.
Decision tree construction, presentation and interpretation.
Exercise:
Practice methods of lectures on examples of specific data.
Papers on new methods of data mining.
Reports on the results of an analysis.
Projects:
Analysis of specific data from their own experience or from a database.
Preprocessing, selection of appropriate methods.
Own calculations, interpretation.
Presentation of results, documentation.
Computer Labs:
A system for data analysis, control methods, presentation of results, applications.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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