460-4025/01 – Data Analysis Methods (MAD)
| Gurantor department | Department of Computer Science | Credits | 5 |
| Subject guarantor | prof. RNDr. Václav Snášel, CSc. | Subject version guarantor | prof. RNDr. Václav Snášel, CSc. |
| Study level | undergraduate or graduate | Requirement | Optional |
| Year | 1 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 2010/2011 | Year of cancellation | 2014/2015 |
| Intended for the faculties | FEI | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
Graduate Course gives the following knowledge and skills:
basic theoretical background for data analysis,
implementation and application of selected methods,
practical application to real data,
application of selected software packed to data analysis,
visualization and analysis results.
Teaching methods
Lectures
Individual consultations
Tutorials
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:
D. Skillicorn, Understanding Complex datasets: data mining with matrix decompositions, Chapman & Hall/CRC, 2007.
Han Jiawei; Kamber Micheline; Pei Jian, Data Mining, The Morgan Kaufmann Series in Data Management Systems, 3rd edition, 2011.
Recommended literature:
Eldén, L., Matrix Methods in Data Mining and Pattern Recognition, SIAM 2007.
T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer; Corr. 3rd edition, 2009.
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
Additional requirements are placed on the student.
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