Description of individual course units

Course unit title / Data Analysis
Programme / Joint doctoral study programme „Business Management”
Year of study
Academic year
Name of lecturer(s) / Irina Jackiva (Yatskiv), prof., Dr.sc.ing.
Credit points / 4 KP
Number of ECTS credits allocated (1KP = 1,5ECTS) / 6 ECTS
Language of instruction / English
Type of course unit (compulsory, optional) / Compulsory
Mode of delivery / Contact hours and independent studies
Aim of Course / This course introduces doctoral students to various modern data analysis concepts and technologies, with ascent on application a range of advanced statistical techniques and using software tools to implement it in process of research and also planning and taking business decisions. To develop an understanding of the strengths and limitations of data analysis techniques. Introduces to a class of methods known as data mining that assists researchers in recognizing patterns and making intelligent use of massive amounts of electronic data collected via the Internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines.
Preliminary knowledge / Probability Theory and Mathematical Statistics
Course contents /
  1. Introduction.Statistics -> Data Analysis -> Data Mining -> Data Science. Basic data mining tasks and techniques review. Big Data challenges.
  2. Data. Types of data: interval-scaled, binary, nominal, ordinal, mixed types. Exploratory analysis: measures of variability, heterogeneity, concentration, asymmetry, bivariate and multivariate analysis.
  3. Data Mining Process (CRISP-DM).Quality of data and its’ influence on result. Data pre-processing: data cleaning, integration and transformation, missing data, outliers, dimensionality reduction. Missing data and imputation methods.
  4. Data visualization. Multivariate data visualization in SPSS software.
  5. Unsupervised and supervised learning algorithms review. Measures of similarity of objects. Applications
  6. Cluster analysis. Hierarchical classification. K-means procedures. Validation of clustering results. Segmentation of clients (objects) on the basis cluster analysis
  7. Discriminant analysis as predictive techniques. Objectives of the discriminant analysis. Classification model. Fraud detection, scoring. Credit rating
  8. Dimensionality reduction problem and different methods of its solution. Method of principal components. Models of factor analysis. Benchmarking and composite indicator constructing on the basis of principal components method
  9. Multiple regression: model building, transformation of variables, interpretation of results.
  10. Spatial and Temporal Data.Time Series. ARIMA. Short-termforecast of transportation volume.
  11. ANOVA,MANOVA
  12. Advanced Methods and Applications.Web Mining. Profiling website visitors. Text Mining. Social Networks. Big Data.

Planned learning activities and teaching
methods / Course grade will be based:
-Quality of participation to classroom discussion (10%),
-Practical assignments (20%). The assignments will consist of actual analyses performed on the computer and presented in the form of a commented reports
-Case Study (10%).
-Independent work andpresentation (30%),
-Exam (30%).
Teaching methods / Study hours
(1KP = 40 hours)
Lectures / 30%
Practical Assignments / 20%
Independent work andpresentation / 20%
Exam / 10%
Work at the library, Case Study, Independent studies / 20%
160 hours
Learning outcomes of the course unit / 1) On successful completion of the course, doctoral students will be able
  • to know the key methods of classification, clustering, prediction and exploration in data analysis
  • for each of the methods to get a general understanding of how each method works, recognize why the method is appropriate to a particular research environment, understand how to perform the analysis using appropriate software and be able to interpret the results in a research context.
  • to be able to identify promising business and research applications of data analysis methods
  • to be able to data analysis task setting, to determine the appropriate techniques and to implement in their future research and business activities
  • to communicate in terms of the conventions of the discipline.
2) The capacity to critically read published research articles which make use of the techniques covered and an understanding of the role of data analysis in research, business and society. Contents may vary according to the interest of participants
3) Ability to apply the modern data analysis methods and technologies for practical research tasks solving and decision making.
Assessment methods and criteria / Study outcomes
The form of assessment / 1. / 2. / 3.
Practical assignments in a classroom / ● / ●
Independent work and its presentation / ● / ● / ●
Written examination / ● / ●
Recommended or required reading / Literature:
  1. Kamath, C. (2009)Scientific Data Mining. A practical perspective. SIAM.
  2. Giudici P., Figini S. (2009)Applied Data Mining for Business and Industry, Second edition, Wiley & Sons, Ltd.
  3. Data Classification Algorithms and Applications.Edited by Charu C. Aggarwal. Chapman & Hall/CRC, 2015.
  4. Landau, S., Everitt, B.S. (2003) A Handbook of Statistical Analyses Using SPSS: 1st (First) Edition. Chapman & Hall.
  5. Nisbet R., Elder J., Miner G., (2009)Handbook of Statistical Analysis & Data Mining Applications, Elsevier Inc.
  6. Berthold, M., Hand, D.J. (2003). Intelligent Data Analysis. N.Y.Springer-Verlag.
Additional literature:
  1. Michael J.A. Berry, Gordon S. Linoff. (2004) Data Mining Techniques For Marketing, Sales, and Customer Relationship Management Second Edition, Wiley Publishing, Inc.
  2. Bramer, V. (2007) Principles of Data Mining. Springer-Verlag.
  3. Yatskiv, I. (2005) Multivariate statistical analysis: classification and dimension reduction. Rīga, TSI, 2005. (in Russian)

Name of lecturerIrina Jackiva