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Research Methodology in Information Technologies

Course developer

Course abstract

The aim of the course is to acquire tasks of organization of fundamental and applied research starting from the determination of the problem and ending with data analysis and interpretation. The course covers questions of experiment planning and performance. the course includes analysis of probability and stochasticity in engineering sciences statistic, mathematic and graphic analysis of data and their application in particular research directions.

Learning outcomes and their assessment

• knowledge - is able to show that familiar with and understand the most advanced frontier of scientific theories and knowledge, manage research methodology and research methods of modern experimental design and experimental performance in information technology and at the interface between fields (1st test: Statistical, mathematical and graphical data analysis);
• skills - is able independently evaluate and select appropriate mathematical and statistical methods for scientific research in information technology, have made a contribution to the expansion of the knowledge limit or give new understanding of existing knowledge and their applications in practice, to implement a significant amount of original research, some of which are at the cited international publications level (2nd test: Experiment planning);
• competences - is able, making independent, critical analysis, synthesis and evaluation, to solve significant research tasks in the information technology field, alone to raise the research idea, plan and find a solution (presentation of the engineering experiment planning theory application for the doctoral thesis development during the period of individual studies and examinations).

Course Content(Calendar)

1. Research process: problem identification, data collection, hypothesis setting and testing [L-4h].
2. Distribution of continuous random variable. Normal distribution [L-2h;P-2h].
3. Gamma distribution. Gamma distribution special cases [L-2h;P-2h].
4. Beta distribution. Gamma distribution special cases [L-2h;P-2h].
5. Statistical models associated with the normal distribution: Log-normal distribution and Cauchy distribution [L-2h;P-2h].
6. Distribution of discrete random variable [L-2h;P-2h].
7. Approximation with empirical distributions. Johnson distribution. Pearson distribution [L-2h;P-2h].
8. Central limit theorem [L-2h;P-2h].
9. 1st test: Statistical, mathematical and graphical data analysis [P-4h].
10. Analysis of variance and some experiment planning issues [L-4h].
11. Correlation and regression analysis using in engineering sciences [L-2h;P-2h].
12. Measurement system design [L-2h;P-2h].
13. Methods of reliability statistical process control [L-2h;P-2h].
14. Experiment planning and trial sequence [L-2h;P-2h].
15. Statistical process control techniques [L-2h;P-2h].
16. 2nd test: Experiment planning [P-4h].

Requirements for awarding credit points

The practical works has been developed and two tests have been passed. Control of the theoretical part during the the period of individual studies and examinations.

Description of the organization and tasks of students’ independent work

The organization of independent work during the semester is independently studying literature, using academic staff member consultations.

Criteria for Evaluating Learning Outcomes

Learning outcomes assessment depends on the semester cumulative assessment (80%) and the theory (20%) during the period of individual studies and examinations.

Compulsory reading

1. John W. Creswell. (2009) Research design: qualitative, quantitative, and mixed methods approaches. Thousand Oaks, Calif.: Sage, 260 pp.
2. Jordan E., Silcock L. (2005) Beating IT Risks. Chichester: John Wiley & Sons, 292 pp.
3. Michael P. Marder (2011) Research methods for science. Cambridge; New York, NY: Cambridge University Press, 227 pp.
4. Moynihan T. (2002) Coping with IS/IT Risk Management: The Recipes of Experienced Project Managers. London: Springer, 328 pp.

Further reading

1. Hahn G. J., Shapiro S. S. (1994) Statistical Models in Engineering. A Wiley-Interscience Publication. John Wiley & Sons, INC, 347 pp. 2. Nathabandu T. Kottegoda, Renzo Rosso (2008) Applied statistics for civil and environmental engineers. Oxford; Malden, MA: Blackwell Publishing, 718 pp. 3. Mark Mitchell, Janina Jolley (2004) Research design explained. Belmont, CA: Wadsworth/Thomson Learning, 570 pp.

Periodicals and other sources

1. ACM Computing Surveys. SSN:0360-0300E-ISSN:1557-7341 2. Foundations and Trends in Machine Learning SSN:1935-8237; E-ISSN:1935-8245 3. Foundations and Trends in Communications and Information Theory. ISSN:1567-2190; E-ISSN:1567-2328 4. Computer Science Review. ISSN:1574-0137 5. Journal of Big Data E-ISSN:2196-1115

Notes

Compulsory course for LLU Faculty of Information Technologies doctoral progamme “Information Technologies”.