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Scientific research methodology

Course developers

Course abstract

PhD students get knowledge about the general principles of scientific research implementation, research methods in solving scientific problems, their compliance with the research task and material, as well as the selection and implementation of the most appropriate method. The study course is focused on the principles of solving scientific problems and choosing methods in experiment planning, acquisition of empirical material and mathematical data processing, mathematical modeling of processes and systems. The course includes methods of mathematical modeling, optimization, mathematical statistics, data mining, survival analysis methods and time series analysis.

Learning outcomes and their assessment

After completing the course Ph.D. student will have:
• knowledge about research methods for emerging scientific theories and its application for PhD research on professional fields (during the practical classes the necessary mathematical methods for testing the hypothesis of the doctoral thesis are analyzed);
• skills individually evaluate and select mathematical methods for scientific researches of important, original and international cited research (during the practical classes the necessary mathematical methods for the experiment planning, acquisition of empirical material and mathematical data processing, mathematical modeling of processes and systems of the doctoral thesis have been selected);
• competences in collaboration with PhD supervisor individually perform critical analysis and evaluation. Solve important scientific or innovative tasks and individually propose research ideas (independent work has been developed and defended).

Course Content(Calendar)

1. Introduction to study course. Scientific research methodology course design and interaction with other courses. Types of research: basic research, applied research, experimental development. [L – 4h]
2. Use of various data processing tools in data processing: free software, commercial software, on-line tools. Free software R for data processing. [L – 2h, P – 2h]
3. Mathematical modelling methods: limits to growth theory, differential equations theory, dynamical systems theory, fractal theory, neural network theory, oscillators and waves theory. [L – 2h, P – 2h]
4. Mathematical modelling methods: category theory, reaction kinetics theory, set theory, membrane theory, systems theory, complex systems theory. [L – 3h, P – 1h]
5. Mathematical statistics methods. Method classification and application. [L – 2h, P – 2h]
6. Mathematical statistics methods. Parametric and nonparametric data analysis methods. Univariate and multivariate statistic. [L – 3h, P – 1h]
7. Time series analysis. Methods classification and application. Extrapolation and decomposition models. [L – 2h, P – 2h]
8. Time series analysis. Autoregressive integrated moving average processes ARIMA (p, d, q). Seasonal ARIMA models. Forecasting tasks and their role in decision making. [L – 3h, P – 1h]
9. Optimization methods. Methods classification and application. Linear programming method. [L – 2h, P – 2h]
10. Optimization methods. Network models and its elements. Transport optimization model. [L – 2h, P – 2h]
11. Data mining methods. Classification methods: decision tree, rule-based classification. Model selection and evaluation. WEKA - Data Mining with Open Source Machine Learning Tool. [L – 2h, P – 2h]
12. Survival analysis. Methods to describe the survival times of members of a group: Life tables, Kaplan-Meier curves, Survival function, Hazard function. [L – 3h, P – 1h]
13. Survival analysis. Method to compare the survival times of two or more groups - Log-rank test. Method to describe the effect of categorical or quantitative variables on survival - Cox proportional hazards regression. [L – 2h, P – 2h]
14. Introduction to experimental design. The principles of successful experiment. [L – 2h, P – 2h]
15. Experimental design methods. [L – 2h, P – 2h]
16. Defending of independent work. [P – 4 h]

Requirements for awarding credit points

Independent work have been developed and defended. Oral defense of a project at the end of the course. At least three different mathematical methods for the real data analysis should be used.

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

The assessment of learning outcomes depends on the degree of development of the independent work. To obtain the minimal assessment it is necessary to formulate and test the hypotheses using at least 3 mathematical methods, based on the data of the doctoral thesis, in experiment planning, acquisition of empirical material and mathematical data processing, mathematical modeling of processes and systems

Compulsory reading

1. Jiawei Han, Micheline Kamber. (2006) Data mining: concepts and techniques. San Fransisco: Morgan Kaufmann; Amsterdam [etc.]: Elsevier, 770 pp.
2. Michael P. Marder. (2011) Research methods for science. Cambridge; New York, NY: Cambridge University Press, 227 pp.
3. Joseph F. Hair, Jr., William C. Black, Barry J. Babin, Rolph E. Anderson (2014) Multivariate data analysis. Harlow, Essex: Pearson, 734 pp.
4. Robert I. Kabacoff (2015) R in action: data analysis and graphics with R. Shelter Island, NY: Manning, 579 pp.
5. Siegmund Brandt. (2014) Data analysis: statistical and computational methods for scientists and engineer. Springer, 523 pp.
6. Montgomery, Douglas C., Cheryl L. Jennings, and Murat Kulahci. (2015) Introduction to Time Series Analysis and Forecasting. 2nd ed. Hoboken, N.J.: Wiley-Interscience, Print. Wiley Ser. in Probability and Statistics.
7. Kim, S., Hosmer, D., & Lemeshow, S. (2002). Solutions manual to accompany Applied survival analysis : Regression modeling of time to event data [by] David W. Hosmer, Jr., Stanley Lemeshow / solutions manual authored by Sunny Kim. New York: Wiley-Interscience.

Further reading

1. John H. Schuenemeyer, Lawrence J. Drew. (2011). Statistics for Earth and Environmental Scientists. Hoboken, New Jersey: John Wiley & Sons, 407 pp.
2. Joseph F. Hair [et al.] (2010) Multivariate data analysis: a global perspective. Upper Saddle River [etc.]: Pearson, 800 pp.
3. Bunday, B. (1984). Basic optimisation methods / Brian D. Bunday. London: Edward Arnold.
4. Massimiliano Bonamente. (2013) Statistics and analysis of scientific data. New York: Springer, 301 pp.
5. Nathabandu T. Kottegoda, Renzo Rosso (2008) Applied statistics for civil and environmental engineers. Oxford; Malden, MA: Blackwell Publishing, 718 pp.
6. John W. Creswell. Research design: qualitative, quantitative, and mixed methods approaches. Thousand Oaks, Calif.: Sage, 2009, P.260.

Periodicals and other sources

1. Statistical Analysis and Data Mining.Hoboken, Wiley-Blackwell. ISSN:1932-1864. E-ISSN:1932-1872
2. Electronic Journal of Applied Statistical Analysis. ESE - Salento University Publishing. ISSN:2070-5948
3. International Journal of Data Mining, Modelling and Management. Inderscience. ISSN:1759-1163. E-ISSN:1759-1171

Notes

Obligatory course for PhD study programs Forest Science, Wood Materials and Technology, Veterinary Medicine, Civil Engineering, Landscape Architecture, Environmental Engineering, as well for other study programs if this course is included in the study plan.