Academic Research
| Type of study | Doctoral |
|---|---|
| Language of instruction | English |
| Code | 157-0999/02 |
| Abbreviation | AV |
| Course title | Academic Research |
| Credits | 20 |
| Coordinating department | Department of Systems Engineering and Informatics |
| Course coordinator | prof. Mgr. Ing. František Zapletal, Ph.D. |
Subject syllabus
1. Publishing in research journals I
a. Information sources, citation databases.
b. Structure of the manuscript.
2. Publishing in research journals II
a. Review and publishing process.
c. Typesetting: MS Word vs. LaTeX.
d. Publication ethics.
f. Attachments to a manuscript (cover letter, highlights, graphical abstract).
3. LaTeX – professional typesetting (Overleaf)
a. Text organization and structure.
c. Math formulas.
d. Citations and references (bibtex).
e. Beamer for presentations.
4. Python – universal language for (not only) economic modelling
5. Decision-making models
a. Methodology of mathematical modelling.
b. Decision support models.
c. Different datatypes: random, uncertain, mssing data.
e. Modelling the preferences of a decision-maker.
f. Basic methods of multi-criteria decision-making.
6. Multi-criteria decision-making
a. Ranking and sorting.
b. Group decision-making.
c. Mathematical programming (Linear vs. non-linear models).
d. Stochastic programming.
e. Robust programming.
e. Monte Carlo simulation.
f. DEA models.
7. Advanced statistical models
a. Random variable and its description.
b. Hypothesis testing, statistical significance.
a. Selected statistical tests (ANOVA, tests in contingency tables).
8. Clustering methods and data reduction
a. Factor analysis.
b. Clustering analysis (hierarchical clustering, k-means).
c. Structural modelling (SEM).
9. Regression models I – estimates and parameters' forecasting
a. Linear regression.
b. OLS, Maximum likelihood, Generalized Method of Moments (GMM).
c. Quantile regression.
d. Logistic regression.
10. Regression models II
a. Panel regression.
b. Difference in differences (DD).
c. Event studies.
11. Regression models III
a. (S)VAR models.
b. VECM models.
c. Local projection.
12. Bayesian data analysis
a. Bayesian vs. classical approach to statistics.
b. Bayes theorem.
c. Bayesian inference in econometrics.
13. Selected areas of Computational Intelligence (AI)
a. Principles of neural networks.
b. Machine learning (ML) for forecasting.
c. Deep learning.
a. Information sources, citation databases.
b. Structure of the manuscript.
2. Publishing in research journals II
a. Review and publishing process.
c. Typesetting: MS Word vs. LaTeX.
d. Publication ethics.
f. Attachments to a manuscript (cover letter, highlights, graphical abstract).
3. LaTeX – professional typesetting (Overleaf)
a. Text organization and structure.
c. Math formulas.
d. Citations and references (bibtex).
e. Beamer for presentations.
4. Python – universal language for (not only) economic modelling
5. Decision-making models
a. Methodology of mathematical modelling.
b. Decision support models.
c. Different datatypes: random, uncertain, mssing data.
e. Modelling the preferences of a decision-maker.
f. Basic methods of multi-criteria decision-making.
6. Multi-criteria decision-making
a. Ranking and sorting.
b. Group decision-making.
c. Mathematical programming (Linear vs. non-linear models).
d. Stochastic programming.
e. Robust programming.
e. Monte Carlo simulation.
f. DEA models.
7. Advanced statistical models
a. Random variable and its description.
b. Hypothesis testing, statistical significance.
a. Selected statistical tests (ANOVA, tests in contingency tables).
8. Clustering methods and data reduction
a. Factor analysis.
b. Clustering analysis (hierarchical clustering, k-means).
c. Structural modelling (SEM).
9. Regression models I – estimates and parameters' forecasting
a. Linear regression.
b. OLS, Maximum likelihood, Generalized Method of Moments (GMM).
c. Quantile regression.
d. Logistic regression.
10. Regression models II
a. Panel regression.
b. Difference in differences (DD).
c. Event studies.
11. Regression models III
a. (S)VAR models.
b. VECM models.
c. Local projection.
12. Bayesian data analysis
a. Bayesian vs. classical approach to statistics.
b. Bayes theorem.
c. Bayesian inference in econometrics.
13. Selected areas of Computational Intelligence (AI)
a. Principles of neural networks.
b. Machine learning (ML) for forecasting.
c. Deep learning.
E-learning
Materials available in the LMS.
Literature
ANDERSON, David Ray. An Introduction to Management Science: Quantitative Approaches to Decision Making. Rev. 16th ed. Mason: South-Western Cengage Learning, 2022. ISBN 0357715462 .
GUJARATI, Damodar N. Essentials of Econometrics. Fifth edition. Thousand Oaks, California: SAGE, 2022. ISBN 978-1-0718-5039-8.
HAIR, Joseph F.; BLACK, William C.; BABIN, Barry J. a ANDERSON, Rolph E. Multivariate Data Snalysis. Eighth edition. Andover: Cengage, 2019. ISBN 978-1-4737-5654-0.
GUJARATI, Damodar N. Essentials of Econometrics. Fifth edition. Thousand Oaks, California: SAGE, 2022. ISBN 978-1-0718-5039-8.
HAIR, Joseph F.; BLACK, William C.; BABIN, Barry J. a ANDERSON, Rolph E. Multivariate Data Snalysis. Eighth edition. Andover: Cengage, 2019. ISBN 978-1-4737-5654-0.
Advised literature
HUMBLE, Steve. Quantitative Analysis of Questionnaires: Techniques to Explore Structures and Relationships. London: Routledge, Taylor & Francis Group, 2020. ISBN 978-0-429-68274-2 .
AJIBESIN, Adeyemi; VAJJHALA, Narasimha A. Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency, IGI Global, 2023. ISBN 13979-8369302552.
HILLIER, Frederick S. a LIEBERMAN, Gerald J. Introduction to Operations Research. 11th ed. New York: McGraw-Hill Higher Education, 2021. ISBN 978-1260575873 .
AJIBESIN, Adeyemi; VAJJHALA, Narasimha A. Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency, IGI Global, 2023. ISBN 13979-8369302552.
HILLIER, Frederick S. a LIEBERMAN, Gerald J. Introduction to Operations Research. 11th ed. New York: McGraw-Hill Higher Education, 2021. ISBN 978-1260575873 .