157-0999/02 – Academic Research (AV)
| Gurantor department | Department of Systems Engineering | Credits | 20 |
| Subject guarantor | doc. Mgr. Ing. František Zapletal, Ph.D. | Subject version guarantor | doc. Mgr. Ing. František Zapletal, Ph.D. |
| Study level | postgraduate | Requirement | Compulsory |
| Year | | Semester | winter + summer |
| | Study language | English |
| Year of introduction | 2024/2025 | Year of cancellation | |
| Intended for the faculties | EKF | Intended for study types | Doctoral |
Subject aims expressed by acquired skills and competences
The Academic Research course has two main objectives, both of which are to facilitate the beginning of a research career for aspiring PhD students. The first objective is to introduce students to the basic aspects of scientific research publishing (R&D evaluation in the Czech Republic, data sources, structure of the scientific article and the publication process). The second objective is to introduce students to selected quantitative methods, their software support and applications in economics.
Teaching methods
Lectures
Individual consultations
Project work
Summary
The Academic Research course introduces students to the principles of academic writing and all phases of the publishing process. It also introduces selected quantitative methods used across economic disciplines and the software support for these methods.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
The student prepares a semester project in the form of a scientific article using quantitative methods and then defends it before a committee.
E-learning
Materials available in the LMS.
Other requirements
Students are required to attend full-time classes. During the lectures, they work out assignments on the discussed topic according to the teacher's instructions.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
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.
d. Stochastic 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 Artificial Intelligence (AI)
a. Principles of neural networks
b. Machine learning (ML) for forecasting.
c. Deep learning
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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