Elective courses for QEM - masters (course group defined by SGH Warsaw School of Economics)
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20221 - Winter semester 2022/23 20222 - Summer semester 2022/23 20231 - Winter semester 2023/24 20232 - Summer semester 2023/24 20241 - Winter semester 2024/25 20241-PRE - Preferences - Winter semester 2024/25 (there could be semester, trimester or one-year classes) |
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| 20221 | 20222 | 20231 | 20232 | 20241 | 20241-PRE | |||||||
| 232401-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) Classes are structured around the fundamental problems of labor economics, from the trade-off between leisure and consumption to the role of technological change. Other key topics include human capital formation, labor demand and remuneration. Besides these main organizing units, classes will include references to transversal topics, such as the role of labor market institutions and policies, and differences along worker?s characteristics. The course provides a formal treatment of labor market problems. Students are expected to be familiar with standard mathematical and statistical tools including: static and dynamic optimization, and fundamental notions of probability calculus and mathematical statistics. This class will consist of three different elements a) Presentation and discussion of theoretical models. b) Empirical verification of proposed mechanisms and its limitations. c) Discussion in classes by students. |
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| 233531-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) (1) S3 framework for object programming (attributes, attributes manipulation, overloading). (2) S4 framework for object programming (class, attributes, methods, generic methods, inheritance). (3) R5 framework for object programming (reference classes, class, attributes, methods, references, inheritance). (4) Practical applications of object oriented programming in R (analysis, methods, data). |
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| 234061-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) Theory of simulation, construction and verification techniques of simulation models. Dynamic stochastic simulation models - discreet event simulation. Sensitivity analysis for output random variables, model optimization techniques. Programming simulation models with Julia using Microsoft Visual Studio Code and Jupyter notebooks. |
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| 220621-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) Presentation of the basic artificial intelligence methods with implementation (in R language) and examples of use. Among the commonly known methods, genetic algorithms, classification trees, random forests, and artificial neural networks are discussed in more detail. Several alternative modelling methods are the basis of student designs. |
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| 237811-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) Basic C++ statements. Control statements and functions. Classes and objects. Input/output operations. Differences between C++ compilers. Graphics mode. C++ libraries which support and automate data processing for economic, business and data analysis purposes. |
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| 220521-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) The course introduces the R programming language. The first lab discusses installing and configuring R and selected editors. The next block of labs introduces basic data structures: vectors and lists, and data structures based on them: matrices, and data frames. The last block of labs introduces basic programming techniques, including conditional execution, looping and mapping, and functions. Some additional packages are also described. |
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| 230201-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) An approach to statistical inference based on the Bayes principle. Prior and posterior distributions. Bayesian estimation. Bayesian confidence intervals and tests. Time series models. Predictive distributions. Model choice. |
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| 223091-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) Within the subject the following topics will be discussed: technological elements of Big Data, data types, Big Data analytics, data streams processing, big data applications, data privacy and ethical issues. |
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| 224391-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) Cloud computing. Infrastructure-as-a-service (IaaS). Platform-as-a-service (PaaS). Collection and data management in the cloud. Relational databases and data warehousing in the cloud. NoSQL databases. Data analytic in the cloud . BigData - Hadoop and Spark int the cloud. Numerical and scientific computing in the cloud - grid computing. Economic fundamentals of cloud computing. Optimizing cloud computing costs. Cloud security. |
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| 223121-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) Classes are focused on teaching students advanced data mining and text mining techniques, including advanced methods of big data analysis. As a result of dynamic development of this interdisciplinary research a lot of effective algorithms were developed allowing automatically discovery of knowledge from databases, including unstructured data available in internet. This knowledge may be used in decision processes in business (including enterprises), science and technology. |
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| 236481-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) Introduction to PL/SQL, which is a procedural complement to SQL in Oracle ORDBMS. Please be informed that SQL basics required |
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| 231231-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) In the last several years an unprecedented development of derivatives market has been observed. Globalization of financial markets facilitated by the processes of deregulation and liberalization of national markets has been an enormous opportunity for the development of traditional and new derivatives. They are used for several purposes, but foremost for speculating and hedging on financial markets... |
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| 222121-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) See semester study programme. |
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| 222991-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) See semester study programme. |
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| 222041-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) See semester study programme. |
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| 233181-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) See semestral plan |
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| 235221-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) The main objectives of the course: - show actual problems and methods of risk management, - provide a methodology for dealing with currency risk, interest rate risk, credit risk and operational risk, - provide students with a good understanding of derivatives (forwards, futures, options and swaps), - show a methodology of pricing and valuation of derivatives, - provide students with techniques and methodologies that are commonly used in risk management. |
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| 233461-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) See semester study programme. |
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| 231571-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) Growth empirics. Exogenous growth models. First generation endogenous growth models. R&D-based models. Scale effects. Technology diffusion. Appropriate technology. Poverty traps. Topics in development economics. |
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| 231271-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) We discuss basic models of oligopoly and their application to clarify issues related to the classic strategic problems of firms as collusion, mergers, investments, pricing strategies, and product differentiation. |
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| 222111-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) See semester study programme. |
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| 234891-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) Recursive approach to dynamic economic models. Global vs local solutions. Curse of dimensionality. Value function iteration. Policy function iteration. Overlapping generations models. Discretization of continuous processes. Models with uninsured idiosyncratic risk. Perturbation techniques. |
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| 231291-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) Topics covered during the course "Macroeconomics and household heterogeneity" pertain to macroeconomic models which depart from the assumption about representative agent and, as such, are well-suited for studying redistributional effects of various economic processes. This class of models has recently become an important workhorse of the economic policy analysis. Course will cover both the theory underlying those models and their numerical implementation. |
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| 220341-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) see: detailed schedule of classes |
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| 234881-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) We will introduce standard models used at central banks and academia to study business cycle dynamics and monetary policy issues. First, standard ways of introducing money into general equilibrium models will be studied. Next, the sticky price new Keynesian model will be introduced. Apart from learning their features the models will be used to study most important issues in monetary economics, e.g. the optimal rate of inflation or the zero lower bound problem. |
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| 236811-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) Introduction to numerical analysis. Programming in Matlab and Octave. Examples of the application of numerical methods in artificial intelligence and various areas of science. Direct and iterative methods of solving systems of linear equations. Solving nonlinear equations. Newton's method. Monte Carlo methods. Recursion. |
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| 222131-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) 1. Introduction to the scope of PSE; 2. Fundamentals of welfare economics: - market efficiency & failures - efficiency & equity 4. Public expenduture theory - public gooods; - public choice; 5. Public expenditure growth 6. Expenditure programs: health care & social insurance 7. Budgets bureaucrats and efficiency; 8. Project appraisal; 9. Partial and general equilibrium analysis of taxation 10. Tax incidence, taxation and efficiency, optimal taxation |
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| 223101-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) Within the subject the following topics will be discussed: methods of data visualisation, visualisation tools, visual data exploration, visualisation of spatial data, building interactive visualisations. Subject contains lectures and practical exercises concerning visual data exploration and building different kind of visualisations. |
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| 222891-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) 1. From Flat Files to Data Mash: Data Processing Models in Big Data. 2. ETL and Batch (Offline Learning) and Incremental (Online Learning) Modeling. Map-Reduce. 3. Data Streams, Events, and Time and Time Window Concepts in Real-time Data Processing. 4. Microservices and Communication via REST API. 5. Contemporary Architectures for Stream Data Processing Applications - Lambda, Kappa, Pub/Sub. 6. Processing Structured and Unstructured Data. Programming Environment for Python. 7. Utilizing Python Object-Oriented Elements in the Modeling Process with Scikit-Learn and Keras. 8. Python Object-Oriented Programming Basics. Building Classes for Random Walk, Perceptron, and Adeline Algorithms. 9. Preparing a Microservice with an ML Model for Production Use. 10. Streaming Data Using RDDs with Apache Spark. Introduction to the DataFrame Object. 11. Methods for Creating Data Streams Using the DataFrame Object in Apache Spark. Setting Output and Input. 12. S |
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| 230891-D |
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Classes
Winter semester 2022/23
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Brief description
(in Polish) The methods of spatial econometrics allow to take account of interactions between investigated units in the modeling, i.e. remove the assumption of observational independence. The underlying concepts of this functional subdiscipline of econometrics will be presented: spatial weight matrices, testing of spatial effects, model specifications and consistent estimation methods, as well as spatial multipliers and some special models. The classes in computer laboratory will Focus on visualisation in maps and working with the models in consideration in the {spdep}/{spatialreg}/{splm} packages in R. |
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| 223491-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) Getting to know statistical algorithms of predictive model building used in decision making support. Practical aspects of model building: data collection and transformation, parameter estimation, prediction and decision making support based on models implemented in a programming language. Model Evaluation measurements and visualization. |
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| 230781-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) part A: decisions under certainty (example questions: What are preferences and utility? How to measure the intensity of preferences? How to consider many criteria at once? How to choose the right option from a menu?) part B: decisions under risk (What is risk? How to measure risk and attitudes towards risk? Does the rational man play the roulette, insure from the flood, place bets on the score of a sport event?) |
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| 222061-D |
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Classes
Winter semester 2022/23
Groups
Brief description
(in Polish) From general to specific strategy. Autocorelation and COMFAC analysis. Parameters changes. Dynamic models. ADL and ECM models. Modelling on the basis of time series generated by nonstationary stochastic processes. The Engle-Granger method. VAR i CVAR models. Cointegration space and testing. The Johansen method. Structuralisation of the CVAR model. Classical multivariate models. Multipliers. Analytical and numerical solution. Nonconvergence and ordering of the equations of the model. Symulation analyses. Forecasts based on multivariate models. |
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