Basics of Logistics in SAP ERP
20
Both
Intermediate
Wrocław
Computer test
English
Basics of Logistics
The aim of the course is to introduce basic transactions of SAP ERP
system. Main topics:
1. Introduction to SAP ERP – installing the client, user interface,
navigation
2. Material Management
3. Production Planning
4. Sales and Distribution
Rising demand for centralized information in the contemporary
Learning
companies results in growing interest in integrated information systems.
outcomes:
One of the best known solutions from this field is the SAP ERP system.
Basic knowledge of this system is more and more often one of the
important requirements in the recruitment procedure.
After completion of this course student will be able to:
1. Navigate in SAP ERP user interface
2. Use SAP Workplace
3. Do basic operations from the field of logistics
4. Find additional information about transactions in SAP ERP
Contact person: Marek Kośny, e-mail: marek.kosny@ue.wroc.pl
Michał Jakubiak, e-mail: michal.jakubiak@ue.wroc.pl
Dowling K.N., SAP project system handbook, McGraw Hill, 2008.
Literature:
Mazzullo J., Wheatley P., SAP R/3 for Everyone: Step-by-Step
Instructions, Practical Advice, and Other Tips and Tricks for Working
with SAP, Prentice Hall, 2005
All
Faculty:
czy przedmiot jest tak nazwa przedmiotu: Systemy informatyczne w logistyce - system R3
wydział: Zarządzania, Informatyki i Finansów
kopią przedmiotu
kierunek: Zarządzanie
prowadzonego na
specjalność: Logistyka
UE?
rok: III (LS)
Title:
Lecture hours:
Study period:
Level:
Location:
Examination:
Language:
Prerequisites:
Course content:
Title:
Lecture hours:
Study period:
Level:
Location:
Examination:
Language:
Prerequisites:
Course content:
Economic analyzes using macros in Excel
30
Both
Intermediate
Wrocław
Computer test
English
Econometrics
The aim of the course is to introduce basics of programming in MS Excel
using Visual Basic for Applications. Main topics:
5. Visual Basic for Applications – language basics.
6. UserForms – definition, controls and event handlers.
7. Calculation of estimation errors using bootstrap procedure.
8. Portfolio analysis with automatized data collection.
The increase in computational complexity and size of data sets analyzed
Learning
by companies forces the automation of analytical processes. The use of
outcomes:
the extension of MS Excel, which is a VBA, allows the realization of an
intermediate solution between the ready-made systems with pre-defined
functionality and - usually very expensive – dedicated programs.
After completion of this course student will be able to:
5. Write and modify simple programs (macros) in Visual Basic for
Applications.
6. Create UserForms and manage their functionality.
7. Apply VBA to perform economic analyzes.
Contact person: Marek Kośny, e-mail: marek.kosny@ue.wroc.pl
Barreto H., Howland F., Introductory Econometrics: Using Monte Carlo
Literature:
Simulation with Microsoft Excel, Cambridge University Press, 2005
Bovey R., Wallentin D., Bullen S., Green J., Professional Excel
Development: The Definitive Guide to Developing Applications Using
Microsoft Excel, VBA, and .NET, Addison-Wesley Professional, 2nd
edition, 2009.
Walkenbach J., Excel 2010 Power Programming with VBA, Wiley, 2010.
All
Faculty:
czy przedmiot jest tak nazwa przedmiotu: Analizy rynkowe z wykorzystaniem VBA
wydział: Zarządzania, Informatyki i Finansów
kopią przedmiotu
kierunek: Analityka gsopodarcza
prowadzonego na
specjalność: Analityk rynku
UE?
rok: III
Title:
Lecture hours:
Study period:
Level:
Location:
Examination:
Language:
Prerequisites:
Course content:
Data Analysis in R Environment
10 h. lectures, 18 h. labs.
one semester (winter or summer)
basic
Wrocław (Jelenia Góra also possible)
Tests for lecture grades, own projects for labs grades.
English (for labs German as second language)
Information technologies at basic level
 Basic issues of data analysis. objects and variables, sata matrix and
an array of data;

Measure scales, normalization, data sources;

Classification of data analysis methods;

R statistical software platform;

Cluster analysis in R;

Multidimensional scaling in R;

Factor analysis in R;

Linear ranging in R;
Learning
outcomes:
Contact person:
Literature:

Decision trees in R;

Neural networks in R;

Data visualization in R/

Knowledge concerning the purposes, research and statistical tools
of multidimensional analysis on the basis of economic research.

Knowledge of practical use of selected methods of
multidimensional statistical analysis to solve specific economic
problems.

Knowledge of packages of R environment concerning
multivariate data analysis methods and ability of fluent work with
these packages with real research examples.
Andrzej Dudek
andrzej.dudek@ue.wroc.pl
+48 757538373
[1] Dennis B.(2012) The R Student Companion. Chapman & Hall/CRC Press, Boca
Raton, FL, 2012. ISBN 978-1-4398-7540-7.
[2] Walesiak M., Gatnar E. (red.) (2009), Statystyczna analiza danych z
wykorzystaniem programu R, PWN, Warszawa.
[3] Gatnar E., Walesiak M. (red.) (2011), Analiza danych jakościowych i
symbolicznych z wykorzystaniem programu R, C.H. Beck, Warszawa.
[4] R Development Core Team (2012), R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, URL http://www.Rproject.org.
[5] Walesiak M., Dudek A. (2012), clusterSim package, URL http://www.Rproject.org.
all students
Faculty:
tak - nazwa przedmiotu: Analiza danych
czy przedmiot jest tak
wydział:EZiT
kopią przedmiotu
kierunek: Ekonomia/Zarządzanie
prowadzonego na
specjalność: kierunkowy
UE?
rok:3
Title:
Lecture hours:
Study period:
Level:
Location:
Examination:
Language:
Prerequisites:
Course content:
E-TOURISM
10 h. lectures, 20 h. labs.
one semester (winter or summer)
basic
Wrocław (Jelenia Góra also possible)
Tests for lecture grades, own projects for labs grades.
English (for labs German as second language)
Information technologies at basic level, basic html knowledge is an
advantage
 Information technology in travel agencies activities.

Website of tourism company. Portals / vortals. Travel search
engines and compare engines. The use of information technology
in the travel distribution systems (computer systems, distribution
and computer reservation systems, GDS, development of web
distribution systems).
Learning
outcomes:
Contact person:
Literature:

Web positioning basic techniques and tools, Google Analytics.

Internet promotion of tourism enterprises, banners, mailing,
newsletters, social media promotion.

Booking systems – technological ground. HTTP Server - Apache,
IIS. HTML. Language JavaSrcipt. PHP language, Web Services,
Major search providers / reservation desk (GDS): MerlinX,
Amadeus.

Creation of a simple travel search engine using MerlinX MDS
webservices. standard.

Knowledge and skills in information technology applied in the
distribution and promotion of tourist offers.

Knowledge of the practical use of booking systems, the latest
online marketing techniques, core design tools to support the
creation and sale of products for the tourist market.

Knowledge of the basic principles of web design and positioning.

Knowledge of the construction features and capabilities of online
booking system (for example) MerlinX MDS webservices

The ability of creation a simple travel search / booking system
engine using MerlinX MDS webservices standard.

The awareness of information technologies significance in the
tourism market.
Andrzej Dudek
andrzej.dudek@ue.wroc.pl
+48 757538373
Buhalis D., Egger R. (2008), eTourism case studies: management and
marketing issues in eTourism, El Sevier, London,
Buhalis D., Laws E. (2004), Tourism Distribution Channels. Practices,
issues and transformations, Thomson Learning, London,
MerlinX
web
service
developer
documentation:
http://docu.mdsws.merlinx.pl/data:request:offers.
all students
Faculty:
tak - nazwa przedmiotu: E-turystyka
czy przedmiot jest tak
wydział:EZiT
kopią przedmiotu
kierunek: Turystyka
prowadzonego na
specjalność: kierunkowy
UE?
rok:3
Title:
Computer network and security
Lecture hours:
Study period:
Level:
Location:
Examination:
Language:
Prerequisites:
Course content:
Learning
outcomes:
Contact person:
Literature:
Faculty:
czy przedmiot jest
kopią przedmiotu
prowadzonego na
UE?
Title:
Lecture hours:
Study period:
Level:
Location:
Examination:
Language:
Prerequisites:
Course content:
45 (lectures) and 15 (laboratories)
Fall or Spring semester
Basic
Wrocław
Written exam
English
None
Introduction to information security. Types of attacks. Basics of cryptography.
Public key cryptography. Methods of authentication and access control
mechanisms. Security of computer systems and networks. Secure network
protocols. Protection applications, systems and computer networks. Security
policy.
Students have knowledge in the field of information security, security threats
and attacks. They can evaluate and increase security of applications, systems and
computer networks. They have basic skills of analysis security of application,
computer systems and networks. They are also aware of the role and importance
of security of computer networks in the socio-economic.
Dr inż. Radosław Rudek, e-mail: radoslaw.rudek@ue.wroc.pl, phone: +48 71 36
80 378
Dr Artur Rot, e-mail: artur.rot@ue.wroc.pl, phone: + 48 71 36 80 71 36 80 379
1. Stinson D. R., Cryptography: Theory and Practice, Third Edition, Chapman &
Hall, 2006.
2. Comer D. E., Computer Networks and Internets with Internet Applications,
Prentice Hall, New Jersey, 2009.
3. Amato V., Lewis W., Cisco Networking Academy Program, Cisco press,
2000.
3. Tanenbaum A. S., Wetherall D. J., Computer networks, Prentice Hall, New
Jersey, 2010.
4. D. R. Ahmad et al., Hack Proofing Your Network, Syngress Publishing, 2002.
All students
tak - nazwa przedmiotu: Computer network and security
wydział: ZIF
kierunek: Business Informatics
specjalność: wszystkie
rok: 2, semester IV
Data structures and algorithms in business application
30 (lecture)
Fall or Spring semester
Basic
Wrocław
Written exam
English
None
1. Data structures and their representation
2. Fundamentals of algorithms
3. Fundamentals of computational complexity theory (polynomial time
solvability, NP-completeness and NP-hardness, pseudopolynomial time
algorithms and strong NP-completeness)
4. Exact and approximation algorithms and efficiency analysis
5. Artificial intelligence methods for business application: descent search,
simulated annealing, tabu search, genetic algorithms, reinforcement learning
6. Parallel and distributed computation
Students are acquainted with data structures and their representation, such that
they are able to choose appropriate data structures for various business
applications. Moreover, students know the practical aspects of
the
computational complexity theory to recognize the difference between practical
problems that can be or cannot be solved optimally in reasonable time.
Furthermore, students acquire knowledge about artificial intelligence methods
that are required to efficiently solve hard (in computational sense) problems in
business application.
Contact person: Dr inż. Radosław Rudek, e-mail: radoslaw.rudek@ue.wroc.pl, phone: +48 71 36
80 378
1. T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to
Literature:
Algorithms, MIT Press, 2009.
2. C.H. Papadimitriou, Computational Complexity, Addison Wesley Longman,
1993.
3. F.W. Glover, G. A. Kochenberger (eds.), Handbook of Metaheuristics,
Springer 2003.
4. D. P. Bertsekas, J. N. Tsitsiklis, Parallel and Distributed Computation:
Numerical Methods, Athena Scientific, 1997.
All students
Faculty:
czy przedmiot jest nie
Learning
outcomes:
kopią przedmiotu
prowadzonego na
UE?
Title:
COMPUTER TOOLS FOR DATA ANALYSIS
Lecture hours:
Lectures: 10 hours
Computer Classes: 20 hours
Winter and Summer Term
Basic
Wrocław
Case Studies and Empirical Paper
English
Mathematics, Statistics
Lectures and Classes:
1. Elements of the Analysis of Survey Data (e.g. data coding, preparing
data for analysis, descriptive statistics) and Basic Data Analysis
(Correlation Analysis, Regression Analysis, ANOVA).
2. Advanced Data Analysis (e.g. measure scales, normalization, Linear
Ordering, Cluster Analysis).
3. Advanced Data Analysis (e.g. Classification Trees, Conjoint Analysis).
4. Advanced Data Analysis (e.g. Contingency Tables; Correspondence
Analysis, Multidimensional Scaling, Factor Analysis)
5. Writing Research Report; Presentation of the Results (Preparing
Presentation, Results Presentation, Graphs and Plots using software (e.g.
MS Excel, Statistica, SPSS).
Computer Classes:
Conducting Data Analyses with the use of Computer Tools: MS Excel and
Statistica, SPSS. Preparing Presentation of the Research Results using
Computer Tools: eg. MS Power Point, Prezi.
Study period:
Level:
Location:
Examination:
Language:
Prerequisites:
Course content:
Knowledge: knowledge of research design and data analysis methods.
Competence and skills: designing economic research, mastering data
analysis methods and techniques using software (MS Excel, Statistica,
SPSS), preparing presentations of the results using software (MS Power
Point or Prezi).
Contact person: dr Marta Dziechciarz-Duda (marta.dziechciarz@ue.wroc.pl)
[1] Anderson T.W.: An Introduction to Multivariate Statistical Analysis,
Literature:
John Wiley & Sons 2003.
[2] Hair J.F., Black W.C., Babin B.J. Anderson R.E: Multivariate Data
Analysis (7th Edition), Prentice Hall 2009.
[3] Lattin J., Carroll D., Green P.: Analyzing Multivariate Data,
Cengage Learning 2002.
[4] Sweet S., Grace-Martin K.: Data Analysis with SPSS (4th Edition),
Pearson 2011.
[5] www.statsoft.com/textbook.
All Faculties
Faculty:
title: Metody analizy danych
Is this a copy of yes
department: ZIF
the lecture
faculty: Z
already taught on
specialty: all
UE?
year: 2 (LS)
Learning
outcomes:
Title:
Lecture hours:
Study period:
Level:
Location:
Examination:
Language:
Prerequisites:
Course content:
Learning
outcomes:
Contact person:
Literature:
Dynamic and Financial Econometrics
Lectures: 10 hours
Computer Classes: 20 hours
Winter and Summer Term
Basic
Wrocław
Case Studies and Research Project Paper
English
Mathematics, Statistics
Lectures:
1. Introduction to Time Series Models.
2. Stationary and Non-stationary Stochastic Processes. Seasonality.
3. Stationarity. Testing for Stationarity.
4. ARIMA Models. ARCH Models.
5. Cointegration. Testing for Contegration. Error Correction Models.
Computer Classes:
Application of Dynamic Econometric Methods in Modelling Financial
Time Series with the Use of Computer Tools: MS Excel and GRETL.
Knowledge: knowledge of dynamic econometric models and methods
Competence and skills: data analysis, applications of dynamic
econometric methods in modelling financial time series using software
(MS Excel and GRETL)
Prof. Józef Dziechciarz (jozef.dziechciarz@ue.wroc.pl)
Mgr Anna Król (anna.krol@ue.wroc.pl)
To get more information visit our Internet site at:
http://www.ekonometria.ue.wroc.pl/
[6] Enders W.: Applied Econometric Time Series, John Wiley & Sons
2010.
[7] Taylor S.: Modelling Financial Time Series, John Wiley & Sons
1992.
[8] Brooks Ch.: Introductory Econometrics for Finance, Cambridge
University Press 2002.
[9] Mills T. C., Markellos R. N.: The Econometric Modelling of
Financial Time Series, Cambridge University Press 2008.
[10] Greene W.H.: Econometric Analysis, Prentice Hall 1999.
All Faculties
Faculty:
Is this a copy of yes title: Ekonometria dynamiczna i finansowa
department: ZIF
the lecture
faculty: IiE
already taught on
specialty: all
UE?
year: 1 (MS)
Title:
Lecture hours:
Study period:
Level:
Location:
Examination:
Language:
Prerequisites:
Course content:
Learning
outcomes:
Contact person:
Literature:
Econometrics
Lectures: 10 hours
Computer Classes: 20 hours
Winter and Summer Term
Basic
Wroclaw
Case Studies and Research Project Paper
English
Mathematics, Statistics
Lectures:
1. Simple Regression Model. Ordinary Least Squares (OLS) Estimation.
Assumptions Underlying Classical Linear Regression Model.
2. Multiple Regression Model. Properties of the OLS Estimators.
3. Goodness of Fit. Hypothesis Testing: t-test, F-test. Normality of the
Disturbance Term.
4. Heteroskedasticity. Autocorrelation.
5. Specification Analysis and Model Selection. Multicollinearity.
Computer Classes:
Application of Econometric Methods in Economics, Finance and Business
with the Use of Computer Tools: MS Excel and GRETL.
Knowledge: basic knowledge of econometric theory, models and methods
Competence and skills: data analysis, techniques of econometric models’
estimation and verification (on the basic level)
Prof. Józef Dziechciarz (jozef.dziechciarz@ue.wroc.pl)
Mgr Anna Król (anna.krol@ue.wroc.pl)
To get more information visit our Internet site at:
http://www.ekonometria.ue.wroc.pl/
[11] Maddala G.S.: Introduction to Econometrics, John Wiley & Sons
2001.
[12] Dougherty Ch.: Introduction to Econometrics, Oxford University
Press 2002.
[13] Greene W.H.: Econometric Analysis, Prentice Hall 1999.
[14] Davidson R., MacKinnon J.G.: Econometric Theory and Methods,
Oxford University Press 2004.
[15] Brooks Ch.: Introductory Econometrics for Finance, Cambridge
University Press 2002.
All Faculties
Faculty:
Is this a copy of yes title: Ekonometria
department: ZIF
the lecture
faculty: FIR, IiE
already taught on
specialty: all
UE?
year: 2 (LS)
Probability Theory with Applications
40 (20+20) [minimal number of students – 10]
Both summer and winter terms
Basic
Wrocław
Test (in writing)
English
Algebra, Analysis
Probability space, random events as sets;
Definitions of probability measures;
Conditional probability and Bayes’ rule;
Independence of random events;
Random variables and their parameters;
Correlation and independence of random variables;
Basic discrete and continuous distributions;
Limit theorems.
The understanding of uncertainity and statistical approaches,
Learning
distinguishing more and less probable possibilities.
outcomes:
Contact person: Dr inż. Albert Gardoń, B-6, Albert.Gardon@ue.wroc.pl
Pitman J. “Probability”. Springer, New York 1993.
Literature:
Lupton R. “Statistics in Theory and Practice”. Princeton U. P. 1993.
McClave J.T., Dietrich F.H. “Statistics”. Dellen, San Francisco 1988.
All
Faculty:
tak - nazwa przedmiotu: Rachunek prawdopodobieństwa
czy przedmiot jest nie
wydział: ZIF
kopią przedmiotu
kierunek: wszystkie
prowadzonego na
specjalność: wszystkie
UE?
rok: 1 lub 2
Title:
Lecture hours:
Study period:
Level:
Location:
Examination:
Language:
Prerequisites:
Course content:
Title:
Lecture hours:
Study period:
Level:
Location:
Examination:
Language:
Prerequisites:
Course content:
Statistics: Inference and Mathematical Statistics
40 (20+20) [minimal number of students – 10]
Both summer and winter terms
Basic
Wrocław
Test (in writing)
English
Mathematics, Probability
Ordering statistical data, empirical density and distribution functions;
Estimation, basic statistical measures (mean, variance, skewness,
correlation);
Linear regression model;
Confidence intervals;
Statistical tests (parametric and non-parametric).
The ability to make statistical inferences, the knowledge of the data
Learning
analysis foundations, the use of mathematical tools in decision making.
outcomes:
Contact person: Dr inż. Albert Gardoń, B-6, Albert.Gardon@ue.wroc.pl
Lupton R. “Statistics in Theory and Practice”. Princeton U. P. 1993.
Literature:
McClave J.T., Benson P.G. “Statistics for Business and Economics”.
Dellen, San Francisco 1985.
All
Faculty:
tak - nazwa przedmiotu: Statystyka Opisowa i Matematyczna
czy przedmiot jest nie
wydział: wszystkie
kopią przedmiotu
kierunek: wszystkie
prowadzonego na
specjalność: wszystkie
UE?
rok: 1 lub 2
Marketing Research
Title:
Lectures: 10 hours
Lecture hours:
Computer Classes: 20 hours
Winter and Summer Term
Study period:
Basic
Level:
Wrocław
Location:
Case Studies and Research Project Paper
Examination:
English
Language:
Mathematics, Statistics
Prerequisites:
Course content: Lectures and Classes:
1. Introduction to Marketing Research, Research Design, Data Collection
and Analysis.
2. Measurement and Scaling, Data Preparation.
3. Questionnaire design.
4. Survey Data Analysis.
5. Writing Marketing Research Report.
Computer Classes:
Application of Marketing Research Methods with the Use of Computer
Tools: MS Excel and Statistica.
Knowledge: basic knowledge of marketing research theory and methods
Learning
Competence and skills: mastering marketing research methods and
outcomes:
techniques using software (MS Excel and Statistica)
Contact person: Dr Klaudia Przybysz (klaudia.przybysz@ue.wroc.pl)
To get more information visit our Internet site at:
http://www.ekonometria.ue.wroc.pl/
[16] Churchill G.A. Jr.: Marketing Research: Methodological
Literature:
Foundations, Dryden Press 1995.
[17] Zikmund W. G.: Exploring Marketing Research, Dryden Press 1994.
[18] Anderson T. W., Finn J. D.: The New Statistical Analysis of Data,
Springer-Verlag 1997.
[19] Malhotra N. K., Birks D. F.: Marketing Research : an Applied
Approach, Prentice Hall 1999.
[20] Webb J. R.: Understanding and Designing Marketing Research,
Academic Press 1992.
All Faculties
Faculty:
title: Badania marketingowe
Is this a copy of yes
department: ZIF
the lecture
faculty: Z
already taught on
specialty: all
UE?
year: 3 (LS)
Title:
Lecture hours:
Study period:
Level:
Location:
Examination:
Language:
Prerequisites:
Course content:
Learning
outcomes:
Contact person:
Literature:
Methods of Data Analysis
Lectures: 10 hours
Computer Classes: 20 hours
Winter and Summer Term
Basic
Wrocław
Case Studies and Empirical Paper
English
Mathematics, Statistics
Lectures and Classes:
1. Research Design (Research Topic, Data Sources, Sample Selection,
Literature Review, Ethical Aspects).
2. Basic Data Analysis (e.g. Measurement Scales, Descriptive Statistics,
Correlation Analysis, Regression Analysis, Hypothesis Testing and
Inference).
3. Advanced Data Analysis and Special Topics (e.g. Classification Trees,
Clustering Analysis, Binary Choice Models).
4. Writing Research Report (Report Structure, Theoretical Introduction,
Data Presentation, Results Presentation, Graphs and Plots, References),
5. Presentation of the Results (Preparing Presentation, Effective
Presentation Techniques).
Computer Classes:
Conducting Data Analyses with the Use of Computer Tools: MS Excel
and Statistica. Preparing Presentation of the Research Results using
Computer Tools: MS Power Point or Latex Beamer Class.
Knowledge: basic knowledge of research design and data analysis
methods.
Competence and skills: designing economic research, mastering data
analysis methods and techniques using software (MS Excel,
Statistica), preparing presentations of the results using software
(MS Power Point or Latex Beamer Class).
Dr Klaudia Przybysz (klaudia.przybysz@ue.wroc.pl)
To get more information visit our Internet site at:
http://www.ekonometria.ue.wroc.pl/
[21] Anderson T. W., Finn J. D.: The New Statistical Analysis of Data,
Springer-Verlag 1997.
[22] Kumar R.: Research Methodology, SAGE Publications 2005.
[23] Warner R.M.: Applied Statistics, Sage 2008.
[24] Gnanadesikan R.: Methods for Statistical Data Analysis of
Multivariate Observations, John Wiley & Sons 1997.
[25] Maddala G.S.: Introduction to Econometrics, John Wiley & Sons
2001.
All Faculties
Faculty:
title: Metody analizy danych
Is this a copy of yes
department: ZIF
the lecture
faculty: Z
already taught on
specialty: all
UE?
year: 2 (LS)
Title:
Lecture hours:
Study period:
Level:
Location:
Examination:
Language:
Prerequisites:
Course content:
DISCRETE LOCATION THEORY
Lecture: 20 hours
Fall Semester
Intermediate
Wrocław
Quizzes (40%) and an examination (60%).
The quizzes will consist of short problems verifying the understanding of
the material from a preceding lecture.
English
A course in operations research or at least a good understanding of
linear programming.
The pervasive theme throughout the whole course is the notion of
‘optimal choice within a spatial context’. Examples of such choices
include the location of factories, warehouses, schools, hospitals and
machines and departments within a production facility.
During the course, there will be discussed four families of locational
theory problems: the p-median problem, the uncapacitated facility
location problem, the p-center problem and the quadratic assignment
problem. If time permits, we will also cover multiperiod capacitated
location models and competitive locations with games.
The ultimate goal is to provide potential decision-makers with
Learning
quantitative tools for finding good solutions to realistic locational
outcomes:
decision problems.
Contact person: Dr Katarzyna Krupińska
e-mail: katarzyna.krupinska@ue.wroc.pl phone: +48 713680334
Most of the course topics can be found, for example, in:
Literature:
Daskin M.S., Network and discrete location: models, algorithms and
applications, John Wiley & Sons, New York, 1995;
or in:
Drezner Z., Hadamacher H.W., Facility location: applications and theory,
Springer, Heilderberg, 2004.
All students
Faculty:
czy przedmiot jest nie
kopią przedmiotu
prowadzonego na
UE?
Title:
Lecture hours:
Study period:
MULTICRITERIA DECISION ANALYSIS
Lecture: 20 hours
Fall Semester
Intermediate
Wrocław
The course grade will be based on an examination (40%) and a project
(60%).
English
Language:
No prior background in multicriteria decision techniques is required.
Prerequisites:
Enthusiasm for learning.
Course content: This course is focused on learning to recognize, understand, analyze and
solve different classes of decision problems involving more than one
criterion. As such, the course shall provide an overview of the wide
spectrum of existing concepts and methods. We will start with different
kinds of multiobjective optimization (vector, lexicographic, Chebyshev);
then we will proceed to obtain satisfying solutions via goal programming
and reference point methods. Further, the various concepts of interactive
improvements of a solution will be discussed. Next, we will consider the
preference modeling with the outranking methods of the classes
ELECTRE and PROMETHEE. After that will come methods within utility
and value theories.
Learning the material is supposed to be fun. Each time a new topic is
presented, there will be some theory given, but the theory will only be
tailored to the level necessary for understanding crucial aspects of models
and methods under consideration, as the ultimate aim of the course is to
convince the students of the wide applicability of the vibrant discipline of
multicriteria decision making.
After taking the course, students are expected to be able to:
Learning
recognize decision situations as multicriteria decision problems,
outcomes:
formulate mathematical models for them, using an adequate to the
problem concept of optimality, and solve those models.
Contact person: Dr Katarzyna Krupińska
e-mail: katarzyna.krupinska@ue.wroc.pl phone: +48 713680334
The course is based on selected papers which will be distributed in class.
Literature:
Some of the topics can be found in:
Bouyssou D., Marchant T., Pirlot M., Tsoukias A., Vincke Ph., Evaluation
and decision models with multiple criteria. Stepping stones for the
analyst, Springer, New York, 2006.
Ehrgott M., Multicriteria optimization, Springer, Berlin, 2000.
All students
Faculty:
czy przedmiot jest nie
kopią przedmiotu
prowadzonego na
UE?
Level:
Location:
Examination:
Title:
Lecture hours:
Study period:
Level:
Location:
Examination:
OPERATIONS RESEARCH METHODS
Lecture: 20 hours
Fall Semester
Intermediate
Wrocław
Attendence, participation, quizzes: 10%
Homework: 15%
Midterm exams: 50%
Final exam: 25%
English
Language:
None
Prerequisites:
Course content: This course is an introduction to an operations-research approach to
decision making. It will give students an outline of methods in linear,
integer, basic nonlinear and dynamic programming, with specific
applications to business decision problems.
After taking the course, students are expected to be able to:
Learning
1. Formulate business decision making problems as linear, dynamical,
outcomes:
or integer programs;
2. Solve linear programs, network optimization problems, and dynamic
programming problems;
3. Understand linear programming duality and perform sensitivity
analysis;
4. Demonstrate necessary and sufficient conditions for optimality in
nonlinear programming.
Contact person: Dr Katarzyna Krupińska
e-mail: katarzyna.krupinska@ue.wroc.pl phone: +48 713680334
Lawrence J.A., Pasternack B.A., Applied management science: a
Literature:
computer-integrated approach for decision making, John Wiley & Sons,
New York, 1998.
All students
Faculty:
czy przedmiot jest nie
kopią przedmiotu
prowadzonego na
UE?
Title:
Lecture hours:
Study period:
Level:
Location:
Examination:
Language:
Prerequisites:
Course content:
Management Information Systems (MIS)
30 hours of lectures + 20 hours of tutorial classes
Winter and Summer Semester
Basic
Wrocław
Assignments and written test (the latter in case of a larger class when the
originality of assignment answers cannot be fully validated).
English
N/A
Management Information Systems is concerned with studies of “soft”
aspects of computing and information systems and combines them with
behavioural issues traditionally studied in management science,
economics, sociology, and psychology. MIS is predominantly an applied
endeavour that studies application and use of information systems in (and
by) business, government and society at large.
Course topics:
1) Information Systems in Global Business Today
a) The Role of Informatics in Business Today
b) Perspectives on Business Systems and Information Technology
c) Contemporary Approaches to Information Systems
2) E-Business: How Businesses Use Information Systems
a) Business Processes and Information Systems
b) Types of Business Information Systems
c) Systems That Span the Enterprise
d) The Information Systems Function in Business
3) Information Systems, Organizations, and Strategy
a) Organizations and Business Informatics
b) Using Information Systems to Achieve Competitive Advantage
c) Managing Information Systems
4) Ethical and Social Issues in Information Systems
a) Understanding Ethical and Social Issues Related to Systems
b) Ethics in an Information Society
c) The Moral Dimensions of Information Systems
Learning
outcomes:

Understanding how information systems are transforming business
and how do they relate to globalization.
 Appreciation why information systems are so essential for running
and managing a business today.
 Thorough knowledge of what exactly is an information system and
what are its management, organization, and technology components.
 Understanding the relationships between business processes and
information systems.
 Identification how systems serve the various levels of management in
a business.
 Recognition of the differences between e-business, e-commerce, and
e-government.
 Recognition of the significance of using information systems to
develop competitive strategies.
 Appreciation of ethical, social, and political issues raised by
information systems.
 Understanding of how and why do contemporary information systems
and technology pose challenges to the protection of individual privacy
and intellectual property.
 In depth inside into how information systems and technology affect
everyday life.
Contact person: Prof. Leszek A. Maciaszek
email: leszek.maciaszek@ue.wroc.pl
web: http://www.iie.ue.wroc.pl/lmaciaszek/en
Laudon K., Laudon J., Management Information Systems : Managing the
Literature:
Digital Firm, 13th ed., Upper Saddle River, Pearson, 2014
This is a service course for all students
Faculty:
czy przedmiot jest Tak:
kopią przedmiotu 1) Informatyka w zarządzaniu (IwZ)
II rok licencjat
prowadzonego na
studenci różnych kierunków
UE?
2) Podstawy systemów informacyjnych (PSI)
I rok licencjat
Informatyka w Biznesie