1 Algorithm design in business application Lecture hours: 30 Study period: Fall or Spring semester Location: Wrocław Examination: Written exam Prerequisites: None Course content: 1. Introduction to data structures and algorithms 2. Introduction to computational complexity theory 3. Polynomial time solvability, NP-completeness and NP-hardness 4. Pseudopolynomial time algorithms and strong NP-completeness 5. Approximation methods and efficiency analysis 6. Branch and bound method 7. Heuristic algorithms 8. Metaheuristic algorithms for business application: descent search, simulated annealing, tabu search, genetic algorithms, reinforcement learning 9. Parallel and distributed computation Learning Students are acquainted with the practical aspects of the computational complexity theory, such that they are able to recognize the difference between practical problems that can be or cannot be solved optimally in reasonable time. Furthermore, students acquire knowledge about techniques of efficient algorithm design that are dedicated for hard (in computational sense) problems in business application. Contact person: Dr inż. Radosław Rudek, phone: Literature: 1. T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms, MIT Press, C.H. Papadimitriou, Computational Complexity, Addison Wesley Longman, F.W. Glover, G. A. Kochenberger (eds.), Handbook of Metaheuristics, Springer D. P. Bertsekas, J. N. Tsitsiklis, Parallel and Distributed Computation: Numerical Methods, Athena Scientific, All students nie Lecture hours: Study period: Location: Examination: Language: Prerequisites: Artificial Intelligence in Economics and Finance Lectures: 15 hours; laboratories: 15 hours Winter and Summer semester Master Studies Wrocław Written exam and assignments English notions in Computer Science and Economics
2 Course content: Learning Contact person: Literature: Topics: Introduction to artificial intelligence. Problems and solutions, universal problem solver concepts. Methods of artificial intelligence overview. Knowledge representation and reasoning techniques in intelligent systems. Machine learning and inductive knowledge. Data and process mining techniques. Intelligent applications in economics and finance: decision support in management, economic predictions, market basket analysis, bankruptcy prediction, credit scoring. Teaching methods: lectures, lab activities with intelligent system project preparation. The course will help students understand an essence and methods of artificial intelligence including application aspects. Course participants will learn: - what are the crucial properties of artificial intelligence approach, - how intelligent systems are designed and implemented, - what intelligent techniques and tools can be used to support decisions in management and finance Prof. Jerzy Korczak, prof. Mieczysław Owoc < Luger G., Artificial Intelligence: Structures and strategies for Complex Problem Solving, Pearson Education Turban E., Aronson J.E, Liang T-P: Decision Support Systems and Intelligent Systems (7th Edition). Prentice Hall, 2004 Russel S., Norvig P., Artificial Intelligence: A Modern Approach, Prentice Hall, Voges K, Pope L., Business Application and Computational Intelligence, Idea Group Pub., 2006 Witten, J., Eibe, F. : Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, Binner J.M, Kendall G., Chen S-H.: Applications of Artificial Intelligence in Finance and Economic. Emerald Group Publishing Limited,2005 Management, Computer Science and Finance nie częściowo - nazwa przedmiotu: Podstawy sztucznej inteligencji wydział: ZIF kierunek: Informatyka i ekonometria, Informatyka w biznesie specjalność: rok: s of Logistics in SAP ERP Lecture hours: 15 Study period: Both Intermediate Location: Wrocław Examination: Computer test Prerequisites: s of Logistics Course content: 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 3. Production Planning 4. Sales and Distribution Learning Rising demand for centralized information in the contemporary companies results in growing interest in integrated information systems. One of the best known solutions from this field is the SAP ERP system. 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, Literature: Dowling K.N., SAP project system handbook, McGraw Hill, 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 tak nazwa przedmiotu: Systemy informatyczne w logistyce - system R3 wydział: Zarządzania, Informatyki i Finansów kierunek: Zarządzanie specjalność: Logistyka rok: III (LS) Lecture hours: Study period: Location: Examination: Language: Prerequisites: Course content: Learning Contact person: DATABASES 15 lectures + 15 labs Whole year Wrocław Written form: Report prepared by students confirming performed database applications and/or multiple choice question single answer test English Fundamentals of computer science and optionally: Information Systems Design, Computer Networks Topics: concepts of databases. Database infrastructure. Query languages overview. SQL an universal access language to modern databases. Query and transaction processing. Advances topics of databases: distributed databases, post-relational databases. Universal DBMS server and future trends in databases. Teaching methods: lectures, lab activities with database project preparation. Understanding an essence and features of database technology. Ability to model and define a database for the specific domain. Capability to process a database using queries (with SQL commands). knowledge about processing modern databases (using transactions and queries respecting database features) on universal database servers). Orientation in future trends in database technology. Mieczysław Owoc Ph.D. hab. prof., phone: , building Z, room. 602
4 Literature: Connolly T.M, Begg C.E.: Concepts of Database Management. Addison- Wesley, Reading 2009 Faroult S., Robson P.: The Art of SQL. O Reilly Media, 2009 Hoffer A.A, Prescott M., Topi H.: Modern Database Management. Addison-Wesley, Reading, 2008 Kroenke D.M., Auer D.: Database Concepts. Prentice-Hall, Englewood Cliffs, 2009 Silberschatz A., Korth H.F., Sudarshan S.: Database System Concepts. McGraw-Hill 2010 Taylor A.G.: SQL For Dummies. Wiley Publishing, 2010 All students tak - nazwa przedmiotu: Bazy danych wydział:zif kierunek:informatyka i ekonometria; Informatyka w biznesie specjalność: wszystkie rok:ii Dynamic and Financial Econometrics ECTS credits: 4 Lecture hours: Lectures: 10 hours Computer Classes: 20 hours Study period: Winter and Summer Term Location: Wrocław Examination: Case Studies and Research Project Paper Prerequisites: Mathematics, Statistics Course content: 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. Learning 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) Contact person: Prof. Józef Dziechciarz Mgr Anna Król To get more information visit our Internet site at: Literature:  Enders W.: Applied Econometric Time Series, John Wiley & Sons  Taylor S.: Modelling Financial Time Series, John Wiley & Sons 1992.
5 Is this a copy of the lecture already taught on  Brooks Ch.: Introductory Econometrics for Finance, Cambridge University Press  Mills T. C., Markellos R. N.: The Econometric Modelling of Financial Time Series, Cambridge University Press  Greene W.H.: Econometric Analysis, Prentice Hall All Faculties yes title: Ekonometria dynamiczna i finansowa department: ZIF faculty: IiE specialty: all year: 1 (MS) Econometrics ECTS credits: 4 Lecture hours: Lectures: 10 hours Computer Classes: 20 hours Study period: Winter and Summer Term Location: Wroclaw Examination: Case Studies and Research Project Paper Prerequisites: Mathematics, Statistics Course content: 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. Learning 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) Contact person: Prof. Józef Dziechciarz Mgr Anna Król To get more information visit our Internet site at: Literature:  Maddala G.S.: Introduction to Econometrics, John Wiley & Sons  Dougherty Ch.: Introduction to Econometrics, Oxford University Press  Greene W.H.: Econometric Analysis, Prentice Hall  Davidson R., MacKinnon J.G.: Econometric Theory and Methods, Oxford University Press Brooks Ch.: Introductory Econometrics for Finance, Cambridge
6 Is this a copy of the lecture already taught on University Press All Faculties yes title: Ekonometria department: ZIF faculty: FIR, IiE specialty: all year: 2 (LS) Human-Computer Interaction (HCI) Lecture hours: 30 (min. hours: 30) Study period: Winter/ Summer Semester specialisation Location: Wrocław Examination: Credit: verification test / short scientific paper / project Prerequisites: information technology, basis of information systems and design methodologies Course content: 1. Introduction, overview of the historical HCI development. 2. Human and computer within an interaction (cognitive processing, perceptual-motor interaction, stimuli processing) 3. Human-computer interaction levels (GUI, virtual reality, ubiquitous computing, augmented reality) 4. Methods for interface testing and evaluation 5. Interactive Systems 6. Managing information resources in the context of human-computer interaction 7. Non-traditional interfaces - part. I 8. Non-traditional interfaces - part. II 9. HCI standards (ISO ergonomics of working with the computer, the paradigm of user-oriented design, UIML) 10. Interaction Design - Paradigms, methods, tools 11. User Modeling - methods and techniques 12. Adaptive processes of interaction 13. Technologies and tools for building interaction in the network communities 14. Entry techniques and technologies, devices, sensors 15. Presentation of contemporary projects on the human-computer interaction development (NaviCam, HUD, 3DV Systems, Geminoid HI-1,etc...) Lectures, Case Study, Exemplification, Presentation, Learning Introduction to the methodology of designing the interaction between user and a system (in the scope of information, sociological and engineering aspects). Understanding the processes of interaction and ergonomics of computer applications. Ability to determine and use the objectives of designing user-system interaction strategy in the context of building business applications. Knowledge of modern trends and technologies used in human-machine interaction beyond the GUI. Contact person: Dr Gracja Wydmuch Literature: Sears, A., & Jacko, J. A. (2008). The Human-Computer Interaction Handbook. Fundamentals, Evolving Technologies and Emerging Applications. New York: Lawrence Erlbaum Associates. Zaphiris, P., & Ang, C. S. (2009). Human Computer Interaction: Concepts, Methodologies, Tools, and Applications. New York: Information Science reference.
7 Lecturer s publications Preece, J., Rogers, Y., & Sharp, H. (2009). Interaction Design Beyond Human- Computer Interaction. New York: John Wiley & Sons, Inc. Kortum, P. (2008). HCI Beyond the GUI. Design for Haptic, Speech, Olfactory, and Other Nontraditional Interfaces. San Francisco: Elsevier. Johnson, J. (2008). GUI Bloopers 2.0. Common User Interface Design Don ts and Dos. San Francisco: Elsevier. All students nie albo tak - nazwa przedmiotu: Interakcja Człowiek-Komputer wydział: Zarządzania, Informatyki i Finansów kierunek: Informatyka w Biznesie specjalność: -- rok: -- Predisposition-based intelligent tutoring system. Adaptive user profiling in human-computer interaction [in:] Information System and Technologies WEBIST. INSTICC, User Profiling in Intelligent Tutoring Systems Based on Myers-Briggs Personality Types. In lecture Notes in Engineering and Computer Science, IAENG - International Associacion of engineers, Intelligent Agents and Multiagent Systems Fundamentals and Applications Lecture hours: 10 Lectures: 2 teaching hours each (in a block) 4 Practice classes: 2 teaching hour each Study period: Winter 2012 or Summer 2013 (TBC) Advanced/specialised Location: Wrocław Examination: Oral examination and project assignment (50/50) Prerequisites: Course content: Learning Introduction to Artificial Intelligence, Programming The course is structured as an intensive and self-learning course consisting of lectures and practice classes/tutorials, and hands-on simple application development assigbnment/project as follows: 10 Lectures: 2 teaching hours each (in a block) 4 Practice classes: 2 teaching hour each Examination day Assignment/project: team-based (2-3 students). The course includes the following topics: Introduction to Intelligent Agents and Multi-agent Systems Deductive and Practical Reasoning Agents Reactive and Hybrid Architectures Agent Interactions and Negotiation Agent Communication Agent Coordination and Adaptation Methodologies and Applications Adaptive Multi-stage Negotiation Multi-issue Mediated Negotiation Distributed Graph-based Multi-agent Planning Self-Organising Resource Allocation Learning Agents in Market-based Resource Allocation By the end of this course the students should be familiar with the concepts and design principles of intelligent software agents, and be able to
8 Contact person: Literature: Lecture hours: Study period: Location: Examination: Language: Prerequisites: Course content: develop simple applications with a standard agent development tool. Prof Ryszard Kowalczyk, (WUE contact Prof Leszek Maciaszek, Text Book and Supporting Material: An Introduction to MultiAgent Systems by Michael Wooldridge, John Wiley & Sons, 2002 Lecture material posted on the web JADE tutorials, documentation and guidelines from Software: JADE (Java Agent DEvelopment Framework) available from Other software as appropriate (e.g. Java, Internet Explored, MS Office, etc) Other Resources: Agent Technology Roadmaps, The Foundation for Intelligent Physical Agents (FIPA), Foundations of Software Agent Technology, and much more available on the Internet Open to all no INTELLIGENT SYSTEMS 15 lectures + 15 labs Whole year Wrocław Written form: Report prepared by students confirming performed intelligent applications and/or multiple choice question single answer test English Databases, s of Problem-Solving Topics: Introduction to artificial intelligence. Problems and solutions, universal problem solver concepts. Taxonomy and properties of intelligent systems. Approaches to intelligent systems development. Knowledge representation and validation techniques. Architecture of expert systems. Machine learning and inductive knowledge. Modern intelligent systems and its applications: neural nets, evolution algorithms, agent systems. Teaching methods: lectures, lab activities with an intelligent application preparation.
9 Learning Contact person: Understanding an essence and specialty of intelligent systems. knowledge about intelligent systems development including different intelligent techniques. Ability to represent a domain knowledge and to conclude with the defined problem area. Orientation in modern and future trends in artificial intelligence applications. Mieczysław Owoc Ph.D. hab. prof., phone: , building Z, room. 602 Literature: 1. Darlington K., The Essence of Expert Systems. Prentice Hall, Schalkoff R.J.: Intelligent Systems: Principles, Paradigms and Pragmatics. Jones and Bartlett Publishers, Turban E., Aronson J.E, Liang T-P: Decision Support Systems and Intelligent Systems (7th Edition). Pearsons, Prentice Hall, Russell S., Norvig P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Negnevitsky M.: Artificial Intelligence: A Guide to Intelligent Systems. Addison-Wesley, Jones M.T.: Artificial Intelligence. A Systems Approach. Infiniti Science Press, 2008 All students częściowo - nazwa przedmiotu: Podstawy sztucznej inteligencji wydział:zif kierunek:informatyka i ekonometria; Informatyka w biznesie specjalność: wszystkie rok:ii Lecture hours: Study period: Location: Examination: Language: Prerequisites: Course content: Management Information Systems (MIS) 30 hours of lectures + 20 hours of tutorial classes Winter or Summer semester 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.
10 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 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: Literature: Prof. Leszek A. Maciaszek web: Laudon K., Laudon J., Management Information Systems : Managing the Digital Firm, 12th ed., Upper Saddle River, Pearson, 2012 This is a service course for all students
11 Czy przedmiot jest kopią przedmiotu Tak: 1) Informatyka w zarządzaniu (IwZ) II rok licencjat studenci różnych kierunków 2) Podstawy systemów informacyjnych (PSI) I rok licencjat Informatyka w Biznesie Marketing Research ECTS credits: 4 Lecture hours: Lectures: 10 hours Computer Classes: 20 hours Study period: Winter and Summer Term Location: Wrocław Examination: Case Studies and Research Project Paper Prerequisites: Mathematics, Statistics Course content: Lectures and Classes: 1. Introduction to Marketing Research, Research Design, Data Collection and Analysis. 2. Measurement and Scaling, Data Preparation. 3. Analysis of Variance and Covariance, Correlation and Regression. 4. Factor Analysis, Cluster Analysis, Multidimensional Scaling, Conjoint Analysis. 5. Writing Marketing Research Report. Computer Classes: Application of Marketing Research Methods with the Use of Computer Tools: MS Excel and Statistica. Learning Knowledge: basic knowledge of marketing research theory and methods Competence and skills: mastering marketing research methods and techniques using software (MS Excel and Statistica) Contact person: Dr Klaudia Przybysz To get more information visit our Internet site at: Literature: Churchill G.A. Jr.: Marketing Research: Methodological Foundations, Dryden Press Zikmund W. G.: Exploring Marketing Research, Dryden Press Anderson T. W., Finn J. D.: The New Statistical Analysis of Data, Springer-Verlag Malhotra N. K., Birks D. F.: Marketing Research : an Applied Approach, Prentice Hall Webb J. R.: Understanding and Designing Marketing Research, Academic Press Is this a copy of the lecture All Faculties yes title: Badania marketingowe department: ZIF
12 already taught on faculty: Z specialty: all year: 3 (LS) Methods of Data Analysis ECTS credits: 4 Lecture hours: Lectures: 10 hours Computer Classes: 20 hours Study period: Winter and Summer Term Location: Wrocław Examination: Empirical Paper, Case Studies, Examination Test Prerequisites: Mathematics, Statistics Course content: Lectures and Classes: 1. Research Design (Research Topic, Data Sources, Sample Selection, Literature Review, Ethical Aspects). 2. Data Analysis (Measurement Scales, Descriptive Statistics, Correlation Analysis, Regression Analysis, Hypothesis Testing and Inference). 3. Advanced Data Analysis and Special Topics (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. Learning 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). Contact person: Dr Klaudia Przybysz To get more information visit our Internet site at: Literature: Anderson T. W., Finn J. D.: The New Statistical Analysis of Data, Springer-Verlag Kumar R.: Research Methodology, SAGE Publications Warner R.M.: Applied Statistics, Sage Gnanadesikan R.: Methods for Statistical Data Analysis of Multivariate Observations, John Wiley & Sons Maddala G.S.: Introduction to Econometrics, John Wiley & Sons All Faculties
13 Is this a copy of the lecture already taught on yes title: Metody analizy danych department: ZIF faculty: Z specialty: all year: 2 (LS) Project Management in MS Project Lecture hours: 15 Study period: Winter Intermediate Location: Wrocław Examination: Written report (prepared in MS Project) Prerequisites: s of MS Office, s of Project Management Course content: The aims of the course are: 1. Presentation of basics of scheduling in project management 2. Training in defining and tracking projects in MS Project Main topics: 1. Defining project structure CPM method 2. Work Breakdown Structure (WBS) 3. Defining resources 4. Automatic and manual resources leveling 5. Tracking project Learning Outcomes and effects of project strongly depend on its preparation. Suitable schedule and proper assignment of resources is the key factor. conditioning its realization. Participant, after finishing the course, is supposed to be able to: 1. Define WBS for the project 2. Construct network diagram and Gannt chart for the project 3. Define and optimize activities and resources in MS Project Contact person: Literature: Marek Kośny, Anderson D.R., Sweeney D.J., Williams T.A., An Introduction to Management Science. Quantitative Approach to Decision Making, West Publishing Company, 2008 Gray C., Larson E., Project Management with MS Project Management, McGraw-Hill/Irwin, 2007 Hallows J.E., The project management office toolkit, American Management Association, 2002 Kerzner H., Project management : a systems approach to planning, scheduling, and controlling, J.Wiley, 2006 All tak nazwa przedmiotu: Instrumenty wspomagające zarządzanie projektami MS Project wydział: Zarządzania, Informatyki i Finansów kierunek: Informatyka i ekonometria specjalność: Menedżer projektu rok: I (niestacjonarne studia magisterskie) Social Networks in Management
14 Lecture hours: Study period: Location: Examination: Language: Prerequisites: Course content: Learning Contact person: Literature: 15 hours of lectures 15 hours of laboratories fall-winter semester advanced Wrocław credit English Fundamentals of Management / Podstawy Zarządzania Information Technology/Technologie Informacyjne Information Systems/Systemy Informacyjne Lecture: 1. Network science and contemporary management 2. Social networks in business: measures, structures and dynamics 3. Methods of social network analysis and software packages 4. Online network diagnosis and aligning networks with strategy 5. Organizational learning and innovation through effective networks 6. Delivering results through process networks 7. Delivering results through project-based networks 8. Driving performance through human resources networks 9. Driving financial return through organizational network investments 10. Boosting transformations efficiency with network management Laboratories: 1. Methods and tools for social network analysis 2. Editing and visualization of social networks 3. Case analysis - manufacturing 4. Case analysis - trade and marketing 5. Case analysis IT, personal communication 6. Cases; Twitter, LinkedIn, Google+, Blogs, Facebook Knowledge: - understanding the essence of social and organizational networks - methodology of social network presentation, analysis and investigation - understanding the impact of social network on efficiency and performance - knowledge of key areas of network management Skills: - diagnosis of social network in business environment - analysis of organizational network structure and dysfunctions - usage of social network analysis software - understanding organizational dynamics through social networks prof. dr hab. Jerzy Korczak, dr hab. Grzegorz Bełz, 1. Barabasi A.L. (2009): Linked, Plume, Penguin Group, USA 2. Cross R. Parker A. (2004): The Hidden Power of Social Networks, Harvard Business Press, Boston
15 3. Cross R., Thomas R.J. (2009): Driving Results Through Social Networks, Jossey-Bas 4. De Nooy W., Mrvar A., Batageli (2011): Xploratory Social Network Analysis with Pajek (Structural Analysis in The Social Sciences) 5. Newman M. (2010): Networks An Introduction, Oxford Press. 6. Russell M.A. (2011): Mining The Social Web: Analyzing Data From Facebook, Twitter, Linkedin, and Other Social Media Sites, O Reilly. Lecture hours: Study period: Location: Examination: Language: Prerequisites: Course content: Learning Contact person: Literature: All students, (I year of Master Studies) nie Statistics 45 hours = 30 hours of lecture with written exercises and 14 hours of computer laboratory both Winter and Summer semesters Wrocław Written test (partly can be substituted by printed project of statistical analysis of sample data) English Mathematics Designing of statistical inquire. Sources of statistical data. Techniques of tabulation and visualization of statistical data. Methods of variable distribution analysis. Measures of mean level, dispersion, skewness and concentration. Lorenz curve and Gini coefficient. Selected discrete and continuous probability distributions (Binomial, Poisson, Hypergeometric, Normal, Exponential, Uniform). Central limit theorems. Elements of statistical inference. Random sample and sample statistics distributions. Estimators and interval estimation. Statistical hypotheses verification. Significance tests and goodness of fit test. Analysis of dependence between variables. Simple linear regression model. Time series analysis: trend and seasonality (multiplicative and additive), forecasting, statistical indexes. Knowledge about statistics (descriptive, economical, inferential), probability and statistical thinking, reading and processing statistical data, designing and realizing surveys, calculating and interpreting descriptive measures of statistical populations, meaning and using methods of statistical inference in statistical analysis, using estimators and verifying parametric and nonparametric hypotheses, constructing regression models and employing regression models in economics, using descriptive measures of dynamics e.g. indices of different aspects of economic and social life, knowledge about statistical software, like Statistica, SPSS, R and skills in making statistical calculation, tables and charts in spreadsheet, e.g. MS Excel or OO Calc. dr Cyprian Kozyra, room B-6, McClave J.T., Benson P.G. Statistics for Business and Economics.
16 Lecture hours: Study period: Location: Examination: Language: Prerequisites: Course content: Dellen, San Francisco Sobol M.S., Starr M.K., Statistics for Business and Economics, An action learning approach, McGraw-Hill, New York All nie albo tak - nazwa przedmiotu: Statystyka wydział: ZIF, NE kierunek: Finanse i rachunkowość I stopnia specjalność: wszystkie rok: II Systems Analysis and Design (SAD) 30 hours of lectures + 20 hours of mixed tutorial and practical sessions Winter or Summer semester Wrocław Assignments and written test (the latter in case of a larger class when the originality of assignment answers cannot be fully validated). English 1. Understanding of principles of information systems. 2. Understanding of fundamental information technologies. The course aims to provide an introduction to and competency in requirements acquisition, problem domain analysis and computer-based system design methods ensuring a close link between requirements and the resulting computer system. This course emphasises the skills of problem formulation, modelling and problem solving. Course topics: 5) Systems and Development Methodologies a) Types of Systems b) Integrating Technologies for Systems c) Need for Systems Analysis and Design d) The Systems Development Life Cycle 6) The Software Development Process a) The Nature of Software Development b) System Planning c) Systems for Different Management Levels d) Systems Development Phases and Activities 7) User Requirements Determination a) From Business Processes to Solution Envisioning b) Requirements Elicitation c) Requirements Negotiation and Validation
17 d) Requirements Management e) Requirements Business Model f) Requirements Document 8) Fundamentals of Systems Analysis a) Depicting Systems Graphically b) Modeling of Business Processes c) Modeling of Business Data d) Modeling of Business States 9) Fundamentals of Systems Design a) Moving from Requirements to Software Solution b) Designing the System Architecture c) Designing the Data d) Designing the Software e) Designing the Graphical User Interface Learning Understanding of various kinds of information systems and various approaches to development and integration of systems. Awareness of the life cycle of system development. Knowledge of requirements elicitation techniques and understanding of particular problem domains. Ability to analyse the system requirements and build a logical model of the problem. Appreciation of the importance of software and system architecture. Ability to turn the logical model from the analysis phase into a design model from which a system can be built. Recognition of how contemporary information technology and tools assist developers in production of information systems. Contact person: Literature: Czy przedmiot jest kopią przedmiotu Prof. Leszek A. Maciaszek web: KENDALL, K.E., KENDALL, J.E. (2011): Systems Analysis and Design, Global Edition, 8 th ed., Pearson, 560p. ISBN:13: MACIASZEK, L.A. (2007): Requirements Analysis and System Design, 3 rd ed., Pearson, 642p. ISBN Management, Informatics and Finance Tak: 3) Analiza i Modelowanie Systemów Informacyjnych (AiMSI) I rok licencjat Informatyka i Ekonometria 4) Analiza Systemów Informacyjnych (ASI) I rok licencjat Informatyka w Biznesie
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