Module Handbook - Universität Trier
Module HandbookMaster Degree ProgrammeM.Sc. Data Science28/11/2017
Universität TrierM.Sc. Data ScienceDifferent chairs/departmentsCurriculumM.Sc. Data Science1st Semester10 LP6 SWSIntroduction to DataScience2nd Semester10 LP6 SWSNumerical Optimization forData Science10 LP3rd Semester / Mobilitywindow10 LP2 SWS4th Semester302 SWSResearch Case Studies4 SWSStatistical Methods of DataScienceMaster s ThesisCompulsory modules5 LPData and Web Mining5 LP3 SWSBig Data Analytics10 LP6 SWSElements of Mathematics10 LP4 SWSElements of ComputerScienceElective modules3 SWS10 LP4 SWS20 LPChoose 2 of 3,depending onpreviousstudies (B.Sc.)Specialization(for module options seemodule handbook)Specialization areas:- Simulation Studies- Data andKnowledge Systems- AlgorithmicOptimization- Applied Statistics- Financial Economics- Geoinformatics- Others withoutspecializationElements of Statistics2
Universität TrierM.Sc. Data ScienceDifferent chairs/departmentsTable of contents1 Compulsory Modules . 42 Elective Modules . 18Preparation Courses . 18Specialization Modules . 24– Simulation Studies. 24– Data and Knowledge Systems . 34– Algorithmic Optimization . 44– Applied Statistics . 48– Financial Economics . 49– Geoinformatics . 53– Others (without Specialization) . -633
Universität TrierM.Sc. Data ScienceDifferent chairs/departments1 Compulsory ModulesModule TitleModule CodeIntroduction to Data ScienceDS.GENE.01Semester1st semesterDurationOne semesterTaught semestersEvery year (winter semester)LanguageEnglishCredits / ECTS10 CPSemester periods per week4 weekly hours (lecture)Contact hours60 hoursSelf-study240 hoursWorkload60 240 hoursType of assessmentexamNon-graded assessment tasksPrerequisitesNoneAssessment weightingThe module grade counts towards the final grade according to thenumber of awarded credit points.Learning outcomesAfter completing the module, students will have a thoroughunderstanding of what data science and big data are, which methodsare used in these fields and which ethical/legal considerations have tobe taken into account when working in these fields.CompetencesStudents are able to define data science and big data.Students understand the interrelation of ethics and, particularly, bigdata.Students can apply basic methods of data science to chosenproblems.ContentThe module introduces data science as a field and, in doing so, gives abroad overview of the contents taught throughout the degreeprogramme. Highlights are presented using small applications ofconcepts.4
Universität TrierM.Sc. Data ScienceDifferent chairs/departmentsThe topics covered include:- Overview of data science methods in computer science,mathematics, and statistics- Definition of data science- Definition of big data- Ethics/disclosure control and data scienceReadingAs the field is still rather young, there are no comprehensivetextbooks on data science yet. Nevertheless, many books cover majortopics in data science.More focussed references will be given in the course syllabus.Types of coursesLecture (Vorlesung) (4 weekly hours)Recommended prerequisitesNoneRequirements for awarding CPsRegular attendance at courses, successful completion of non-gradedassessment-tasks, passing of module exam.Module applicabilityCompulsory module in degree programme „Data Science“ (M.Sc.)Module ConvenorProf. Dr. Ralf Schenkel / Prof. Dr. Volker Schulz / Prof. Dr. RalfMünnichAdditional information5
Universität TrierM.Sc. Data ScienceDifferent chairs/departmentsModule TitleModule CodeNumerical Optimization for Data ScienceDS.MATH.02Semester2nd semesterDurationone semesterTaught semestersevery year (summer semester)LanguageEnglishCredits / ECTS10 CPSemester periods per week6 weekly hoursContact hours90 hoursSelf-study210 hoursWorkload300 hoursType of assessmentOral exam or written examNon-graded assessment tasksexercisesPrerequisitesnoneAssessment weightingThe module grade counts towards the final grade according to thenumber of awarded credit points.Learning outcomesThe students know the mathematical foundations in the area ofnumerical optimization and their relation to data science. They know,how to implement basic optimization methods and how to controladvanced optimization methods and to interpret their results. As partof the course, deepen knowledge in the programming languagePython.--Optimization problems in data scienceTheoretical foundations of nonlinear optimizationAlgorithms for nonlinear optimization: steepest descent, (Quasi-)Newton method, conjugate gradient method, quadraticprogramming, SQP methodsConvergence and complexity analysisJ. Nocedal and S. Wright: Numerical Optimization, 2nd Edition,Springer 2006Reading-Types of coursesLecture (4 weekly hours) and practical course (2 weekly hours)including exercisesRecommended prerequisitesnone6
Universität TrierM.Sc. Data ScienceDifferent chairs/departmentsRequirements for awarding CPsRegular attendance at courses, successful completion of non-gradedassessment-tasks, passing of module exam.Module applicabilityCompulsory module in degree programme „Data Science“ (M.Sc.)Module ConvenorProf. Dr. Volker SchulzAdditional information7
Universität TrierM.Sc. Data ScienceDifferent chairs/departmentsModule TitleModule CodeStatistical Methods of Data ScienceDS.STAT.02Semester2nd semesterDurationOne semesterTaught semestersEvery year (summer semester)LanguageEnglishCredits / ECTS10 CPSemester periods per week4 weekly hours (2 2, lecture and presentations)Contact hours60 hoursSelf-study240 hoursWorkload60 240 hoursType of assessmentPresentation (40%) and written exam (90 minutes; 60%)Non-graded assessment tasksPrerequisitesAssessment weightingThe module grade counts towards the final grade according to thenumber of awarded credit points.Learning outcomesAfter completing the module, students will have a solid knowledge oftools and statistical methods needed to deal with big data and datacollected using non-probability samples.CompetencesStudents are able to extract data from the internet and analyse suchdata, taking their collection into account.Students are able to finish a small practical project in the context ofbig data/data collected using non-probability sampling and to presentthe results in a concise manner.ContentThe module covers statistical methods that can be used to solvepractical problems in data science. Furthermore, awareness for thespecifics of the collection of big data etc. and related implications forthe analysis of big data is raised.Big data is an essential part of data science. The analysis of vastamounts of, potentially unstructured, data, i.e. data streams, requiresthe use of special methods which are introduced in this module.Furthermore, some programming tools needed to extract such dataare introduced as well.8
Universität TrierM.Sc. Data ScienceDifferent chairs/departmentsA key assumption behind traditional statistical methods is that datahas been sampled using some form of probability sampling. In thecontext of data science, this assumption often does not hold (e.g.people using a social network have explicitly chosen to do so, leadingto self-selection problems for a sample taken from the population ofusers of the social network). Proper analysis has to take this nonprobability into account.ReadingSome suggestions:Hastie, T.; Tibshirani, R.; Friedman, J. (2009): The Elements ofStatistical Learning. Data Mining, Inference, and Prediction. Springer.Prajapati, V. (2013): Big Data Analytics with R and Hadoop. PacktPublishing.More focussed references will be given in the course syllabus.Types of coursesLecture (Vorlesung) and presentations (2 2 weekly hours)Recommended prerequisitesElements of StatisticsRequirements for awarding CPsRegular attendance at courses, successful completion of (non-graded)assessment-tasks, passing of module exam.Module applicabilityCompulsory module in degree programme „Data Science“ (M.Sc.)Module ConvenorProf. Dr. Ralf MünnichAdditional information9
Universität TrierM.Sc. Data ScienceDifferent chairs/departmentsModule TitleModule CodeData and Web MiningDS.INFO.03Semester2nd semesterDurationone semesterTaught semestersevery summer semesterLanguageEnglishCredits / ECTS5 CPSemester periods per week3 weekly hoursContact hours45 hoursSelf-study105 hoursWorkload150 hoursType of assessmentwritten exam (90 minutes)Non-graded assessment tasksexcercisesPrerequisitesnoneAssessment weightingThe module grade counts towards the final grade according to thenumber of awarded credit points.Learning outcomes students are able to explain the various data mining algorithms indetail and to evaluate them with respect to their advantages andshortcomingsstudents are able to apply the various data mining methods forthe analysis of given data using a data mining tool and tointerpret the resultsstudents are aware of the standard data mining process modelstudents are aware of the criteria for the selection of data miningtoolsCompetences ability to structure and aggregate new information andknowledge in the field of data and web mining self-directed and self-organised learning professional use of advanced information technology presentation skillsContent clarification of terms: knowledge discovery, data mining, webmining, data warehouseslearning conjunctive concepts10
Universität TrierM.Sc. Data ScienceReadingDifferent chairs/departments learning of decision treesanalogy-based learningprobabilistic learningneural netscluster analysisweb mining & recommender systemsdata pre-processingdata mining toolspractical exercises with Rapid Miner Tom Mitchell (1997). Machine Learning. McGraw-Hill. Ian H. Witten & Eibe Frank (2011). Data Mining: Practical MachineLearning Tools and Techniques. Morgan Kaufmann Bing Liu (2011). Web Data Mining: Exploring Hyperlinks,Contents, and Usage Data (Data-Centric Systems andApplications). SpringerTypes of courses(a) lecture (2 weekly hours) and practical course (1 weekly hours)including exercisesRecommended prerequisitesfoundational knowledge in computer science and algorithmsRequirements for awarding CPsRegular attendance at courses, successful completion of non-gradedassessment-tasks, passing of module examModule applicabilityCompulsory module in degree programme “Data Science” (M. Sc.)Elective module in degree programme “Informatik” (M.Sc.)Elective module in degree programme “Wirtschaftsinformatik”(M.Sc.)Elective module in degree programme “Digital Humanities” (M.Sc.)Elective module in degree programme “Wirtschaftsinformatik” (B.Sc.)Module ConvenorProf. BergmannAdditional informationLast edited, Oct 19,2017.11
Universität TrierM.Sc. Data ScienceDifferent chairs/departmentsModule TitleModule CodeBig Data AnalyticsDS.INFO.02Semester2nd semesterDurationone semesterTaught semestersevery year (summer semester)LanguageEnglishCredits / ECTS5 CPSemester periods per week3 weekly hoursContact hours45 hoursSelf-study105 hoursWorkload150 hoursType of assessmentwritten exam (90 minutes) or oral (individual) examNon-graded assessment tasksexercisesPrerequisitesnoneAssessment weightingThe module grade counts towards the final grade according to thenumber of awarded credit points.Learning outcomesThe course provides in-depth methodological knowledge on solvingtypical analytical problems on big data with standard softwarepackages. Students acquire profound methodological knowledge andare, therewith, prepared to solve analytical tasks with standardapproaches on large data collections. They are able to choose the besttool for a given application scenario. Students also know theunderlying theoretical foundations of these systems. Furthermore,students develop an in-depth understanding of the core approachesand algorithms for data organization and data processing.Content distributed file systems with HDFS as an example the map-reduce programming paradigm with Apache Hadoop as anexample implementation of simple analysis tasks with Hadoop systems for processing big data, including Apache Spark andApache Flink typical analysis on big data collections and their implementation,e.g., recommender systems, graph analysis, text analysis, machinelearning, geometric and temporal analysis NoSQL databases, including Apache HBase, Apache HIVE, andMongoDBReading Guy Harrison: Next Generation Databases: NoSQL, NewSQL,and Big Data. Apress, 2015. ISBN 978-1484213308Tom White: Hadoop: The Definitive Guide. O’Reilly UK Ltd.,12
Universität TrierM.Sc. Data ScienceDifferent chairs/departments 2015. ISBN 978-1491901632Martin Kleppmann: Designing Data-Intensive Applications:The Big Ideas Behind Reliable, Scalable, and MaintainableSystems. O’Reilly UK Ltd., 2017. ISBN 978-1449373320Sandy Ryza, Uri Laserson, Josh Wills, Sean Owen: AdvancedAnalytics with Spark (2nd edition). O’Reilly UK Ltd., 2017.ISBN 978-1491972953Types of courses(a) lecture (2 weekly hours)(b) practical course (1 weekly hour) including exercisesRecommended prerequisitesNoneRequirements for awarding CPsRegular attendance at courses, successful completion of non-gradedassessment-tasks, passing of module exam.Module applicabilityCompulsory module in degree programme „Data Science“ (M.Sc.)Elective module in degree programme „Informatik“ (M.Sc.)Module ConvenorProf. SchenkelAdditional informationnone13
Universität TrierM.Sc. Data ScienceDifferent chairs/departmentsModule TitleModule CodeResearch Case StudiesDS.GENE.02Semester3rd semesterDurationOne semesterTaught semestersEvery year (winter semester)LanguageEnglishCredits / ECTS10 CPSemester periods per week2 weekly hours (colloquium/seminar)Contact hours30 hoursSelf-study270 hoursWorkload30 270 hoursType of assessmentPortfolioNon-graded assessment tasksPrerequisitesAssessment weightingThe module grade counts towards the final grade according to thenumber of awarded credit points.Learning outcomesIn this module, students autonomouslypursue and answer a specific research question in an individualproject. They, therewith, gainexperience in planning and conducting data science research.CompetencesStudents practice the intensive engagement with a complex datascience problem, the implementation of respective methods in a programming language, and thewriting of a scientific thesis.ContentThe topic is chosen after consultation with the individual advisor.ReadingReferences will be given.Types of coursesIndividual counselling, meetings in small groups, seminar, colloquiumRecommended prerequisitesCurriculum of first 2 semestersRequirements for awarding CPsRegular attendance at courses, successful completion of non-gradedassessment-tasks, passing of module exam.14
Universität TrierM.Sc. Data ScienceDifferent chairs/departmentsModule applicabilityCompulsory module in degree programme „Data Science“ (M.Sc.)Module ConvenorProf. Dr. Ralf Schenkel / Prof. Dr. Volker Schulz / Prof. Dr. RalfMünnich plus other professors of computer sciences, mathematicsand statistics/economicsAdditional information15
Universität TrierM.Sc. Data ScienceDifferent chairs/departmentsModule TitleModule CodeMaster‘s ThesisDS.GENE.03Semester4th semesterDurationOne semesterTaught semestersEvery year (summer semester)LanguageEnglishCredits / ECTS30 CPSemester periods per week2 weekly hours (research colloquium and master‘s thesis)Contact hours30 hoursSelf-study720 hoursWorkload30 720 hoursType of assessmentPresentation of intermediate results; thesisNon-graded assessment tasksPrerequisitesCompletion of Preparation coursesAssessment weightingThe module grade counts towards the final grade according to thenumber of awarded credit points.Learning outcomesStudents learn how to write a scientific thesis and present therespective results.CompetencesSee above.ContentThe topic is chosen after consultation with the individual advisor.ReadingReferences will be given.Types of coursesIndividual counselling, research colloquiumRecommended prerequisitesCurriculum of first 3 semestersRequirements for awarding CPsPassing of thesis (module exam)Module applicabilityCompulsory module in degree programme „Data Science“ (M.Sc.)Module ConvenorProf. Dr. Ralf Schenkel / Prof. Dr. Volker Schulz / Prof. Dr. RalfMünnich plus other professors of computer sciences, mathematics16
Universität TrierM.Sc. Data ScienceDifferent chairs/departmentsand statistics/economicsAdditional information17
Universität TrierM.Sc. Data ScienceDifferent chairs/departments2 Elective ModulesPreparation CoursesModule TitleModule CodeElements of MathematicsDS.MATH.01Semester1st semesterDurationone semesterTaught semestersevery year (winter semester)LanguageEnglishCredits / ECTS10 CPSemester periods per week6 weekly hoursContact hours90 hoursSelf-study210 hoursWorkload300 hoursType of assessmentwritten exam (120 minutes)Non-graded assessment tasksexercises (Übungsaufgaben)PrerequisitesnoneAssessment weightingThe module grade counts towards the final grade according to thenumber of awarded credit points.Learning outcomesAfter completing the module, the students know the mathematicalfoundations in the areas of linear algebra and numerical mathematics.As part of the course, they acquire or deepen knowledge in theprogramming language Python.CompetencesStudents are able to use linear and selected nonlinear mathematicalmodels in Data Science and to treat them in a numerically sound way.They can implement basic numerical methods and know how tointerpret results from advanced numerical algorithms.Content-Linear mappings and matricesMatrix decompositions and linear equationsEuclidean vector spaces and linear least squares problemsEigenvalues and singular value decompositionNumerical interpolation and integrationSolution of nonlinear equations and least squares problems18
Universität TrierM.Sc. Data ScienceReadingDifferent chairs/departments-C. D. Meyer: Matrix analysis and applied linear algebra, SIAM2001P. Deuflhard and A. Hohmann: Numerical Analysis in ModernScientific Computing: An Introduction, 2nd Edition, Springer2003Types of coursesLecture (4 weekly hours) and practical course (2 weekly hours)including exercisesRecommended prerequisitesnoneRequirements for awarding CPsRegular attendance at courses, successful completion of non-gradedassessment-tasks, passing of module exam.Module applicabilityElective module in degree programme „Data Science“ (M.Sc.)Module ConvenorProf. Dr. Volker SchulzAdditional information19
Universität TrierM.Sc. Data ScienceDifferent chairs/departmentsModule TitleModule CodeElements of Computer ScienceDS.INFO.01Semester1st semesterDurationone semesterTaught semestersevery year (winter semester)LanguageEnglishCredits / ECTS10 CPSemester periods per week4 weekly hoursContact hours60 hoursSelf-study240 hoursWorkload300 hoursType of assessment2 partial written exams (120 minutes (50%) 90 minutes (50%))Non-graded assessment tasksexercisesPrerequisitesnoneAssessment weightingThe module grade counts towards the final grade according to thenumber of awarded credit points.Learning outcomesThe course provides fundamental knowledge of computer scienceconcepts, including foundations of programming, foundations of datamanagement, and foundations of algorithms. Students acquireprofound methodological knowledge in these topics. They are able todesign programs of medium complexity and implement them with anobject-oriented programming language. They are aware of typicaldata structures for storing information and algorithms for accessingthem. Students are also prepared to represent and query informationwith relational databases.ContentFoundations of Computer Science Programming paradigms Formal representation of algorithms Important algorithms for representing and accessing data as sets,lists, mapsFoundations of a typical programming language (Java) Data types Control flow Procedural programming Classes and object-oriented programming Designing and testing nontrivial programsFoundations of Databases20
Universität TrierM.Sc. Data ScienceDifferent chairs/departments Models for representing data Conceptual data modelling with the entity-relationship model The relational model Query languages: relational calculus, relational algebra, SQL Database normalizationReading Robert Sedgewick, Kevin Wayne: Computer Science: Aninterdisciplinary Approach, Addison Wesley, 2016, ISBN 9780134076423Hector Garcia-Molina, Jeffrey D. Ullman, Jennifer Widom:Database Systems – The complete book, Pearson Education,2013, ISBN 978-1292024479Types of courses(a) flipped classroom with self-study of pre-recorded online coursesand additional tutorial (1 weekly hour)(b) practical course (3 weekly hours) including exercisesRecommended prerequisitesnoneRequirements for awarding CPsRegular attendance at courses, successful completion of non-gradedassessment-tasks, passing of module exam.Module applicabilityElective module in degree programme „Data Science” (M. Sc.)Module ConvenorProf. SchenkelAdditional informationnone21
Universität TrierM.Sc. Data ScienceDifferent chairs/departmentsModule TitleModule CodeElements of StatisticsDS.STAT.01Semester1st semesterDurationOne semesterTaught semestersEvery year (winter semester)LanguageEnglishCredits / ECTS10 CPSemester periods per week4 weekly hours (programming course and tutorials)Contact hours60 hoursSelf-study240 hoursWorkload60 240 hoursType of assessmentwritten exam (120 minutes)Non-graded assessment tasksParticipation in e-tutorials as prerequisite to take examPrerequisitesNoneAssessment weightingThe module grade counts towards the final grade according to thenumber of awarded credit points.Learning outcomesAfter completing the module, students will have a solid knowledge ofcore concepts of descriptive and inferential statistics as well asregression modelling.Additionally, students will be familiar with the open source statisticalprogramming language and environment R.The module prepares students for the advanced statistics modules ofthe degree programme.CompetencesStudents are able to adequately analyse data and present therespective results in suitable tables and graphics. Furthermore, theyare able to estimate relevant parameters and test hypotheses.Students can implement standard statistical and computationalmethods, visualise statistical content and produce reusableprogramming code in R.Students improved their self-dependent learning skills using theoffered e-tutorials and pre-recorded e-learning videos.22
Universität TrierM.Sc. Data ScienceContentDifferent chairs/departmentsThe module covers statistics and statistical programmingpropaedeutics needed for a successful completion of the degreeprogramme.In particular, core concepts of descriptive and inferential statistics areintroduced/refreshed. These include frequency tables, measures ofcentral tendency and variation as well as measures of association, thefundamentals of probability theory and random variables, chosendistributions, estimation and hypothesis testing. Special attention ispaid to methods of regression analysis.In a first block, the basics of the open source statistical programminglanguage and environment R are introduced. The topics coveredinclude the basic syntax and central commands, graphics andstatistical programming.ReadingSome suggestions:Crawley, M.J. (2015): Statistics: An Introduction Using R. 2nd edition.John Wiley & Sons.Field, A.; Miles, J.; Field, Z. (2012): Discovering Statistics Using R.SAGE Publications.Wooldridge, J.M. (2013): Introductory Econometrics: A ModernApproach. Cengage Learning.More focussed references will be given in the course syllabus.Types of coursesProgramming course and tutorials (Übung) (4 weekly hours),including flipped classroom elements, accompanying e-tutorials ande-learning videos, deepening students’ understanding of the topicscovered in the latterRecommended prerequisitesNoneRequirements for awarding CPsSuccessful completion of non-graded assessment tasks and passing ofmodule examModule applicabilityElective module in degree programme „Data Science“ (M.Sc.)Module ConvenorProf. Dr. Ralf MünnichAdditional information23
Universität TrierM.Sc. Data ScienceDifferent chairs/departmentsSpecialization ModulesSimulation StudiesModule NameModule CodeSimulation and ManagementDS.INFO.11Semester2nd or 4th semesterDurationOne semesterTaught semestersEvery year (summer)LanguageGermanCredits / ECTS5 CPSemester periods per week3 weekly hoursContact hours45 hSelf-study105 hWorkload150 hType of assessmentOral examNon-graded assessment tasksExercise sheetsPrerequisitesnoneAssessment weightingThe module grade counts towards the final grade according to thenumber of awarded credit points.Learning outcomes Content Factual and method knowledge about foundations of decisionsupport and simulationFactual and method knowledge about design, execution, andanalysis of simulation experimentsUnderstanding of possible applications of simulation in a businesscontextDecision making and decision support Cognitive decision process Approaches for decision support Limitations of decision supportFoundations of simulation Systems and Processes Simulation techniques Random numbers Queuing theory Examples of applicationCalibration, Validation, and Verification24
Universität TrierM.Sc. Data ScienceDifferent chairs/departments Reading Abstraction and discretization Calibration Validation VerificationProcess of simulation studies and experiments Design Execution AnalysisSimulation of business processes Goals of simulating business processes Discrete-event modeling of business processesMaterial flow simulation Goals of material flow simulation Modeling of material flow systems Optimization of logistical system Simulation of distributed production systems (SupplyChain)Agent-based Social Simulation Foundations of agent-based simulation models Techniques for controlling agent-based simulation Modeling of social systems Simulation of emergent effectsFurther concepts Distributed simulation Parallel simulationLaw, A. M. (2013). Simulation modeling and analysis. McGrawHill, New York. [ISBN 978-0-07-340132-4]Montgomery, D. C. (2013). Design and Analysis of Experiments.John Wiley and Sons, Singapore. [ISBN 978-1-118-09793-9]Banks, J.; Carson II, J. S.; Nelson, B. L. & Nicol, D. M. (2014).Discrete-Event System Simulation. Pearson Education. [ISBN978-1-29202-437-0]Types of coursesLecture (2 SWS) and exercise (1 SWS) with homeworkRecommended prerequisitesnoneRequirements for awarding CPsRegular attendance at courses, successful completion of non-gradedassessment-tasks, passing of module exam.Module applicabilityModule ConvenorProf. Dr.-Ing. Ingo J. TimmAdditional information25
Universität TrierM.Sc. Data ScienceDifferent chairs/departmentsModule TitleModule CodeAgent-Based ModelingDS.INFO.06(Agentenbasierte Modellierung)Semester2nd semesterDurationone semesterTaught semestersevery summer semesterLanguageEnglish or GermanCredits / ECTS5 CPSemester periods per week3 weekly hoursContact hours45 hoursSelf-study105 hoursWorkload150 hoursType of assessmentportfolio examinationNon-graded assessment tasksnonePrerequisitesnoneAssessment weightingThe module grade counts towards the final grade according to thenumber of awarded credit points.Learning outcomes students are familiar with methods, procedures, and tools foragent-based modelingstudents are able to explain different agent architectures and toapply them to a given modeling problemstudents are able to analyze real world application scenarios andto transfer them into computational modelsstudents are able to verify, calibrate, and validate agent-basedmodels using basic methods and toolsstudents are able to conduct and interpret simple simulationexperiments with agent-based modelsCompetences ability to structure and aggregate new information andknowledge in the field of agent-based modeling and simulation self-directed and self-organized learning teamwork professional use of advanced information technology presentation skills26
Universität TrierM.Sc. Data ScienceContentDifferent chairs/departments ReadingFoundations of systems and their structural abstraction intoactor-oriented (i.e., agent-based) modelsRepresentation of agents and their environments in conceptualand computational modelsAgent architectures: Reactive, goal-oriented, and utility-basedagents; psychological and sociological foundations of intelligentagentsAgent communication and coordinationProcedure models, requirements, and design principles for agentbased modelingFoundations of hypotheses, model verification and validation,and agent-based simulationImplementation of agent-based models in NetLogoApplications of agent-based models in the social sciences,economics, and logistics Uri Wilensky and William Rand. An Introduction to Agent-BasedModeling: Modeling Natural, Social, and Engineered ComplexSystems with Netlogo. The MIT Press, 2015 Hamill, Lynne, and Nigel Gilbert
Students are able to define data science and big data. Students understand the interrelation of ethics and, particularly, big data. Students can apply basic methods of data science to chosen problems. Content The module introduces data science as a field and, in doing so, gives a broad
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Unified Modeling Language (UML) Peter Sturm Universität Trier Bereiche der Modellierung . UML Diagramme. Software Reuse Sommer 2004 (c) Peter Sturm, Universität Trier 16 . Association Classes Beziehung selbst hat Eigenschaften Company Person * 1.* employer employee Job description
7 Happ Rd Northfield, IL 60093-3411 phone 847.784.3408 johnsonc@newtrier.k12.il.us October 14, 2021 President, Members of the Board of Education, and Citizens of New Trier Township New Trier Township High School District 203 Northfield, Illinois 60093 Introduction
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3 Scienti c and Organizing Committees 3.1 Scienti c Committee ( ) Liliane Basso Barichello (UFRGS, Porto Alegre, lbaric@mat.ufrgs.br)( ) Piermarco Cannarsa (Universit a di Roma Tor Vergata, Roma,cannarsa@axp.mat.uniroma2.it)( ) Ciro Ciliberto (Universit a di Roma Tor Vergata, Roma, cilibert@axp.mat.uniroma2.it)- co-chair ( ) Giorgio Fotia (Universit a di Cagliari, Giorgio.Fotia@crs4.it)
Clemens Berger, Universit e de Nice-Sophia Antipolis: cberger@math.unice.fr Richard Blute, Universit e d’ Ottawa: rblute@uottawa.ca Lawrence Breen, Universit e de Paris 13: breen@math.u
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