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D. Data Analytics Methods1. OverviewModuleno.AC 652DA 110Name of ModuleAdditional Course – Data AnalyticsMethodsComputational Analysis 2*17HWSE618HWS/FSSE6BI*IE 500Data Mining IIE 560Decision SupportHWSE6BI*IE 661Text AnalyticsHWSE6BI*IE 671Web MiningFSSE3BI*IE 672Data Mining IIFSSE6BI*IE 675bMachine LearningHWSE9BI*IE 676Network AnalysisHWSE6BI*IE 678Deep LearningFSSE6BI*IE 684Web Mining ProjectFSSE3BI*IE 692Advanced Process B 504 Mathematics and InformationirregularG8MBE*MAB 508 Algebraische StatistikirregularG/E8MBE*HWSG8WM*IE 694IE 696IS 622Artificial Intelligence Applicationsin IndustryAdvanced Methods in TextAnalyticsNetwork ScienceMAA 519 Stochastic CalculusMAC 404OptimierungMAC 502Computational FinanceFSSG/E6MBE*MAC 507Nichtlineare OptimierungFSSG/E6MBE*MAC 527Markov ProcessesFSSE4MBE*MKT 511Marketing AnalyticsFSSE6MMM*MKT 545Customers, Markets and FirmStrategyFSSE6MMM*15

Lecture Cross Sectional Data AnalysisTutorial Cross Sectional Data AnalysisLecture Advanced QuantitativeMethodsTutorial Advanced PS*Lecture Longitudinal Data AnalysisFSSE6Soc*Tutorial Longitudinal Data AnalysisFSSE3Soc*Lecture Research DesignHWSE6Soc*Tutorial Research DesignHWSE3Soc** For a detailed description, please see the module catalogs of the respective following degree programs: BI: M.Sc. Business Informatics, anisation/m-sc-business-informatics/ WM: B.Sc. Wirtschaftsmathematik, anisation/b-sc-wirtschaftsmathematik/ (only available in German) MBE: M.Sc. Mathematics in Business and Economics, anisation/m-sc-wirtschaftsmathematik/ (only available in German) PS: M.A. Political Science, de/politikwissenschaft/ma-political-science/ (only available in German) Soc: M.A. Sociology, de/soziologie/masociology/ MMM: Mannheim Master in Management, /#c17663716

AC 652Additional Course – Data Analytics MethodsForm of moduleDepends on courseLevelMasterECTSMax. 18WorkloadDepends on coursePrerequisitesDepends on courseAim of moduleThe course falls into the data analytics methods area of theMMDS and covers topics related to data analytics methods,but is not directly equivalent to any course in the MMDS module catalogue. The course level equals a regular course inMMDS study program. The module can be taken either at theUniversity of Mannheim or at any other university in Germanyor abroad.Learning outcomesqualification goalsandDepends on courseMedia / Literature / Methods / Form and duration of Depends on courseassessmentLanguageEnglish preferred, but any other language possible if Mannheim faculty member is able to identify content and levelOfferingSpring semester / Fall semesterLecturerLecturer at the host universityPerson in chargeLecturer at the host universityDuration of module1 SemesterFurther modules-Range of applicationMMDSSemester2nd/3rd/4th semester17

DA 110Computational Analysis of CommunicationForm of moduleExerciseType of moduleData Analytics MethodsLevelMasterECTS6WorkloadPrerequisitesAim of moduleLearning outcomes andqualification goalsHours per semester present: 28 (2 SWS)Self-study: 145h (70h lectures/exercises, 75h research report)Basic skills in descriptive and inferential statistics, basicknowledge of data structures and data wrangling procedures,machine learning, web-scraping/web-miningAs “big data” and “algorithms” affect our daily communication, new research questions arise at the intersection betweensocieties and technologies. Many of these questions are ofgreat social relevance and are therefore prominently discussed both by researchers and in the media. One outstanding, recent example from the field of media psychology is a rising interest in the association of (social) media use and mental-health. Another example, from the realm of political communication, is the ongoing debate about the role of new communication technologies during political campaigns (e.g., tospread disinformation). Both questions revolve around theprocess of communication. Sound research in this area thusrequires both a solid foundation from communication theoryas well as expertise in handling new and “big” data. To closethis gap, the growing discipline of Computational Communication Science (CCS) takes on a combinatory perspective between social and computer science. The present course willprovide an overview about the current state of CCS and intends to motivate students to approach pressing social questions from a different perspective.Expertise:After the course the students are aware of the typical researchtopics and questions in automated media content analysesand the different methodological approaches for tacklingthem; they know the different methods’ potentials, limitations, and typical fields of application; they are able to developtheir own specific research questions and can make an informed decision about which method to apply for answeringitMethodological competence:Students are able to independently develop a research question and design in the area of automated media content18

analysis and can conduct a respective analysis using one of thedifferent methodological approaches introduced in the exercise; they are able to document the results of their analyses ina research report and reflect upon their findings’ limitationswith regards to reliability and validityPersonal competence:The course supports students to develop problem-solvingcompetences with regards to research-design oriented questions. By solving exercises independently, the transfer of thelearned material to related questions is promoted and selfconfidence with regards to research-oriented tasks is gathered.MediaLiteratureMethodsExercise sheets and lecture slides are available onlinevan Atteveldt, W., Trilling, D., & Arcila, C. (2021). Computational Analysis of Communication: A practical introduction tothe analysis of texts, networks, and images with code examples in Python and R. http://cssbook.net/Lecture elements, student presentations, weekly exercises,literature studiesForm of assessmentWritten research reportAdmission requirementsfor assessment-Duration of rson in chargeMKWDuration of module1 semesterFurther modules-Range of applicationM.Sc. Data ScienceSemester1st/ 2nd/3rdsemester19

E. Responsable Data Science1. OverviewModuleno.Name of ModuleCS 652Data Security and PrivacyCS 718OfferedLanguageECTSPageFSSE6BI*Legal and Ethical Aspects of PrivacyHWSE321AI and Data Science in Fiction andSocietyHWSE423* For a detailed description, please see the module catalogs of the respective following degree programs: BI: M.Sc. Business Informatics, anisation/m-sc-business-informatics/20

2. Detailed DescriptionLegal and Ethical Aspects of PrivacyForm of moduleLectureType of moduleResponsible Data ScienceLevelMasterECTS3WorkloadHours per semester present: 28 h (2 SWS)Self-study per semester: 60 h Pre-and post-lecture studying and preparation (30h) Examination preparation (30h)PrerequisitesNoneAim of moduleIn a first section the course will acquaint the students with theorigins and basic principles of privacy law mainly in Europe.Furthermore, it will contrast the European privacy foundationswith the U.S. approach. At the core of this course stands thenew European General Data Protection Regulation (GDPR) andits applicability to specific cases and basic principles. Moreover, the course will cover current challenges to the existing privacy paradigms by big data and big data analytics.In a second section the course will cover ethical aspects of theuse of personal and non-personal data. Data potentially allowsto identify and target individuals and offer individualized products to them. However, sometimes this kind of individualization might be legal but the question arises whether it is alsodesirable from an ethical and societal point of view? Thecourse will use selected examples (e.g. first-degree price discrimination) in order to illustrate the ambivalence of legality,legitimacy, and ethics. In this context, the use of artificial intelligence and its impact on privacy will be addressed.Learning outcomesqualification goalsStudents will have a basic knowledge on the applicability of the General DataProtection Regulation (GDPR) and its basic principles; be aware of privacy issues and potential legal limitations whenprocessing data;and be aware of current challenges to the existing privacy have an understanding why privacy issues are treated differently in Europe and the U.S.; paradigms by big data and big data analytics; be aware of currently discussed new approaches to privacy (e.g.privacy by design); be aware of ethical issues of using personal as well as non21

MediaLiteratureMethodspersonal databe aware of the chances and challenges the use of artificial intelligence will bringVideo tutorials, lectures, online quizzesStudents will receive reading assignments for each unit together with the syllabus at the beginning of the semester.The class will generally be conducted as a lecture. However,some of the sessions will be conducted on an inverted classroom principle. Students will be able to access the video lectures at the beginning of the semester. The content of thesevideos will be discussed along with additional reading in theindividual class sessions.Form of assessmentwritten examinationAdmission requirementsfor assessmentSuccessful participation in 5 out of at least 7 online quizzesDuration of assessment90 minutesLanguageEnglishOfferingHWSLecturerProf. Dr. Thomas FetzerPerson in chargeProf. Dr. Thomas FetzerDuration of module1 semesterFurther modules-Range of applicationMMDSSemester1st/2nd/3rd semester22

CS 718AI and Data Science in Fiction and SocietyForm of ModuleSeminarType of ModuleSeminarLevelMasterECTS4Workload120 h per semesterPrerequisitesBachelor degreeAim of moduleLearning Outcomes andQualification GoalsIn this seminar, students analyze and discuss fictional worksin the area of AI and data science with respect to technological and societal aspects. The present the results orally and ina written report.Expertise:Students will learn about societal effects of AI and data science and become aware of potential threats and dangers,but also of chances of those new technologies.Methodological competence:Students will develop methods and skills to find relevant literature for his/her topic, and to write a well-structured scientific paper and to present his/her results. He/she will be alsoaware of the need to avoid plagiarism. The key qualificationScientific Research is highly recommended as a prerequisitefor the seminar.Personal qualification:Students will acquire skills on how to find relevant literaturefor a research topic, discuss a fictional work using secondaryliterature as background material, write a well-structured,concise paper about it and present the results of their work.He/she is well prepared to write and present a Master’s Thesis.MediaFictional and non-fictional textsLiteratureA detailed literature list is compiled for each offering.Teaching and LearningMethodsDo scientific work independently under the guidance of aprofessor or a research staff memberForm of AssessmentGrading of the seminar paper, Peer Review, Presentation23

Admission requirementsfor assessmentDuration of AssessmentN/ALanguageEnglishOfferingFall semesterLecturersProf. Dr. Heiko Paulheim and research staff membersPerson in chargeProf. Dr. Heiko PaulheimDuration of module1 semesterFurther modules-Range of ApplicationMMDS, M. Sc. Wirtschaftsinformatik, Lehramt für GymnasienSemester3. Semester24

F. Projects and Seminars1. OverviewModuleno.Name of ModuleOfferedLanguageECTSPageAC 653Additional Course – Projects andSeminarsHWS/FSSEAC653*26TP 500Team ProjectFSS/HWSG/E12BI*IP 500Individual ProjectirregularG/E827SQ 500Scientific ResearchHWS/FSSE2BI*FSSE4BI*HWS/FSSE4BI*CS 701CS 704Seminar Selected Topics in Algorithmics and CryptographyMaster Seminar Artificial IntelligenceCS 705DatenbankseminarHWS/FSSG4BI*CS 707Seminar Data and Web ScienceHWS/FSSE4BI*CS 708Seminar Software EngineeringHWS/FSSE4BI*CS 709Seminar Text AnalyticsHWS/FSSG/E4BI*CS 710Seminar Prof. PaulheimHWS/FSSG/E4BI*CS 715Seminar Large Scale Data IntegrationFSSE4BI*CS 716Seminar Prof. ArmknechtHWSE4BI*CS 719Seminar on Process AnalysisHWS/FSSE4BI*CS 720Uncertainty EstimationFSSE4BI*CS 721Seminar Data-Science IFSSE4BI*IE 704Seminar AI Systems EngineeringHWS/FSSE4BI** For a detailed description, please see the module catalogs of the respective following degree programs: BI: M.Sc. Business Informatics, anisation/m-sc-business-informatics/25

2. Detailed descriptionsAC 653Additional Course – Projects and SeminarsForm of moduleDependsLevelMasterECTSMax. 18WorkloadDependsPrerequisitesDependsAim of moduleThe course equals a seminar in the MMDS study program. Themodule can be taken either at the University of Mannheim orat any other university in Germany or abroad.Learning outcomes andqualification goalsDepends on courseMedia / Literature / Methods / Form and duration of DependsassessmentLanguageEnglish preferred, but any other language possible if Mannheim faculty member is able to identify content and levelOfferingSpring semester / Fall semesterLecturerLecturer at the host universityPerson in chargeLecturer at the host universityDuration of module1 SemesterFurther modules-Range of applicationMMDSSemester2nd/3rd/4th semester26

IP 500Individual ProjectForm of moduleProjectType of moduleIndividual ProjectLevelMasterECTS8WorkloadSelf study: 240 h per semesterPrerequisitesDepends on topicAim of ModulesThe student solves a practical problem individually. The student has to analyse and refine the problem and come up witha project plan for developing a concrete solution. Concretetopics for projects are defined by the supervisors and offeredto the students who can apply for different topics. Problemarea and techniques involved depend on the expertise of theoffering chair.Learning outcomes andqualifications goalsDepending on the actual topic of the project, participants willacquire in-depth knowledge in a certain application of data science knowledge about methods and technologies typically applied in the application area knowledge about practical problems and challenges whenapplying a certain technique in a given application areaParticipants will learn to refine a given problem statement by analysing requirements and the state of the art using techniques like literature research and expert interviews. define a workplan including tasks, milestones, deliverables and resources and continually assess and modify theplan according to the actual progress of the work.MediaDepends on projectLiteratureDepends on topicMethodsSelf study, presentationsForm of AssessmentFinal report and presentation27

Admission requirementsfor assessment-Duration of Assessment15 minutes (presentation)LanguageEnglish/GermanOfferingSpring semester/Fall semesterLecturerPerson in ChargeProfessors of the Institute of School of Business Informaticsand Mathematics or of the School of Social SciencesA professor of the Institute of School of Business Informaticsand Mathematics of the School of Social SciencesDuration of module1 semesterFurther modules-Range of ApplicationsMMDSSemester1st/2nd/3rd semester28

G. Master ThesisMaster ThesisForm of moduleMaster ThesisType of moduleThesisLevelMasterECTS30WorkloadSelf study: 840 h per semesterPrerequisites-Aim of ModulesLearing outcomes andqualifications goalsDevelop a deep understanding of an advanced topic of datascienceExpertise:The student has a deep understanding of an advanced topic.(MK1)Methodological competence:The student is familiar with methods for analysing and independently solving advanced, complex problems.(MK1, MK2, MK3)Personal competence:The student has the capability to understand, analyse and independently find solutions to advanced, complex problems.The student has the capability to assess and understand thestate-of-the-art in business informatics and adapt the latesttechnologies and methods to solve real world problems.The student is able to present a complex topic in written andoral form in a clear and understandable way.(MF1, MF2, MF3, MF4, MKO2, MKO3)MediaVariousLiteratureTopic dependentMethodsIndependent research workForm of AssessmentWritten thesisAdmission requirementsfor assessmentTo be permitted to write the master thesis, the stud

Duration of assessment 90 minutes (written exam)/30 minutes (oral exam) Language English Offering Spring semester Lecturer Prof. Dr. Heiko Paulheim Person in charge Prof. Dr. Heiko Paulheim Duration of module 1 semester Further modules Database Systems II, Transaktionssysteme, Anfrageoptimie-rung, Large Scale Data Management