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Methodological Competence
  • Reproduce the contents of software engineering and programming cases
  • describe various problems and the appropriate solutions as encountered in the cases
  • explain the differences and similarities of various methods of software engineering by means of case studies
  • explain the differences and similarities of relational and object-oriented approaches to modeling using case studies
  • explain the differences and similarities of various data and control structures of programming
  • Solve case studies with the help of the learned concepts and methods
  • Apply IDE and CASE tools
  • analyze complex issues and identify appropriate concepts and methods for their solution
  • Create simple object-oriented programs (e.g., in the learning environments Scratch or Greenfoot)
  • Apply best practices (e.g., design patterns, CASE tools, quality factors)
Professional Competence
  • list methods of software engineering (e.g., waterfall model, agile methods)
  • list relational and object-oriented approaches to modeling (e.g., ERM, UML)
  • understand the elements of different modeling languages (e.g., ERM, UML)
  • understand key concepts of SQL
  • understand key concepts of object-oriented programming (e.g., classes, attributes, methods, relationships, inheritance)
  • create data and system models (e.g., ERM, UML)
  • perform normalization of database tables
  • write queries in SQL
  • write simple object-oriented programs
  • compare and contrast key programming structures (e.g., data structures, control structures)
  • identify best practices of software engineering and programming (e.g., design patterns, quality factors)
  • design object-oriented applications by applying the methods learned
  • choose appropriate methods for given tasks
Personal Competence
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Social Competence
  • nehmen Argumente von Mitstudierenden wahr und auf
  • arbeiten in Gruppen gemeinsam an der Lösung kleiner Fallbeispiele
  • beurteilen die Lösungen von Kollegen, bewerten diese relativ zur eigenen Lösung
  • nehmen neue oder alternative Lösungsansätze auf und verknüpfen diese mit eigenen Ansätzen
  • vertreten und verteidigen die eigene Lösung im Lichte von Kritik
Methodological Competence
  • kennen die wesentlichen Bestandteile und den Ablauf einer Case Study Methode
  • können die Case Study Methode auf konkrete Fallbeispiele aus dem strategischen Bereich anwenden
  • können strategische Analysen durchführen
  • sind in der Lage, strategische Ergebnisse zu verdichten und zu einer systematischen Strategieformulierung umzusetzen
  • sind in der Lage, verschiedene strategische Optionen zu bewerten
Professional Competence
  • kennen die wichtigsten Instrumente sowie den Prozess des strategischen Managements und Risikomanagements
  • verstehen die wesentlichen Erfolgsfaktoren des strategischen Managements
  • wenden Instrumente des strategischen Managements korrekt an
  • können diese Instrumente mit Prozessen des strategischen Lernens verbinden und dabei zwischen verschiedenen Lernstufen unterscheiden.
  • Die Studierenden sind in der Lage, eine konsistente strategische Konzeption zu erstellen und strategische Ziele zu formulieren.
  • bewerten Strategien und beurteilen deren Eignung in unterschiedlichen Situationen
Methodological Competence
  • Know the central statistical techniques that are often used in business applications.
  • Understand the meaning of statistical notions.
  • Use the introduced concepts in a purposeful way, interpret the results in the context and formulate their conclusions correctly.
  • Use basic commands of the software package R to analyze data graphically and numerically.
  • Apply standard learning techniques in abstract contexts so that they get used to working with scientific publications on their own.
  • Analyze data to justify decisions in business applications.
  • Analyze business cases using methods of probability theory.
  • Can critically check the content of statistical results while planning economic actions.
  • Argue in a precise and rational way in their comments.
  • Strengthen their skills to argue rationally in a scientific environment.
  • Judge the relevance of statistical conclusions and their limitations correctly.
  • Judge arguments critically whether they are sound, reasonable and consistent.
  • Judge the uncertainties in statistical conclusions correctly.
Personal Competence
  • Internalize the use of standard learning and working techniques to learn on their own.
Social Competence
  • Cooperate while working out problems or while preparing themselves for the final exam.
  • Formulate the findings from the analyses of empirical data using the terminology made available to them, to indicate the degree of uncertainty in the conclusions correctly.
  • Are able to argue in a rational and controversial way in a scientific environment and include different points of view in their considerations.
Professional Competence
  • Know about the roles of quantiles, variances, standard deviations and correlations to measure risks.
  • Know the axioms of a discrete probability space.
  • Know the most important distributions and their properties.
  • Know the importance of the central limit theorem.
  • Can describe univariate and bivariate data according to the level of scale using numerical measures and graphical representations.
  • Can explain the content of the axioms of a discrete probability space while modelling a random experiment.
  • Use the law of large numbers to interpret a probability as a relative frequency in the long run.
  • Can explain why and when a certain distribution is used to model economic situations.
  • Can name the basic idea of testing hypotheses referring to the possible types of errors.
  • Name the basic ideas of standard testing procedures.
  • Calculate the critical values in the decision rules of binomial tests.
  • Can explain the meaning of confidence intervals and indicate the duality between confidence intervals and testing hypotheses.
  • Use the principle of ordinary least squares to estimate the parameters of a regression model.
  • Run simple linear regressions, set up the ANOVA-table and judge the residual plot.
  • Calculate probabilities using addition rules, decision trees and combinatorics.
  • Can explain the results of Bayes' theorem.
  • Use limits theorems to approximate distributions and probabilities.
  • Use calculations rules for expectations and variances correctly and can explain their meanings in the context of risk measuring.
  • Calculate the critical values of binomial tests and the resulting probability of a type 2 error.
  • Evaluate the test statistics of standard procedures, read the corresponding critical values from statistical tables and formulate the conclusion of the testing procedure correctly in the given context.
  • Calculate confidence intervals and interpret them correctly in a given context.
  • Interpret measures as quantiles, variances, standard deviations, correlations, skewness, curtosis correctly.
  • Use the vocabulary introduced to them to describe graphical representations correctly and include the advantages and disadvantages of such representations while interpreting them.
  • Judge the certainty or uncertainty of statistical conclusions and formulate their interpretations accordingly.
  • Judge the practical relevance of a linear regression in the given context.
  • Judge the uncertainty in the conclusions from statistical testing procedures correctly
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