Name |
Language |
Teaching Method |
ECTS
SWS |
Controlling (CONTR)
German /
kMod
|
German |
kMod |
5.00
- |
Business Simulation (UNPLA)
German /
UE
|
German |
UE |
2.00
1.00 |
Course description
In this sub-module, students deepen, expand and cross-link the business management skills taught in previous semesters within the course of a business game.
Methodology
Flipped Classroom
Learning outcomes
After passing this course successfully students are able to ...
-
formulate value-oriented corporate goals
-
differentiate between strategic and operative business decisions
-
optimally coordinate the various marketing policy instruments
-
evaluate the advantages of investments using suitable calculation methods
-
develop an optimal production and sales program
-
weigh up between in-house production and external procurement
-
conduct a break-even analysis
-
calculate balance sheet ratios for the interpretation of financial statements
-
identify rationalization potentials and take appropriate measures to realize them
-
deal with large amounts of information in a structured way
Course contents
-
Strategic Management
-
Accounting
-
Balance Sheet Analysis
-
Procurement Management
-
Production Management
-
Marketing
-
Investment Planning
-
Cost Accounting
Prerequisites
Fundamentals of Business Administration
Literature
-
Wala, Grobelschegg: Kernelemente der Unternehmensführung, Linde-Verlag
Assessment methods
-
Immanent performances (100%)
Anmerkungen
Details see Moodle course
|
IT-Based Controlling (ITCON)
English /
ILV
|
English |
ILV |
3.00
2.00 |
Course description
In this course, typical internal accounting tasks (cost accounting, financing, controlling) are carried out with the help of software.
Methodology
Workshops, Exercises
Learning outcomes
After passing this course successfully students are able to ...
-
analyse annual financial statement using key business figures
-
calculate cash flows
-
apply core concepts of cost accounting (e.g. cost type acc., cost center acc., cost unit acc.)
-
prepare investment decisions
-
create a budget
Course contents
-
state-of-art spreadsheet calculation
-
practical implementation of internal accounting tasks
-
business decision calculations
Prerequisites
Management
Accounting
Investment & Financing
Assessment methods
-
2 interim tests (25 points)
-
1 final exam (50 points)
|
Data Analysis and Applied Statistics (DATEN)
German /
iMod
|
German |
iMod |
5.00
- |
Introduction to Statistical Learning (STAT2)
German /
ILV
|
German |
ILV |
2.00
1.00 |
Course description
"Einführung in Statistical Learning" is targeted on establishing a basis for the utilization of Statistical Learning methodology, showcasing four standard methods related to that framework. Statistical Learning brings together statistical and machine learning concepts and - as continuation of the "Statistische Datenanalyse" course - the course will exhibit additional possibilities to analyze data which are not based on the classic principles of inferential statistics, i.e., testing, estimation, and modelling. By means of classification methods, it will also be made clear that the main focus is not on explanation, but on prediction.
Learning outcomes
After passing this course successfully students are able to ...
-
visualize and cluster multivariate data.
-
apply classification methods and assess the quality of prediction.
-
summarize the findings in a structured report.
Course contents
-
Naive Bayes and k-nearest-neighbors classification, spineplot, spinogram, doubledecker plot
-
resampling methods
-
performance evaluation based on the confusion matrix
-
hierarchical cluster analysis, distance measures, dendrograms, banner plot
-
nonhierarchical cluster analysis, scree, elbow and silhouette plot
Prerequisites
Mathematics 1 & 2
Applied probability and statistics
Statistical data analysis
Literature
-
Datenanalyse und statistische Modellierung (D. Meyer, M. Wurzer)
-
R. Hatzinger, K. Hornik, H. Nagel, M. Maier: R – Einführung durch angewandte Statistik (2. Auflage), Springer, 2014
Assessment methods
-
Final exam: Write a comprehensive statistical report
-
Students get additional points for exercises in class an Moodle minitests
|
Statistical Data Analysis (STAT3)
German /
ILV
|
German |
ILV |
3.00
2.00 |
Course description
"Statistische Datenanalyse" is targeted on providing a hands-on introduction to the applied analysis of empirical data. Students will gain an understanding of the need to be able to select the correct statistical method for a given problem and will learn how to execute the corresponding analysis step by step, i. e., reading and manipulating data as well as applying descriptive and inferential statistical methods. A special focus is on the interpretation and communication of analysis results by means of statistical reports. The "Literate programming"-approach guarantees the reproducibility of results, imperative in modern research. In order to show that data used for statistical analyses are the result of a structured process, the course also covers the main features of empirical social research.
Learning outcomes
After passing this course successfully students are able to ...
-
explain main tasks of statistics and relate them to real-world tasks.
-
understand mode of operation and requirements of empirical social research.
-
preprocess, describe and visualize data in R.
-
carry out hypothesis tests for categorical and metric variables.
-
test associations between two categorical or two metric variables.
-
establish and check regression model for metric variables.
-
perform basic time series analyses.
Course contents
-
fundamentals of empirical social research (design, sampling)
-
data management in R
-
one categorical variable: absolute and relative frequencies, bar charts, chi-squared test
-
wo categorical variables: contingency tables, grouped bar charts, spine plots, chi-squared tests for independence and homogeneity
-
one metric variable: histogram, indicators for mean and variance, boxplots, t-test
-
two metric variables: scatter plot, correlation analysis, regression analysis
-
time series analysis: time series plots, regression models, naive forecasting
-
Reproducible Scientific Research: Literate Programming using R Markdown
-
choosing the appropriate statistical method for a given problem
|
Distributed Systems (DISYS)
German /
iMod
|
German |
iMod |
5.00
- |
Distributed Systems (DISYS)
English /
ILV
|
English |
ILV |
5.00
3.00 |
Course description
This course introduces the development of component-based (in particular service-oriented) software systems.
Methodology
Lectures, homework / project work and self-study with practical examples and supervised project work
Learning outcomes
After passing this course successfully students are able to ...
-
implement component-based systems using a selected programming language
-
implement service-oriented systems using a selected programming language
-
analyzing existing monolithic systems and converting them into flexible, distributed systems
-
exchange data asynchronously between (sub)systems using message queues, file transfer, RPC or shared databases
-
encapsulate data layer functionalities using O/R Mappers and make them available using interfaces
-
consider and apply design principles in the context of object orientation in the programming process
Course contents
-
Component Based System Engineering
-
Service-oriented System Components
-
Various principles of system design
-
SOA related to system components
-
UML modeling (component/sequence diagrams)
Prerequisites
Basics in software development with a selected programming language. Basic knowledge of software architecture.
Literature
Assessment methods
-
Multimodal:
-
Theoretical assessment (Moodle MC test)
-
Homework (Coding Hand-Ins)
-
Project work
|
Elective Modules (VERT)
German /
kMod
|
German |
kMod |
10.00
- |
Elective Module: App & Web Development (VAWD)
German /
kMod
|
German |
kMod |
10.00
- |
Android App Development (AAD)
English /
ILV
|
English |
ILV |
5.00
3.00 |
Course description
The first part of the specialization "App & Web Development" tackles the devlopment of mobile Android applications.
Learning outcomes
After passing this course successfully students are able to ...
-
develop advanced applications for the Android platform using appropriate tools
-
discuss typical design patterns and best practices for the development of Android applications
-
implement own projects using these patterns
Course contents
-
Development of Android smartphone applications
-
Design patterns for Android Applications
-
Android Application Design (user interface, threading, web services, data persistence)
-
PItfalls and best practices (memory management, memory leaks, debugging, crash-logs, performance)
Prerequisites
programming, web engineering.
|
iOS App Development (IOSD)
German /
ILV
|
German |
ILV |
5.00
3.00 |
Course description
The second part of 'App & Web Development' covers the development of iOS based applications.
Methodology
Slides, links and documents for Self-studying. Topics and questions will be discussed in class and implementations will be shown.
Small assignments after each class.
Learning outcomes
After passing this course successfully students are able to ...
-
develop advanced iOS applications using the current development tools and IDEs
-
name and explain advanced design patterns and best practices for developing iOS applications
-
structure and build your own iOS application implementations according to these design patterns and best practices
Course contents
-
Development of iOS based applications
-
Design patterns for iOS apps
-
Basic skills in programming language Swift
-
Design of iOS apps (Coding UI, Threading, Web Service access, Persistence)
-
Pitfalls and proven methods.
Prerequisites
Knowledge in object oriented programming.
Assessment methods
-
Small assigments after each class.
-
Theory exam at the end of the semester.
|
Elective Module: Big Data & Data Science (VBIGD)
German /
kMod
|
German |
kMod |
10.00
- |
Big Data Engineering (BDENG)
German /
ILV
|
German |
ILV |
5.00
3.00 |
Course description
This in-depth course focuses on data handling in heterogeneous environments with a wide variety of data types (from structured to unstructured, from continuous and discrete data to text data) with a special focus on Big Data environments.
This course is part 2 of a series on Big Data & Data Science topics ("Big Data & Data Science" consisting of Big Data Infrastructure, Big Data Engineering, Data Science, Machine Learning). After completing the entire series, students can independently build and assess a continuous Big Data workflow.
Methodology
Content is taught in a flipped classroom methodology, i.e. students independently acquire new topics, and in the attendance phase this content is deepened and practiced with practical examples.
Learning outcomes
After passing this course successfully students are able to ...
-
understand the differences between data types and data formats in order to process data in a targeted manner.
-
retrieve data from web sources (websites and provided interfaces)
-
read and process larger data sets using Apache Spark
-
further process structured and unstructured data using Spark SQL
-
elaborate advantages of different data architectures (Lambda and Kappa)
-
control the workflow of data and to apply processing steps in a targeted manner
Course contents
-
data in big data environments
-
WebScraping, REST-APIs
-
Apache Spark
-
Kafka
-
Nifi
Prerequisites
The course assumes data management, Python knowledge as well as the contents of the course "Big Data Infrastructure" and is itself a prerequisite for the advanced course Data Science.
Literature
-
Kleppmann, M., 2017. Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Revised Edition. ed. O’Reilly UK Ltd., Boston.
-
Luu, H., 2021. Beginning Apache Spark 3: With DataFrame, Spark SQL, Structured Streaming, and Spark Machine Learning Library, 2nd ed. ed. Apress, S.l.
-
Estrada, R., 2018. Apache Kafka Quick Start Guide: Leverage Apache Kafka 2.0 to simplify real-time data processing for distributed applications. Packt Publishing, Birmingham Mumbai.
Assessment methods
-
short Moodle-Tests
-
exercises
-
final project
|
Big Data Infrastructure (BDINF)
German /
ILV
|
German |
ILV |
5.00
3.00 |
Course description
This course teaches fundamentals and basic skills for technologies with a special focus on big data. Prior to this, necessary skills in dealing with operating systems (Linux), DevOps engineering (Git, Docker), programming languages (Python) and development environments (Jupyter Notebook) are taught.
This course is part of a series on Big Data & Data Science topics ("Big Data & Data Science" consisting of Big Data Infrastructure, Big Data Engineering, Data Science, Machine Learning). After completing the entire series, students can independently build and assess a continuous Big Data workflow.
Methodology
Content is taught in a flipped classroom methodology, i.e. students independently acquire new topics, and in the attendance phase this content is deepened and practiced with practical examples.
Learning outcomes
After passing this course successfully students are able to ...
-
use Linux basic commands for practical tasks, import or manage files in a Linux system
-
pull content from a Git repository and deploy services using containers
-
use the main commands and data structures of the Python programming language, as well as develop executable code in the form of Python programs and Jupyter notebooks
-
read data from CSV files and manage Jupyter notebooks in a version-controlled manner
-
store and retrieve structured, semi-structured, and unstructured data using NoSQL database technologies
-
provide an overview of the dimensions and challenges of big data and plan the distribution of data and computational operation on a Hadoop cluster
-
understand the use of a Hadoop cluster and understand it through simple examples
-
store data distributed on a Hadoop cluster and process it using Hadoop's own methods
-
apply the technologies learned in the course combined for an exemplary scenario to perform computations for a dataset on a Hadoop cluster
Course contents
-
Linux, Git and Docker
-
Python and Jupyter Notebooks
-
Data storage and NoSQL
-
Big Data Introduction
-
Hadoop Basics
Prerequisites
The course assumes data management and programming skills and is itself a prerequisite for the in-depth course Big Data Engineering.
Literature
-
Leornardo, C., 2020. Docker: Docker for the Absolute Beginner. Independently published.
-
Loeliger, J., McCullough, M., 2012. Version Control with Git: Powerful tools and techniques for collaborative software development, 2nd ed. O’Reilly and Associates, Beijing.
-
Meier, A., Kaufmann, M., 2019. SQL & NoSQL Databases: Models, Languages, Consistency Options and Architectures for Big Data Management, 1st ed. 2019 Edition. ed. Springer Vieweg, New York, NY.
-
Kunigk, J., George, L., Wilkinson, P., Buss, I., 2019. Architecting Modern Data Platforms: A Guide to Enterprise Hadoop at Scale, 1st Edition. ed. O’Reilly UK Ltd., Beijing China ; Sebastopol, CA.
Assessment methods
-
short Moodle-Tests
-
exercises
-
final project
|
Elective Module: Business Applications (VBAP)
German /
kMod
|
German |
kMod |
10.00
- |
Customizing of ERP Systems (BUSAPP2)
German /
ILV
|
German |
ILV |
5.00
3.00 |
Course description
The course teaches skills to customize the ERP system Navision to your own needs.
Simple adaptations include, for example, the appearance and the possibilities provided to the respective role to work with the program. If an extended functionality is required, it is not enough to make an adjustment, adaption on the databasis of Navision and in the application logic is necessary.
These topics will be taught in the course by providing examples. To implement new functionality in Navision, the internal programming language C/AL is used.
In addition to learning the programming language and the procedure for supplementary programming, a new client is created and existing data is entered.
The handling of data will also be covered in the topic of interfaces. (How external systems can exchange data with Navision).
The final part is the report creation (visual representation of interesting data) and the integration of the report in the role center of the respective user role.
Students gradually learn the structure of the ERP system, thereby understanding how it works and better understand the internal processes, such as which data sources are changed, where this data is stored and how it can be accessed. In the course of creating the visual representation of data in the form of an input and output mask, the overall understanding of the structure of the program becomes apparent. Students are thus able to display their own information, but also to configure existing views in such a way that the workflow can be simplified. The transfer of knowledge is directly visible and tangible, as it is very easy to make your own adjustments (for example to text, positioning, display, additional tabs, ...).
The understanding of the handling of the database and the presentation forms the basis for the supplementary programming, where the understanding of the internal processes of Navision are necessary to develop own functionality.
This specialized knowledge, which goes beyond the application, is always needed in practice
Methodology
Lectures and guided exercises, live demonstration, project work
Learning outcomes
After passing this course successfully students are able to ...
-
describe the data model (table structure) of the ERP system MS Dynamics Navision.
-
to make different adjustments to the appearance of the ERP system MS Dynamics Navision
-
create additional functionality in MS Dynamics NAV with the C/AL language using the Dynamics development environment and integrate it into the system.
-
connect different systems to MS Dynamics using selected connectors to allow access from outside the ERP system.
-
perform data migration (import existing data, for example, from legacy systems)
-
create reports (visual representation of data of interest) and integrate them into the ERP system
-
create a new client (perform minimal configuration)
-
create new users in the ERP system, assign roles and rights and create individual views for roles
Course contents
-
Basics architecture and structure of MS Dynamics Navision
-
Dealing with the development environment: debugging, code creation
-
C/AL programming language
-
Implement extensions in MS Dynamics Navision
-
Configuration of the ERP system (client setup, data import and migration)
-
Dealing with the development environment: debugging, code creation
-
Connect ERP system via web interface (OData & SOAP)
Prerequisites
ERP Basics
Basic programming knowledge
Assessment methods
-
Homework
-
Projektwork
-
Project presentation
|
Selection of ERP Systems (AERPS)
German /
ILV
|
German |
ILV |
5.00
3.00 |
Course description
Project-based and process-controlled selection of ERP systems
Methodology
blended learning
case study
Learning outcomes
After passing this course successfully students are able to ...
-
plan, coordinate and control the selection of an ERP system using project management methods, taking into account operational conditions.
-
use selected methods of business process management (process mapping, business process master list, business process modelling) to analyze the as-is organization, to identify potentials and to describe the target picture.
-
to describe the standard selection process (procedure, methods) and to adapt it to the operational conditions.
-
to systematically develop a set of criteria for the ERP system selection and to use it in the various stages of the selection process (shortlist/longlist) according to the situation.
-
to conduct the selection decision in the process based on methods (benefit analysis, AHP) and to evaluate the strengths/weaknesses of the methods used.
-
to plan, organize and to perform a negotiation workshop based on a predefined script.
-
to support the selection decision at different stages based on cost and investment analysis (TCO, ROI).
-
to identify and evaluate the requirements for configuration and customizing based on a FIT/GAP analysis.
Course contents
-
ERP systems (basics, benefits, market overview)
-
organizational analysis (organizational structure, process organisation)
-
system selection (Process, methods)
-
set / catalogue of criteria (reference)
-
optimizing the set / catalogue of criteria (cost/benefit analysis, analytic hierarchy process)
-
cost and investment analysis
-
Fit/Gap Analysis
-
project management in ERP projects
-
process models in ERP projects
Prerequisites
Course: Introduction to Business Informatics
Course: Business process analysis and modeling
Literature
-
Gronau, N., 2010. Enterprise Resource Planning. Architektur, Funktionen und Management von ERP-Systemen. 2. Auflage. München: Oldenbourg Wissenschaftsverlag GmbH
-
Kurbel, K., 2003. Produktionsplanung und –steuerung – Methodische Grundlagen von PPS-Systemen und Erweiterungen. 5. Auflage. München.
-
Hesseler M., Görtz M., 2008. Basiswissen ERP-Systeme. W3L-Verlag, 1. Auflage, Witten
-
Jansen, M. 2014. Nutzwertanalyse – wenn Entscheiden schwerfällt. VDI. ERFA-Kreis Nürnberg/München
-
Becker, J., Vering, O., Winkelmann, A. (2007). Softwareauswahl und Einführung in Industrie und Handel – Vorgehen und Erfahrung mit ERP- und Warenwirtschaftssystemen. Springer Verlag Berlin
Assessment methods
-
Individual: mid-term exam
-
group: case study
|
Elective Module: Business Digitalization (VBD)
German /
kMod
|
German |
kMod |
10.00
- |
Business Integration (BSINT)
German /
ILV
|
German |
ILV |
5.00
3.00 |
Course description
This second part of the specialization "Business Digitalization" extends the "Process Automation" course to the IT support inter-company processes (in particular customers and suppliers)
Methodology
In the course, a case study on the digitization of a company is processed against the background of embedding in a corporate network (e.g. via the supply chain). The processing starts with the creation of a digitalization strategy, digitalization roadmap up to the ("hands-on") automation of business processes by means of BPA and RPA tools - with the aim of integration across company boundaries.
Learning outcomes
After passing this course successfully students are able to ...
-
assess the potentials of digitalization and automization in the transformation of digital companies
-
identify inter-company processes suitable for automization
-
identify process and data interfaces between companies
-
select technologies for data exchange between companies
Course contents
-
Models for organizing and optimizing intercompany processes
-
Business Process Automation (BPA) and Robotic Process Automation (RPA) in the context of company networks
Prerequisites
Process Automation
Literature
-
Venkatraman, N. (1994). IT-enabled business transformation: From automation to business scope redefinition. Sloan management review, 35, 73–73.
-
Krumay, B., Rueckel, D., & Koch, S. (2019). Model for Strategic Positioning in Transformative Situations. Proceedings of ICIS 2019 – International Conference on Information Systems, 1–17.
-
Beetz, R., & Riedl, Y. (2019). Robotic Process Automation: Developing a Multi-Criteria Evaluation Model for the Selection of Automatable Business Processes. 25th Americas Conference on Information Systems, AMCIS 2019, Cancún, Mexico, August 15-17, 2019. https://aisel.aisnet.org/amcis2019/enterprise_systems/enterprise_systems/4
-
Lehnert, M., Röglinger, M., & Seyfried, J. (2018). Prioritization of Interconnected Processes. Business & Information Systems Engineering, 60(2), 95–114. https://doi.org/10.1007/s12599-017-0490-4
-
Wanner, J., Hofmann, A., Fischer, M., Imgrund, F., Janiesch, C., & Geyer-Klingeberg, J. (2019). Process Selection in RPA Projects—Towards a Quantifiable Method of Decision Making. In H. Krcmar, J. Fedorowicz, W. F. Boh, J. M. Leimeister, & S. Wattal (Hrsg.), Proceedings of the 40th International Conference on Information Systems, ICIS 2019, Munich, Germany, December 15-18, 2019. Association for Information Systems. https://aisel.aisnet.org/icis2019/business_models/business_models/6
Assessment methods
-
Case study (70%), knowledge check (30%)
|
Process Automation (PRAUT)
German /
ILV
|
German |
ILV |
5.00
3.00 |
Course description
This first course of the specialization "Business Digitalization" deals with one of the most important topics of digital transformation: process automation, here limited to internal processes.
Methodology
In the course, a case study on the digitization of a company is worked on. The work begins with the creation of a digitization strategy and digitization roadmap and continues with the ("hands-on") automation of business processes using BPA and RPA tools.
Learning outcomes
After passing this course successfully students are able to ...
-
Recognize digitization and automation as a tool in the transformation of businesses
-
Apply process models for the digital transformation of companies
-
Identify internal company processes that can be optimized by means of automation
-
Identify potential for new in-house digital workflows
-
Automate internal company processes for the purpose of optimization (using BPA and RPA)
Course contents
-
Digitization, digitalization and digital transformation
-
Models of business transformation
-
Digitalization (optimization, automation) of business processes
-
Business Process Automation (BPA) and Robotic Process Automation (RPA)
-
Automation within the company by means of workflow management systems (for the implementation of BPA) and automation systems for RPA
Prerequisites
Business process engineering
Literature
-
Krumay, B., Rueckel, D., & Koch, S. (2019). Model for Strategic Positioning in Transformative Situations. Proceedings of ICIS 2019 – International Conference on Information Systems, 1–17.
-
Venkatraman, N. (1994). IT-enabled business transformation: From automation to business scope redefinition. Sloan management review, 35, 73–73.
-
Han, P. F. (2013). Criteria, Use Cases and Effects of Information Technology Process Automation (ITPA). Advances in Robotics & Automation, 03(03). https://doi.org/10.4172/2168-9695.1000124
-
Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital Business Strategy: Toward a Next Generation of Insights. MIS Quarterly, 37(2), 471–482. https://doi.org/10.25300/MISQ/2013/37:2.3
-
Koch, C., & Fedtke, S. (2020). Robotic Process Automation: Ein Leitfaden für Führungskräfte zur erfolgreichen Einführung und Betrieb von Software-Robots im Unternehmen. Springer Vieweg.
Assessment methods
-
Case study (70%), knowledge check (30%)
|
Elective Module: User Experience & Software Quality Assurance (VUXSQA)
German /
kMod
|
German |
kMod |
10.00
- |
UX and Interaction Design (UXSQA)
German /
ILV
|
German |
ILV |
5.00
3.00 |
Course description
The second part of the immersion "UX Software Quality Assurance" covers the user centered design of user interfaces.
Methodology
Integrated course
Learning outcomes
After passing this course successfully students are able to ...
-
explain the design lifecycle
-
explain and apply design methods
-
develop user interfaces iteratively using approved methods
-
develop low and high fidelity prototypes using appropriate tools
Course contents
-
prototyping
-
design methods & patterns
-
4 dimensions of a prototype
-
prototyping tools
Prerequisites
BWI-4-BB - Usabilty Evaluation
Assessment methods
-
Assignments
-
Project assigments
|
Usabilty Evaluation (UXEV)
German /
ILV
|
German |
ILV |
5.00
3.00 |
Course description
The first part of the immersion "UX & Software Quality Assurance" covers the evaluation of software with regard to usability.
Methodology
Integrated course
Learning outcomes
After passing this course successfully students are able to ...
-
give an overview of usability testing methods
-
independently conduct selected evaluation methods (heuristic evaluations, tress testing, usability test)
-
design evaluations according to scientific principles
-
analyse test results and document them using standardized reporting templates
-
explain the importance of accessibility and ethics in designing
Course contents
-
psychological and technical basics
-
planning, execution and analysis of heuristic evaluations, tree tests and usability tests
-
preparation of test results for clients
Prerequisites
keine
Assessment methods
-
Assignments
-
Project assignments
|
Web Development (WEBEN)
German /
iMod
|
German |
iMod |
5.00
- |
Web Development Project (WEBEN)
German /
PRJ
|
German |
PRJ |
2.00
1.00 |
Course description
In this course, the focus is put on frontend & backend development for web applications. Previously acquired knowledge and skills from the courses Web Technologies and Web Scripting will be applied and deepened in a specific web development project. The lecturers act as clients in order to simulate daily professional life for web developers. Regular sprint reviews are part of the course and represent the preparation for recurring meetings with the clients, which are also part of the professional life of web developers.
Methodology
Independent development of a web application. Teachers act as clients. During the self-study phases, sprints are carried out, which are reviewed during the classes.
Learning outcomes
After passing this course successfully students are able to ...
-
independently develop a web application using front-end and back-end technologies
Course contents
-
Deepening the contents from Web Technologies & Web Scripting
Prerequisites
Web Technologies & Web Scripting
Assessment methods
-
Project planning (15%)
-
Project execution (20%)
-
Project results (50%)
-
Project presentation (15%)
|
Web Scripting (WEBSC)
German /
ILV
|
German |
ILV |
3.00
2.00 |
Course description
The course follows directly the contents of the "Webtechnologies" course and builds upon the already learnt basics of web application development. Script languages such as JavaScript and TypeScript are worked out and advanced state-of-the-art front-end frameworks such as jQuery are presented and used. The course primes for the follow-up courses "Web Frameworks" respectively "Web Development Project".
Methodology
lectures and guided exercises, programming assignments, mid-term tests and code discussions
Learning outcomes
After passing this course successfully students are able to ...
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integrate dynamic contents (user interactions, AJAX) into a web site via JavaScript.
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integrate and make use of parts of other developers and frameworks (e.g. Bootstrap, jQuery).
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apply the basics of web application development via TypeScript.
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link JS/TypeScript-based web frontends to backend components.
Course contents
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JavaScript, DynamicHTML, DOM (browser-side programming)
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dynamic loading of web contents via AJAX
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JavaScript-libraries and frameworks demonstrated via jQuery
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basics of TypeScript
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connection of web frontent to backend
Prerequisites
basics of web application development, client-server-infrastructures, HTML, CSS, working with HTML/CSS frameworks (see previous module "Webtechnologies")
Assessment methods
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Exercises
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Mid-term tests (moodle quizzes)
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practical mid-term test
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Project
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