Computer Science

Experience in Software Enigneering and Programming

B2 Level in English

• Identify real-world problems and recognise the problems of creating complex solutions using a wide variety of IoT platforms. They are in a position to analyse the environment of the problem and are able to discuss these in advance in cooperation with companies.

• Acquire knowledge of design thinking, agile project management and the independent implementation of projects is acquired in teamwork. They are able to apply interdisciplinary knowledge, integrate the problem solver into the project in an agile manner and to present the results of their work.

The cooperating companies offer the students real problems from the most important IoT domains, such as Smart Agriculture, Smart Building, Smart Energy, Smart Production, eHealth, etc.

The problem is described in detail using defined application cases. In addition, the aspects of IoT Cloud and IoT Security are also examined.
The students are supervised by the lecturer and the coach of the innovation lab.

Experience in Programming and Software Engineeering

B2 Level in English

After successful completion of this course, students:

• Know the problems of creating complex systems
• Can apply the basics of scientific work and know how to independently carry out projects appropriate to the degree programme
• Have learned to work in a team and have acquired knowledge in estimating the scope of projects as well as in the management and supervision of projects.
• Are able to apply interdisciplinary knowledge and present work results.

Students are motivated to work scientifically and will be able to acquire subject-specific knowledge from the scientific literature and to prepare this knowledge for specific target groups.

The course covers four areas:

1. Basics of scientific work

Students understand the necessity of a scientific approach to problems and are able to understand the basic concepts of scientific work. (e.g. research questions, argumentation logic, writing style, citation)

2. Research methods

Students are able to apply essential research methods commonly used in business informatics and to assess the basic applicability of these methods for a given problem. In addition, they understand the basics of design-oriented research (Design Science). Furthermore, they are able to conduct a systematic literature study on their own.

3. Handling of scientific texts

Students can describe the structure of scientific texts, apply reading strategies and assess their basic scientific quality. Furthermore, they can compile, evaluate and compare the core statements of different scientific texts.

4. Presentation and discussion

In the area of presentation and discussion, students understand the essential elements of effective presentations and are able to apply them to a lecture. In addition, they are able to apply argumentation strategies for professional discussions and methods for effective discussion moderation.

Implicitly, this course promotes the English language level of the students to the level B2.2/C1.1 of the CEFR. Through intensive literature work with English-language scientific texts and their presentation/discussion, they have the ability to understand the main content of complex texts on concrete and abstract topics as well as to participate in specialist discussions in the field of business information systems.

Qualification goals:
Y Combinator is one of the most successful startup/accelerator programs from Silicon Valley and is
particularly focused on HiTech.

Y Combinator Exists are for example Airbnb, Stripe, Dropbox, Reddit, etc. The aim of the Y Combinator program is in particular to create independent start-ups within a short period of time (3 months), regardless of the phase of the idea (idea generation or already existing concept or demonstrator) to achieve significant progress in order to present not only their plan on Y Combinator Demo Day, but also to achieve a significantly improved, realistic result with which a possible further implementation can be further implementation can actually be realized.

In this module, the students should get insight into how to move from an idea to actual implementation for AI-based business models and innovations.
This module provides students with both the tools as well as the know-how on how to efficiently and specifically use the current AI methods and possibilities for entrepreneurial success, evaluate whether the idea is viable under the given premises and what, in addition to the technical requirements, is necessary for the successful realization of the product/project.

Course content:

• Current AI business models
• Success criteria for selection and evaluation, benchmarking
• Essential decision criteria for successful entrepreneurship/intrapreneurship
• Presentation technique

Qualification goals:
Students will gain an overview of AI applications and their practical applicability within real company examples. The students develop the ability to identify AI application cases and to methodically evaluate their applicability.

The focus here is particularly on the area of process optimization. In this context, the following are also rounded off the topics of data selection and management, bias mitigation as well as ethical and legal principles for the application.

Course content:
- Identification of potential for process optimization through AI applications
- Methodical evaluation of application cases and feasibility
- Proof-of-concept planning
- Legal basis for data collection, preparation and processing for the envisaged AI applications
- Bias mitigation approaches, ethical principles

Qualification goals:

Students are able to implement machine learning in the cloud and are familiar with various machine learning methods. They are able to implement these methods in a cloud environment and practically solve ML problems. They are able to select suitable cloud infrastructure and services for the problems at hand and are familiar with the practical use of standard tools.

Course content:

- Basic concepts of cloud computing, especially using AWS

- The cloud computing machine learning pipeline:

- ML problem formulation and business case definition

- ML data collection and labelling, data cleaning, ETL

- Understanding and evaluating ML data, Pandas library, statistics for understanding data

- ML feature engineering

- ML model selection and training with Amazon Sagemaker

- (Automated) hyperparameter tuning

- Model deployment in the cloud

- ML model evaluation

- Special topics, vision, NLP and forecasting

- Tools: Python libraries Pandas, Skikit