What is Master of Business Analytics

Extra-occupational course in Business Analytics (M.Sc.)

Big data, data science, machine learning, Industry 4.0, the Internet of Things - buzzwords that are encountered more and more frequently in companies, but for which the experts are often lacking. The analysis of constantly growing amounts of data using the most modern technologies, the formulation of the correct questions resulting from this for a company and the trend-setting interpretation of such large amounts of data are now more than ever in demand for the skills of highly sought-after specialists. The part-time master's degree in Business Analytics teaches you these skills based on its content and didactically well thought-out curricular concept.

The course is aimed at young professionals, young managers as well as project managers and consultants who want to optimally expand and deepen their skills in dealing with the challenges of "Industry 4.0" and "Big Data". For this purpose, you will be taught interdisciplinary business, mathematical and information technology skills in the course. In addition to topics such as data mining, machine learning and predictive methods through to strategic and business process management for developing new business models, you will receive the tools and sound technical and business expertise that is applied and deepened in practical exercises in the modules of the course.

Course director
Prof. Dr. Mischa Seiter
Institute for Business Analytics

Course coordinator
Ralf Boenke

Modules of the course

Study objectives

The Business Analytics course is aimed at middle and top management executives who are to be enabled to solve operational problems with the help of data-based evidence. This is done in the course of study business, mathematical and IT skills conveyed.

Competencies in the field of economics

Within the competence area Economics business knowledge is imparted. The students can identify business problems and assess their relevance. In the next step, these can be converted into analytical questions by the students. In this context, the students can use the evidence to develop and implement measures and justify their use. As part of the identification of business management problems, the students can use concrete company examples, e.g. B. Evaluate key financial figures or make and justify investment decisions. The students are able to identify areas of application of business analytics in everyday operations. Against this background, you can select descriptive, predictive and prescriptive algorithms to solve specific business problems and develop or adapt them further according to business needs. The students can present evidence in a meaningful way with regard to business problems. In doing so, they assess independently and aware of requirements and points of criticism which forms of visualization are appropriate for the target group and the situation. For the implementation of evidence-based measures, the students can plan and map operational processes, manage them and design and implement the further development of operational processes.

Mathematics competencies

The aim of the competence area is to teach descriptive, predictive and prescriptive algorithms for data analysis and interpretation mathematics. The participants are able to apply methods for the analysis of extensive amounts of structured and unstructured (social media) data and to assess them with regard to the data quality. Furthermore, they can apply and adapt standard procedures for solving specific analytics questions based on knowledge of the strengths and limitations of various descriptive, predictive and prescriptive algorithms. In this context, it is of great importance that the students can also comment critically on whether algorithms can be applied to the specific problem, regardless of their mathematical implementation. You can evaluate the special challenges in big data applications in the context of numerics. In addition, they can analyze and assess selected algorithms and their application for high-dimensional problems. The participants can identify business analytics questions and problems for which stochastic methods and models can be used in order to construct and carry out corresponding modeling in the next step. Another important competence is the ability to adapt or transform the modeling methods learned to different analytics issues. The handling and use of software tools is also trained.

Competencies in the field of computer science

Within the competence area Computer science Participants in the course receive competencies for the use of information technology and software for analytics purposes. You can describe the essential characteristics, components and functions of process-oriented information systems and classify them within an overall architecture. In addition, the participants are able to evaluate selected process scenarios and implement them with the help of a process management system. The students can carry out basic methods, procedures and concepts of data and process mining for data analysis and interpretation using software tools with reference to given analytics issues and explain, visualize and present their analysis results appropriately. You can evaluate the cloud model from different perspectives and are able to comment on various issues in this context. You can explain the basic concepts of IT security and cryptography. In addition, they can assess various elementary security mechanisms such as email encryption or authentication with digital certificates, taking into account the respective limits of these systems, and determine appropriate measures for the operational context.

Learning outcomes of the course

Graduates of the program are able to use descriptive, predictive and prescriptive algorithms to bring about improvements in all operational functions - from research and development to sales. Typical problems are the improvement of processes, the quantitative foundation of decisions as well as the further development of the strategy and the business model.

A successfully completed master’s degree in Business Analytics should enable
  1. Independently identify and process issues in the field of business analytics at a high university level.
  2. To be able to plan and assess the business analytics process from the development of the business problem to data analysis using algorithms and evidence-based decision-making.
  3. To apply descriptive, predictive and prescriptive algorithms as part of the business analytics process, if necessary to adapt them to the analytics question.
  4. To be able to assess requirements and aspects in the processing of large amounts of structured and unstructured data (big data) as well as to use suitable instruments for analysis and interpretation by means of data mining.
  5. on planning, development and research tasks in scientific and public institutions.
  6. to work as a research assistant at a university and
  7. for access to a doctorate.

 

Against the background of a constantly growing amount of data, the students can also assess data protection aspects so that they can assess and implement compliance with the legal requirements of data protection in the area of ​​business analytics.

The course also takes into account the fact that, on the one hand, the demand for specialists in data analysis and interpretation is increasing, and on the other hand, there are hardly any courses on offer in this subject area, especially in German-speaking countries. Graduates of the course are able to use their skills in a wide variety of companies and institutions. The competencies of data analysis and decision-making based on large amounts of data can be used in insurance companies, banks, processing and manufacturing companies, consulting companies or even in public institutions.

Learning management system

The learning management system (LMS) used is a system that enables module participants to use all device classes from PCs to tablets and smartphones in all common operating systems.
An installation of the widespread LMS Moodle was adapted to the special needs of the user group. The adaptation ensures that an adapted technical layout creates a visual appearance that can be used for all device classes. This reduces both the maintenance effort and the development time when implementing new features.
The highlighted display of processing statuses in relation to interim questions and exercise sheets should give students a high level of overview of the current processing status of a module at all times.
The integration of the open source video conference system "BigBluButton" also opens up consultation hours and tutorials. The software is available to students at any time for the independent organization of meetings.

Methods

The modules are subject to a broad media mix. This includes, for example, the following concepts:


The following additional measures are offered:


Depending on the module, there are different assessments and types of exams:

Study part-time

The course and the certificate courses are carried out in blended learning, a combination of face-to-face events and a virtual offer. The didactics and the structure of the instruction design depend on the specific requirements of the course content and the needs of the participants. The learning documents required for successful participation are made available in the "virtual desk in the cloud", a specially developed learning environment.

Blended learning concept
E-learning concepts are combined with virtual classrooms and a few, multi-day presence phases to deepen the learning content and for individual exchange between the students and with the lecturers.

Reliable didactic concept
For each module, you will be familiar with all the framework conditions in advance: the amount of time to be learned, the forms of learning offered and didactic learning paths, the practical exercises planned, the dates of the attendance times at the university, the work phase to be carried out and the module completion with the form and date of the examination.

Intensive care and support concept
In order to promote efficient learning progress, we provide intensive, interactive supervision and support tools by the lecturers and lecturers as well as the course coordination. Each student also receives a personal mentor in each module, whom they can turn to with questions. In this way, we guarantee quick feedback on content-related issues and the review of submitted seminar tasks.

Practical use
Although the study design contains many online parts, we place particular emphasis on the practical phases and the attendance times.
The master’s program is intended to give students great flexibility and many options.

Consistently modularized
All modules of the master’s course can be attended individually and largely independently of one another. Modules that build on each other are specially marked. Models for the structure of their studies are recommended to students. For every successfully completed module you will receive a certificate from Ulm University. This will also be credited at a later point in time as a study module in the master’s program in Innovation and Science Management.

Free choice of modules
A balanced support model and the free choice of the number of modules to be taken per semester enables our students to harmonize their studies with their individual career planning and their family situation. You can study time and largely independent of location!

Duration of study
You determine the duration of your studies yourself! The curricula plan to study in 4 semesters. The reduction in the number of modules per semester results in a correspondingly longer period.

Studies for specialists
Successful completion of the degree opens up opportunities for you to take on management positions in your research and development-oriented company. It also gives you the opportunity to do a doctorate at a university.

Guaranteed scientific quality
The scientific quality of the study program is ensured by the responsible collaboration of a group of professors from the Faculty of Mathematics and Economics at Ulm University. The course director selects the lecturers and lecturers for complementary, supplementary content. Regular feedback and continuous evaluation by the lecturers and students involved ensure that the quality of the modules is optimized. After completing your Master of Science degree, you have the opportunity to do a doctorate at a university.

Conditions of admission
1. University degree (at least Bachelor)
2. For the course: at least one year of professional experience