Structure and program outline

The module is an introduction to the training program and serves three purposes:

1. It should motivate the study of data science, outline its main areas of application and give an overview of the techniques and methods used. In particular, it should be made clear that this program sees itself as a joint offering from different disciplines (computer science, mathematics, statistics, business administration and marketing, as well as communication science), their meeting and cross-fertilization is essential for successful data science projects.

2. The participants should present their own projects that they intend to work on during the course. This is to ensure that everyone realizes early on what they want to achieve in this program. At the same time, the participants should get to know each other.

3. The module serves as an introduction to the essential programming systems, R and Python used in the course, which are used regularly in the following modules and can also be used in the preparation of the master's thesis.

Overview of course content:

• Introduction / basics of data science, disciplinary approaches

• Python installation (Anaconda, iPython), notebook concept

• Fundamentals of the Python language and the interpretation of Python programs (numbers, strings, variables, basic operators and functions, working with files, data structures lists, dictionaries, tuples, control structures if-else, while, for, importing packages, object orientation, etc.)

• Numerous sample programs that are provided as Python notebooks

• Introduction to the statistical programming language R (structures in R, import and export of data, functions and loops, ..)

• Explorative data analysis with R

Professional competencies:

The students will be able to recognize what constitutes data science and why one speaks of a “cross-sectional competence” in which knowledge from computer science, mathematics, statistics, business administration, marketing, communication sciences (and possibly other areas) overlap and complete. You are familiar with the basics of R and Python and can build on them in the following modules.

Key qualifications:

The participants are able to deepen the acquired knowledge through independent learning. They learn and deepen problem-solving work in small groups as well as presentation techniques in the context of presenting their results of the exercises.

Module lead: Prof. Drs. Thorsten Quandt, Heike Trautmann, Gottfried Vossen, Thorsten Wiesel

The module is an introduction to handling large amounts of data, both on the basis of traditional (SQL) and newer (NoSQL) database systems and on the basis of file management systems (such as HDFS). The module conveys basic concepts, algorithms and procedures for the area of ​​data science. The participants are also familiarized with concrete implementations in the context of the Python programming language. The module is another basic module; the techniques learned are also required and used in other modules.

Overview of course content:

• Data management with SQL and NoSQL systems, differences and similarities, application for OLAP tasks, application criteria for processing large amounts of data

• Distributed file systems (HDFS) as an alternative, architecture options (Hadoop ecosystem, Spark, Flink)

• Basic algorithms of data mining, map reduce applications, determination of similarity, recommendation, community detection.

• Further treatment of the Python language, modules of the Python ecosystem, development of a data science workflow

• Tool selection for and programming implementation of exemplary applications such as general data exploration, development of a web crawler, text and mood analysis, matrix operations, recommender, image classification

Professional competencies:

Students will be able to decide which system category is suitable for dealing with a specific data science problem and why. You are familiar with the basics of Python and can indicate in which way you can programmatically address a data management system in a specific case. They know basic algorithms and approaches of data mining and central data science applications and have the necessary problem-solving skills.

Key qualifications:

The participants are able to deepen the acquired knowledge through independent learning. They learn and deepen problem-solving work in small groups as well as presentation techniques in the context of presenting their results of the exercises.

Module lead: Prof. Dr. Gottfried Vossen

The module teaches central analytical methods for the area of ​​data science with the help of the statistical software R. It functions as a basic module, the techniques learned are also required and used in other modules.

Overview of course content:

• Exploratory data analysis and data preprocessing

• supervised learning (classification, regression)

• unsupervised learning (cluster analysis, dimensional reduction)

• model validation

• Programming in R

Professional competencies:

After completing the course, the students are able to process, analyze and interpret data in a structured manner. In the corporate context, data-based decision-making processes can be efficiently controlled and carried out.

Key qualifications:

Teamwork as well as communication and cooperation skills, interdisciplinary work

Module lead: Prof. Dr. Heike Trautmann

In addition to the challenges in management by dealing with growing amounts of data, the topics of IT security, ethics and also the legal framework are dealt with. The participants are made aware of the special features of the design of relationships and processes in data management within and between organizations. Companies can generate added value by using and making open data available. At the same time, there is a need to securely manage closed and sensitive data. In this area of ​​tension, the participants learn how data standards, semantics and architecture can be used for effective management. You will be enabled to take into account not only the technical, but also the social aspects. The participants also learn about the essential legal framework and regulations and how to take them into account in data management. For every company that increasingly comes into contact with confidential data, the ethical aspects must also be taken into account. In this context, the students are familiarized with recognizing and recording ethical problems in the area of ​​IT management and developing solutions.

Although the importance of data science in companies is now largely recognized, the actual use of data science analysis for decision-making purposes is often viewed critically. In addition, data science departments are often qualitatively and quantitatively understaffed, which results in a lack of trust among decision-makers in the results of their specialists' analyzes. For this reason, this module focuses on the implementation and introduction of data science in a company. The participants get to know essential concepts of entrepreneurial decision-making in the area of ​​tension between data science knowledge and business necessities and are thereby enabled to understand the effects of comprehensive data evaluation, analysis and interpretation as part of business decision-making. The following important topics are dealt with in this context:

• Influence of data science results on management decisions

• Data science as part of decision-making processes

• Behavior and role model of executives and the special role of data scientists

• Quality of innovative teams and their effects on data science results

• Methods for effective negotiations and targeted preparation of data science issues with managers

• Methods for presenting and discussing data science results with decision makers

The case analysis is central in this module. The participants learn how to differentiate between main issues and secondary issues in complex "real live" cases in Domain Data Science and thus to get to the heart of the problem and to define it. Based on this problem definition, possible creative solutions are proposed to the participants and the solutions are weighed based on certain criteria (SMART). The participants will first work out an approach plan to implement the solution. The report is made in writing. In between, the participants will have the opportunity to present the problem analysis, proposed solutions and approach plan and to improve / supplement the reporting based on peer feedback / review.

Overview of course content:

• Case analysis in Data Science

• Present problem description

• Weigh the proposed solutions critically according to defined criteria

• Peer feedback from interim results

• Written report

• Present for feedback

According to this module, the participant is able to:

• critically analyze a complex case in the context of data science,

• to arrive at a problem result on the basis of a critical analysis,

• bring creative solutions to solve the problem,

• Weigh the solutions according to specified criteria (SMART),

• develop an approach plan to implement the solutions in practice,

• Present interim results and reflect and process peer feedback / review in a constructive way in the written reportage,

• Record analysis, problem description, proposed solutions and approach plan in a consistent manner.

The module introduces social science research into network-based communication. The disciplinary foundations of empirical social research, especially communication science and media psychology, are taught. Based on this, various "computational methods" are practiced, which are suitable for the analysis of social media and other forms of online communication. In particular, network analytical methods and methods of (semi) automated content analysis are used, which are deepened with the help of concrete examples. Current program packages and analysis software are used, which are presented in their basic uses and are practically used in the exercises.

Overview of course content:

• Introduction to social science research, basic logic of empirical research, basic methods in the social sciences

• Social science research on online communication, research areas, classic studies and current results

• Basics of media psychology, research areas, classic studies and current results

• Computational Communication Research, basic approaches and research logic

• Network analysis, social science and mathematical basics, use cases, exercises for network analysis with the help of appropriate analysis software

• (Semi-) automated content analysis, basics, use cases, procedures, methods, exercises with the help of appropriate analysis software

Professional competencies:

Students will be able to plan, implement and evaluate social science research projects on social media and online communication. You can also evaluate corresponding projects based on media psychological and communication science findings. You are familiar with the basics of network analytical procedures as well as (semi) automated content analysis procedures. You have acquired competencies for evaluating such analysis methods and can use them with the help of current program packages or analysis software in specific cases.

Key qualifications:

Participants can expand their knowledge and analytical skills through independent learning. In addition, they are able to assess appropriate approaches in terms of their goals, procedures and knowledge potential. You have competencies for individual and team-oriented work in small groups. They can also use presentation techniques to present their results.

Module lead: Prof. Dr. Thorsten Quandt

The module builds on the previous basic modules. The aim is to discuss and apply the techniques learned in various application contexts. The application contexts can be in the areas of marketing, customer relationship management, supply chain management, logistics, company foundation or budget allocation. It is critically discussed to what extent the applications create value for both the customer and the company. In addition, future developments regarding data-driven decisions towards possible automated decisions by machines are discussed critically.

Overview of course content:

• Discussion of data-driven decision making to create value for both customers and businesses

• Introduction to basic procedures with regard to data-driven decision-making in various fields of application such as marketing, customer relationship management, supply chain management, logistics, business start-ups or budget allocation

• Evaluation of adequate analysis methods for the respective application field

• Use current program packages or analysis software in specific cases.

• Discussion of future developments regarding data-driven decisions towards possible automated decisions by machines

Professional competencies:

Students will be able to plan, implement and evaluate projects for data-driven decision-making in various fields of application such as marketing, customer relationship management, supply chain management, logistics, start-ups or budget allocation. You will acquire skills for evaluating adequate analysis methods and can use them in specific cases with the help of current program packages or analysis software.

Key qualifications:

Participants are enabled to deepen the knowledge and analytical skills they have acquired by applying the course content in practical or job-oriented settings, thereby training their transfer skills. You will learn and deepen problem-solving, team-oriented work in small groups as well as communication skills and presentation techniques in the context of presenting your results of the applications. Due to the potential interdisciplinary composition of the group of participants, the participants also deepen their ability to take an interdisciplinary approach.

Module lead: Prof. Dr. Thorsten Wiesel

Based on the knowledge and skills acquired in the modules, the students should show that they are able to work independently on a decision and analysis problem according to scientific criteria. The processing time is 7 months.