Wissenschaftliche Methodik der Wirtschaftsinformatik

25.10.2021 - 14.02.2022

Weekly on Mondays from 14:00 to 16:00

Format: Seminar in presence

Course offered: Winter semester & Summer semester 

Description: 

The course covers the critical elements of the research process: writing an introduction, specifying the aim of the study, setting research questions and hypotheses, and further developing methods and procedures for data collection and analysis.

The structure of the class includes, in the beginning, some lectures accompanied by practical exercises. Each session will cover a different step of a research process (e.g., formulating a research question, understanding the role of theory, formulating hypotheses, academic writing) or a method (e.g., systematic literature review, experimental design). Subsequently, the participants in a group of two will carry out their own research project, present the preliminary and final results in the interim or final presentation. A short written report should be submitted to summarize the investigations conducted.

Lecturer:

Dr. Olga Abramova

PULS

Fundamentals of Data Science

13.04.2021 - 20.07.2021

Weekly on Tuesdays from 10:00 to 14:00

Format: Online Lecture and Exercise

Description: 

Data is increasingly seen as a driving force behind many industries, ranging from data-driven start-ups to traditional manufacturing companies. Recent years have been marked by the hype around big data technologies and the implications that go along with it. In response to these developments, data science has become one of the most demanded specializations. Against this background, the class will familiarize students with data science fundamentals.

Purpose of the class: This course is an introduction to data science using the statistical programming language R. Preliminary R knowledge is not required. We start with the very basic concepts of R programming and work our way through more sophisticated tasks of data representation, manipulation, and analysis. We illustrate every step with easy-to-follow examples.  After taking the course, you should be able to do the following:

• Program in R for data science, which includes (a) getting help and (b) applying the code contributed by the active community of R developers
• Get the data in and out of R
• Understand the data via conducting an exploratory analysis
• Create beautiful graphs and visualizations with the ggplot package
• Use the power of R to build and assess statistical and machine learning models
• Write reports and blog-posts in R Markdown

Audience: Bachelor students who are interested in data science and data analysis. At a broader level, the course serves as good preparation for writing a bachelor thesis or doing an internship in the "data science" field.

Format: Each week, we will cover a new topic and offer materials for practicing new skills and self-studying (HW assignments). Towards the end of the semester, group project work will allow course participants to apply their R-programming and data science skills and share results with fellow students. Each project group is assigned a specific dataset and works on the corresponding task, e.g., predicting customer churn, earthquakes, defaults on a loan, or mortgage.

The language of project presentations: German or English. Lectures and exercises will be held in English.

Lecturers: 

Dr. Abramova, Gladkaya

PULS

Wissenschaftliche Methodik der Wirtschaftsinformatik

13.04.2021 - 20.07.2021

Weekly on Tuesdays from 14:00 to 16:00

Format: Online Seminar

Description: 

The course covers the critical elements of the research process: writing an introduction, specifying the aim of the study, setting research questions and hypotheses, and further developing methods and procedures for data collection and analysis.

The target audience is Bachelor students who are interested in carrying out a research project. On a broader level, the course serves as good preparation for writing a bachelor thesis. For this reason, this course is recommended from the 4th FS.

The structure of the class includes, in the beginning, some lectures accompanied by practical exercises. Each session will cover a different step of a research process (e.g., formulating a research question, understanding the role of theory, formulating hypotheses, academic writing) or a method (e.g., systematic literature review, experimental design). Subsequently, the participants in a group of two will carry out their own research project, present the preliminary and final results in the interim or final presentation. A short written report should be submitted to summarize the investigations conducted.

Topics related to current fields of research in business informatics will be offered, especially in the following areas:

• Social media and user behavior
• Internet and its impact on society
• The effects of digitalization on well-being and behavior
• Digital work and the Internet of Things
• Human-computer interaction
• Information security and information privacy

Lecturer:

Dr. Abramova

PULS

Wissenschaftliche Methodik der Wirtschaftsinformatik

02.11.2020 - 16.02.2021

Weekly on Mondays from 10:00 to 12:00

Format: Online Seminar

Description: 

The course covers the critical elements of the research process: writing an introduction, specifying the aim of the study, setting research questions and hypotheses, and further developing methods and procedures for data collection and analysis.

The target audience is Bachelor students who are interested in carrying out a research project. On a broader level, the course serves as good preparation for writing a bachelor thesis. For this reason, this course is recommended from the 4th FS.

The structure of the class includes, in the beginning, some lectures accompanied by practical exercises. Each session will cover a different step of a research process (e.g., formulating a research question, understanding the role of theory, formulating hypotheses, academic writing) or a method (e.g., systematic literature review, experimental design). Subsequently, the participants in a group of two will carry out their own research project, present the preliminary and final results in the interim or final presentation. A short written report should be submitted to summarize the investigations conducted.

Topics related to current fields of research in business informatics will be offered, especially in the following areas:

• Social media and user behavior
• Internet and its impact on society
• The effects of digitalization on well-being and behavior
• Digital work and the Internet of Things
• Human-computer interaction
• Information security and information privacy

Lecturer:

Dr. Abramova

PULS

Fundamentals of Data Science

28.04.2020 - 21.07.2020 

Weekly on Tuesdays from 10:00 to 14:00

Format: Online Lecture and Exercise

Description: 

Data is increasingly seen as a driving force behind many industries, ranging from data-driven start-ups to traditional manufacturing companies. Recent years have been marked by the hype around big data technologies and the implications that go along with it. In response to these developments, data science has become one of the most demanded specializations. Against this background, the class will familiarize students with data science fundamentals.

Purpose of the class: This course is an introduction to data science using the statistical programming language R. Preliminary R knowledge is not required. We start with the very basic concepts of R programming and work our way through more sophisticated tasks of data representation, manipulation, and analysis. We illustrate every step with easy-to-follow examples.  After taking the course, you should be able to do the following:

• Program in R for data science, which includes (a) getting help and (b) applying the code contributed by the active community of R developers
• Get the data in and out of R
• Understand the data via conducting an exploratory analysis
• Create beautiful graphs and visualizations with the ggplot package
• Use the power of R to build and assess statistical and machine learning models
• Write reports and blog-posts in R Markdown

Audience: Bachelor students who are interested in data science and data analysis. At a broader level, the course serves as good preparation for writing a bachelor thesis or doing an internship in the "data science" field.

Format: Each week, we will cover a new topic and offer materials for practicing new skills and self-studying (HW assignments). Towards the end of the semester, group project work will allow course participants to apply their R-programming and data science skills and share results with fellow students. Each project group is assigned a specific dataset and works on the corresponding task, e.g., predicting customer churn, earthquakes, defaults on a loan, or mortgage.

The language of project presentations: German or English. Lectures and exercises will be held in English.

Lecturers: 

Dr. Abramova, Gladkaya

PULS

Wissenschaftliche Methodik der WI | Research Methods in Business Informatics

Seminar | 2 SWS | B.Sc. Wirtschaftsinformatik, Studiumplus

Course Content: 

Der Kurs behandelt die Schlüsselelemente des Forschungsprozesses: Schreiben einer Einführung, Angaben zum Ziel der Studie, Festlegen von Forschungsfragen und -hypothesen sowie Weiterentwicklung von Methoden und Verfahren zur Datenerhebung und -analyse.

Publikum: Bachelor-Studenten, die an der Durchführung eines Forschungsprojektes interessiert sind. Auf einer breiteren Ebene dient der Kurs als gute Vorbereitung für das Schreiben einer Bachelorarbeit. Aus diesem Grund wird dieser Kurs ab dem 3. FS empfohlen.

Format: Zu Beginn werden die Grundlagen der Forschung und des wissenschaftlichen Schreibens vorgestellt. Die Vorlesung wird von praktischen Übungen begleitet, die dazu dienen, den Teilnehmern das Formulieren von Forschungsfragen sowie die Prinzipien des wissenschaftlichen Schreibens näher zu bringen. Anschließend erhalten die Teilnehmer in einer Gruppe von zwei Personen die Möglichkeit, ihr eigenes Forschungsprojekt durchzuführen, sowie die vorläufigen und endgültigen Ergebnisse in der Zwischen- bzw. Abschlusspräsentation vorzustellen. Am Ende soll ein kurzer schriftlicher Bericht vorgelegt werden, der die durchgeführten Untersuchungen zusammenfasst.

Es werden Themen zu aktuellen Forschungsgebieten der Wirtschaftsinformatik angeboten, insbesondere in den folgenden Bereichen:

• Social media and user behavior
• Internet and its impact on society
• The effects of digitalization on well-being and behavior 
• Digital work and the Internet of Things
• Human Computer Interaction
• Information Security and Information Privacy

Sprache der Präsentation und des Abschlussberichts: Deutsch oder Englisch

Language:

English

Schedule:

Room: 3.06.S12

Mon 21.10 Methoden der Wirtschaftsinformatik (Vorlesung/Übung)

Mon 28.10 Methoden der Wirtschaftsinformatik (Vorlesung/Übung)

Mon 04.11 Methoden der Wirtschaftsinformatik (Vorlesung/Übung)

Mon 11.11 Methoden der Wirtschaftsinformatik (Vorlesung/Übung)

Mon 18.11 Methoden der Wirtschaftsinformatik (Vorlesung/Übung)

Mon 25.11 Methoden der Wirtschaftsinformatik (Vorlesung/Übung)

Mon 02.12 Methoden der Wirtschaftsinformatik (Vorlesung/Übung)

Mon 09.12 Methoden der Wirtschaftsinformatik (Vorlesung/Übung)

Mon 16.12 Mid-Term Präsentation

Mon 06.01 Akademische Koordination

Mon 13.01 Akademische Koordination

Mon 20.01 Abschlusspräsentation

Mon 27.01 Abschlusspräsentation

Mon 03.02 – Akademische Koordination

Mon 10.02 – Abgabe des Forschungsberichts

Lecturer:

Prof. Dr. Hanna Krasnova | Olga Abrmova

Assesment: 

Tests, Hausaufgaben, Mid-Term Präsentation, Abschlusspräsentation (auf Deutsch oder auf Englisch), Forschungsbericht 

Social Media Strategy

Lecture + Seminar | 2 + 2 SWS | B.Sc. Wirtschaftsinformatik, Studiumplus

Course Content:

As Social Media platforms are gaining popularity among users, businesses are looking for opportunities to leverage their potential. However, the mechanics of Social Media use presents businesses with a number of challenges that need to ensure smooth integration of Social Media channels into the overall business strategy. Against this background, in the first haft of the course (lecture) the students with be familiarized with the fundamentals of social media strategy, including such topics as social media planning and organization, integration of social media into the sales funnel, social media content strategy, ROI of social media, basics of social media advertising, and best practices of social media use. In the second half of the course (seminar), the students will have to craft and implement a social media strategy for a particular product, company or idea, and finally present their concept. Interdisciplinary teamwork is particularly encouraged in this course. Presentations could be made in either English or German.

Language:

Englisch

Schedule:

Room: 3.06.S21

Course: 22.10.2019 to 03.12.2019 / Tuesdays / 10:00-14:00 / weekly

Seminar: 10.12.2019 to 04.02.2020 / Tuesdays / 10:00-14:00 / weekly

Lecturer:

Prof. Hanna Krasnova 

Assesment: 

Seminar presentation, Short report

Written Exam: 

First round: (90 minutes) on 04.02.2020 at 10:00 in room 3.06.S21

Second round: (90 minutes) on 31.03.2020 at 10:00 in room 3.06.S26

Bachelor and Master Thesis (SMDS)

Forschungskolloquium für Bachelor- und Masterarbeiten

Course Content:

Im Forschungskolloquium des Lehrstuhls wird von Doktoranden, Masteranden und ausgewählten Bacheloranden der Bearbeitungsstand ihrer Arbeiten vorgestellt und diskutiert. An einzelnen Terminen finden Gastvorträge statt.    

Lecturer:

Prof. Dr. Hanna Krasnova

Fundamentals of Data Science

Lecture + Seminar | 2 + 2 SWS | B.Sc. Wirtschaftsinformatik, Studiumplus

Course Content: 

Data is increasingly seen as a driving force behind many industries, ranging from data-driven start-ups to traditional manufacturing companies. Recent years have been marked by the hype around big data technologies and the implications that go along with it. In response to these developments, data science has become one of the most demanded specializations. Against this background, this class will introduce students to the fundamentals of data science, using R for data analysis.

Purpose of the class:

This course is an introduction to data science using the statistical programming language R. Preliminary R knowledge is not required. We start by introducing the interface and work our way from the very basic concepts of the R language through more sophisticated data manipulation and analysis. We illustrate every step with easy-to-follow examples. R doesn’t function as your average scripting language, and it has plenty of unique features that may seem surprising at first. After participating in the class you should be able to do the following:
-Perform data analysis by using a variety of powerful tools
-Use the power of R to do statistical analysis and other data-processing tasks
-Know how to find, download, and use code that has been contributed to R by its very active community of developers
-Know where to find extra help and resources to take your R coding skills to the next level
-Create beautiful graphs and visualizations of your data

Audience:

Bachelor students who are interested in data science and data analysis. At a broader level, the course serves as good preparation for writing a bachelor thesis or doing an internship in the "data science" field.

Format:

Each week contains a mix of taught material, self-study material, practical exercises and homework (assignments). Then, participants in a group of 2 people will be given an opportunity to apply the skills they got to their own project and share the results with other participants during the presentation session.

The language of project presentations:

German or English. Lectures and Exercises will be held in English.

Shedule:

16.04 10:15 - 13:45 – Introduction
23.04 10:15 - 13:45 – Lecture | Exercise
30.04 10:15 - 13:45 – Lecture | Exercise
07.05 10:15 - 13:45 – Lecture | Exercise
14.05 10:15 - 13:45 – Lecture | Exercise
21.05 10:15 - 13:45 – Lecture | Exercise
28.05 10:15 - 13:45 – Lecture | Exercise
04.06 10:15 - 13:45 – Lecture | Exercise
11.06 10:15 - 13:45 – Seminar | Project Work
18.06 10:15 - 13:45 – Seminar | Project Work
25.06 10:15 - 13:45 – Seminar | Project Work
02.07 10:15 - 13:45 – Seminar | Project Work
09.07 10:15 - 13:45 – Seminar | Project Work
16.07 10:15 - 13:45 – Exam

Lecturer: 

Olga Abramova | Margarita Gladkaya

Assesment: 

Seminar presentation, short report, and written exam

Wissenschaftliche Methodik der WI

Seminar | 2 SWS | B.Sc. and M.Sc. Wirtschaftsinformatik, BWL

Course Content: 

Der Kurs behandelt die Schlüsselelemente des Forschungsprozesses: Schreiben einer Einführung, Angaben zum Ziel der Studie, Festlegen von Forschungsfragen und -hypothesen sowie Weiterentwicklung von Methoden und Verfahren zur Datenerhebung und -analyse.

Audience: 

Bachelor-Studenten, die an der Durchführung eines  Forschungsprojektes interessiert sind. Auf einer breiteren Ebene dient der Kurs als gute Vorbereitung für das Schreiben einer Bachelorarbeit.

Format: 

Zu Beginn werden die Grundlagen der Forschung und des wissenschaftlichen Schreibens vorgestellt. Die Vorlesung wird von praktischen Übungen begleitet, die dazu dienen, den Teilnehmern das Formulieren von Forschungsfragen sowie die Prinzipien des wissenschaftlichen Schreibens näher zu bringen. Anschließend erhalten die Teilnehmer in einer Gruppe von zwei Personen die Möglichkeit, ihr eigenes Forschungsprojekt durchzuführen, sowie die vorläufigen und endgültigen Ergebnisse in der Zwischen- bzw. Abschlusspräsentation vorzustellen. Am Ende soll ein kurzer schriftlicher Bericht vorgelegt werden, der die durchgeführten Untersuchungen zusammenfasst.

Topics related to current fields of research in business informatics will be offered, especially in the following areas:

• Social media and user behavior
• Internet and its impact on society
• The effects of digitalization on well-being and behavior 
• Digital work and the Internet of Things
• Human Computer Interaction
• Information Security and Information Privacy

 
Language of the Presentation and the Final Report: Deutsch oder Englisch

Language of the Lecture: English

Dates:

Tuesday 14:15 - 15:45

16.04 14:15 - 15:45 – Methoden der Wirtschaftsinformatik: Übersicht
23.04 14:15 - 15:45 – Methoden der Wirtschaftsinformatik: Übersicht
30.04 14:15 - 15:45 – Methoden der Wirtschaftsinformatik: Übersicht
07.05 14:15 - 15:45 – Methoden der Wirtschaftsinformatik: Übersicht
14.05 14:15 - 15:45 – Methoden der Wirtschaftsinformatik: Übersicht
21.05 14:15 - 15:45 – Methoden der Wirtschaftsinformatik: Übersicht
28.05 14:15 - 15:45 – Methoden der Wirtschaftsinformatik: Übersicht
04.06 14:15 - 15:45 – Methoden der Wirtschaftsinformatik: Übersicht
11.06 14:15 - 15:45 – Mid-Term Präsentation
18.06 14:15 - 15:45 – Akademische Koordination
25.06 14:15 - 15:45 – Akademische Koordination
02.07 14:15 - 15:45 – Abschlusspräsentation
09.07 14:15 - 15:45 – Abschlusspräsentation
16.07 14:15 - 15:45 – Abgabe des Forschungsberichts

Lecturer:

Olga Abrmova  |  Prof. Dr. Hanna Krasnova

Assesment: 

Early-Bird Präsentation, Mid-Term Präsentation, Abschlusspräsentation (auf Deutsch oder auf Englisch); Forschungsbericht