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Managing Data Science - (B-E-M)

Kurs ID
QUANT506
Art des Kurses
MSc Kurs
Wochenstunden
2,5
ECTS
5
Semester
FS 2024
Vortragssprache
Englisch
Vortragende/r
Dr. Tobias Keller
Bitte beachten Sie, dass AustauschstudentInnen im BSc-Programm der WHU eine höhere Anzahl an Credits erwerben als hier aufgeführt. Für weitere Informationen wenden Sie sich bitte direkt an das [International Relations Office].
Objectives and focus of the course

The world’s most valuable resource is no longer oil, but data” – The Economist, May 2017
Data has the potential to create immense business value, to disrupt existing, and to create new business models. With the recent advances in artificial intelligence (AI), the competitive pressure to harness the potential of the companies’ data has further increased. However, it is not enough to hire data scientists and give them some data. Data science, including the development of artificial intelligence systems, is a team sport that needs to be managed. Despite being enabled by technology, data science is a business-centric discipline – managers therefore must understand and think like data scientists and implement the necessary organizational changes in order to facilitate a data-driven business model.

This lecture aims at enabling students to build and lead data science teams, to understand and deal with typical risks throughout the lifecycle of data science projects and the development of AI systems. Throughout the whole lecture, examples from “real life” AI & data science projects will be used to illustrate the presented concepts and methods. Understanding the essentials of AI & data science methods including statistics and machine learning will enable the students to ask the right questions and what (not) to expect from AI & data science. Finally, the students will learn about the technical and organizational requirements for utilizing the full business potential of AI & data science.

Structure of the course

1. Business Potential and Lifecycle of Artificial Intelligence (AI) & Data Science

  • Artificial Intelligence & (Big) Data Science for Competitive Advantage
  • Defining Artificial Intelligence & Data Science

2. Organizational Challenges in Artificial Intelligence & Data Science – Part 1

  • Why AI & Data Science pose specific challenges
  • The Data Science Lifecycle
  • Building and Managing AI & Data Science Teams

3. AI & Data Science Methods - Essentials and Practical Applications

  • Statistical Analysis and Modeling
  • Machine Learning
  • Evaluating AI & Data Science Results – What Questions to Ask

4. Technical Foundations of Artificial Intelligence & Data Science

  • Software Toolbox and Technical Infrastructure for AI & (Big) Data Science
  • Deploying and Managing Analytical Models and AI systems

5. Organizational Challenges in Artificial Intelligence & Data Science – Part 1

  • Integrating AI & Data Science (Teams) in the Organization
  • Data Governance, Data Quality, and Privacy
Date Time
Friday, 12.01.2024 09:45 - 17:00
Wednesday, 31.01.2024 11:30 - 18:45
Thursday, 01.02.2024 11:30 - 18:45
Friday, 09.02.2024 08:00 - 13:00
Thursday, 22.02.2024 10:00 - 11:30
Many of the topics are covered by the following recommended book:Caffo, B., Peng, R.D., Leek, J. (2018) – Executive Data Science – A Guide to Training and Managing the Best Data Scientists, electronic publication, available online: https://leanpub.com/eds: Chapter 1 (pp. 1-29), Chapter 2 (pp. 30-75), and Chapter 3 (pp. 76-106), as well as the first part of Chapter 4 (pp. 77-129).In addition, the following resources are recommended for specific chapters (further resources are mentioned in-place):Business Potential and Lifecycle of Artificial Intelligence (AI) & Data ScienceMorabito, V. (2015): Big Data and Analytics – Strategic and Organizational Impacts, Springer International Publishing, DOI 10.1007/978-3-319-10665-6, pp. 3-6.AI & Data Science Methods - Essentials and practical ApplicationsField, A., Miles, J., Field, Z. (2012): Discovering Statistics using R, Sage Publications Ltd.: Chapter 1 (pp. 7-11, 19-29), Chapter 2 (pp. 33-46, 50-59), Sections 6.2 and 6.3 (pp. 206-212), Section 7.2 (pp. 246-253); You may skip the parts about using the R language.Chollet, F. (2017): Deep Learning with Python, Manning Publications. Chapter 1 (Sections 1.1 to 1.3, pp. 3-24), Chapter 4 (Sections 4.1 to 4.2, pp. 93-101); you may skip the parts about using the Python language.Organizational Challenges in Artificial Intelligence & Data Science – Part 2Morabito, V. (2015) – Big Data and Analytics – Strategic and Organizational Impacts, Springer International Publishing, DOI 10.1007/978-3-319-10665-6, Chapter 5: Big Data Governance (pp. 83-90).
Lectures, exercises, self-study
Grading is based on the following components:
100% Exam
A basic knowledge of statistics ist required
150
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