Managing Data Science

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