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2026/2027  KAN-CEAPV2601U  Topics in Data Analytics for Innovation, Technology, and Markets

English Title
Topics in Data Analytics for Innovation, Technology, and Markets

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for Finance, Economics & Mathematics
Programme MSc in Economics and Finance
Course coordinator
  • Marek Giebel - Department of Economics (ECON)
Main academic disciplines
  • Innovation
  • Statistics and quantitative methods
  • Economics
Teaching methods
  • Face-to-face teaching
Last updated on 30-01-2026

Relevant links
Learning objectives
  • Understand the debate on the impact of innovations on organizations and society
  • Identify and apply appropriate theories to analyze the effects of innovation
  • Argue and critically assess how an empirical analysis relates to theories about the impact of innovations on organizations and society
  • Identify and apply appropriate econometric methods to analyze the effects of innovation
  • Interpret estimation results and critically assess the validity of the applied empirical methodology
  • Set empirical results in the context of the broader debate on the impact of innovations on organizations and society
Course prerequisites
Basic knowledge of microeconomics, descriptive statistics, and linear regression models.
Examination
Topics in Data Analytics for Innovation, Technology, and Markets:
Exam ECTS 7,5
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance, see also the rules about examination forms in the programme regulations.
Individual or group exam Oral group exam based on written group product
Number of people in the group 2-4
Size of written product Max. 10 pages
Definition of number of pages:
Groups of
2 students, 5 pages max.
3-4 students, 10 pages max.
Assignment type Project
Release of assignment Subject chosen by students themselves, see guidelines if any
Duration
Written product to be submitted on specified date and time.
10 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
The re-take exam is to be based on the same report as the ordinary exam:

*if a student is absent from the oral exam due to documented illness but has handed in the written group product she/he does not have to submit a new product for the re-take.

*if a whole group fails the oral exam they must hand in a revised product for the re-take.

*if one student in the group fails the oral exam the course coordinator chooses whether the student will have the oral exam based on the same product or if he/she has to hand in a revised product for the re-take.
Course content, structure and pedagogical approach

 

This is a hands-on, data-oriented course that uses econometric and statistical tools to analyze the determinants and impacts of innovation and new technologies on firms and society. Innovations and new technologies are fundamental to today's fast-changing global economy. As a result, managers and policymakers must decide which technologies to support, how to organize technological discovery, and how to evaluate the impact of new technologies. Over the past decades, large datasets, big data sources, and analytical tools have become available, playing a key role in evaluating the use and impact of new technologies. Modern analysis methods include simple linear regression models for exploring correlations, more sophisticated causal analysis techniques, and advanced methods such as machine learning. This course builds on ideas from economics, finance, and management. We combine theory, learned empirical methods and real-world data using statistical software to analyze the impact of innovation on firms and society. 

 

A central component of the course is the analysis of innovation-related datasets (e.g., firm-level, patent, or labor-market data) using statistical software. The focus is on the causal interpretation of regression results and the selection and assessment of empirical strategies. We will adopt an integrated approach, discussing main topics in innovation through theory, empirical studies, and our own data analysis. Core topics include innovation incentives and competition, innovation policy, intellectual property rights and strategy, and innovation financing and valuation. We will also examine how innovation, automation, and artificial intelligence impact economic growth, financial markets, labor markets, firms, and society. Additionally, this course will teach you how to interpret and communicate results that inform strategic decisions and policy design.
Research-based teaching
CBS’ programmes and teaching are research-based. The following types of research-based knowledge and research-like activities are included in this course:
Research-based knowledge
  • Classic and basic theory
  • New theory
  • Teacher’s own research
  • Methodology
  • Models
Research-like activities
  • Development of research questions
  • Data collection
  • Analysis
  • Discussion, critical reflection, modelling
  • Activities that contribute to new or existing research projects
  • Students conduct independent research-like activities under supervision
Description of the teaching methods
The course follows the structure of a three-hour per week class that includes lectures and practical computer exercises. Teaching includes lecture-style classes, in-class workshops, and practical computer exercises. Students are encouraged to present and actively participate in discussions. Blended learning instances will help the students deepen their understanding of the statistical software, empirical methods, and empirical applications.
Feedback during the teaching period
Throughout the course, students will take quizzes to deepen their understanding of the syllabus and to practice applying their knowledge. Some quizzes will provide automatic feedback with correct answers, while those with essay questions or empirical applications will be discussed in class afterward. Additionally, feedback will be provided during class activities, including exercises, group work, discussions, and presentations.
Student workload
Preparation 106 hours
Classes 30 hours
Exam 70 hours
Expected literature

Core reading includes selected chapters from:

 

  • Bryan, K.A. and Williams, H.L. (2021). Innovation: market failures and public policies. In Handbook of Industrial Organization (Vol. 5, No. 1, pp. 281-388). Elsevier.
  • Hall, B.H. and Rosenberg, N. (2010). Handbook of the Economics of Innovation. North-Holland.
  • Scotchmer, S. (2004). Innovation and incentives. Cambridge, Mass.: MIT Press.
  • Békés, G., & Kézdi, G. (2021). Data analysis for business, economics, and policy. Cambridge University Press.
  • Cunningham, S. (2021). Causal inference: The mixtape. Yale university press.

 

More literature will be announced in the syllabus. 

Last updated on 30-01-2026