Course - Data Science - BBAN4001
Data Science
About
About the course
Course content
The course provides a theoretical and practical introduction to a number of topics in data analysis and statistical learning, with special emphasis on applications in the field of economics.
These topics may include:
- Linear, non-linear and logistic regression
- Cross-validation
- Bootstrapping
- Decision trees and boosting
- Support vector machines
- Clustering
- Neural networks
- k-nearest neighbors (k-NN)
- Data visualization
- Generative AI
- Explainable AI
The course provides an introduction to the use of the programming language R or Python for data analysis. Use of other computer tools, such as SQL, can also be included.
Learning outcome
Knowledge
The candidate should:
- Have a good knowledge of the basic techniques of data science
- Be able to link applications of data science to issues related to the economic-administrative field
Skills
The candidate should:
- Be able to perform basic data analyses in the programming language R or Python
- Be able to understand and evaluate advanced data analyses as well as results from certain machine learning techniques
General Competence
The candidate should:
- Be able to use data science to express, analyze and communicate economic issues
- Have an understanding of data science and basic machine learning that can form the basis for further studies and lifelong learning
Learning methods and activities
Lectures and exercises.
Compulsory assignments
- Assignment
Further on evaluation
Written school exam.
The mandatory assignment must be approved before students can take the exam.
Information about compulsory exercises will be given at the start of the semester.
Other calculators allowed in the course are: Casio FC-100V and Texas Instruments - BAII Plus.
Note: students who attend the Master in Accounting and Auditing will have access to a postponed exam in August without any requirement of a valid due date or fail due to a potential need to achieve a C requirement in the course. These students must contact the department before the registration deadline of 9 July.
In case of a re-sit exam or the final exam when the course is no longer being taught, the exam may be changed to an oral exam
Specific conditions
Admission to a programme of study is required:
Accounting and Auditing (MRR)
Economics and Business Administration (MSIVØK5)
Economics and Business Administration (ØAMSC)
Financial Economics (MFINØK)
Management of Technology (ØAMLT)
Recommended previous knowledge
MET1002 or an equivalent introductory course in probability and statistics.
TDT4110 or an equivalent introduction to basic programming in Python.
Course materials
Textbook (subject to changes):
Shmueli, G., Bruce, P. C., Gedeck, P., & Patel, N. R. (2025). Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python, 2nd Edition. John Wiley & Sons.
Which chapters are on the syllabus, as well as possible additional reading materials, will be specified during the semester.
Credit reductions
| Course code | Reduction | From |
|---|---|---|
| BMRR4015 | 7.5 sp | Autumn 2020 |
| TMA4268 | 7.5 sp | Autumn 2020 |
Subject areas
- Statistics
- Economics and Administration