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
- Linear and quadratic discriminant analysis
- Cross-validation
- Bootstrapping
- Decision trees and boosting
- Support vector machines
- Clustering
- Neural networks
- Data visualization
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. The mandatory assignment must be approved before students can take the exam.
Compulsory assignments
- Assignment
Further on evaluation
Written school exam.
Information about compulsory exercises will be given at the start of the semester.
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 the case of a re-sit exam and the last exam after the course has been discontinued, the form of assessment 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. (2019). Data mining for business analytics: concepts, techniques and applications in Python. 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
Contact information
Course coordinator
Lecturers
Department with academic responsibility
Examination
Examination
Ordinary examination - Autumn 2025
School exam
The specified room can be changed and the final location will be ready no later than 3 days before the exam. You can find your room location on Studentweb.