Examination arrangement

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.

Specific conditions

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