course-details-portlet

BBAN4001 - Data Science

About

Examination arrangement

Examination arrangement: School exam
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
School exam 100/100 4 hours D

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.

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 To
BMRR4015 7.5 AUTUMN 2020
TMA4268 7.5 AUTUMN 2020
More on the course

No

Facts

Version: 1
Credits:  7.5 SP
Study level: Second degree level

Coursework

Term no.: 1
Teaching semester:  AUTUMN 2024

Language of instruction: Norwegian

Location: Trondheim

Subject area(s)
  • Statistics
  • Economics and Administration
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
NTNU Business School

Timetable

List view
Calendar view

Examination

Examination arrangement: School exam

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD School exam 100/100 D 2024-11-25 15:00 INSPERA
Room Building Number of candidates
SL110 turkis sone Sluppenvegen 14 40
SL520 Sluppenvegen 14 0
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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