Course - Data Science - BBAN4001
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 the turn in the graded semester assignment.
Compulsory assignments
- Assignment
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 | To |
|---|---|---|---|
| BMRR4015 | 7.5 | AUTUMN 2020 | |
| TMA4268 | 7.5 | AUTUMN 2020 |
No
Version: 1
Credits:
7.5 SP
Study level: Second degree level
Term no.: 1
Teaching semester: AUTUMN 2023
Language of instruction: Norwegian
Location: Trondheim
- Statistics
- Economics and Administration
Examination
Examination arrangement: School exam
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Autumn ORD School exam 100/100 D 2023-12-06 15:00 INSPERA
-
Room Building Number of candidates SL515 Sluppenvegen 14 1 SL520 Sluppenvegen 14 1 SL210 Sluppenvegen 14 48 SL311 lyseblå sone Sluppenvegen 14 8 - Spring ORD School exam 100/100 D 2024-06-03 09:00 INSPERA
-
Room Building Number of candidates SL410 orange sone Sluppenvegen 14 18
- * 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.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"