Course - Essentials of Business Analytics - BBAN3001
Essentials of Business Analytics
About
About the course
Course content
Business Analytics combines data science with business, economics, and management. It focuses on collecting, processing, and analyzing data to develop models that support decision-making. This course covers key methods such as regression, classification, and clustering.
Learning outcome
Knowledge
The student will acquire knowledge about:
- Drivers and trends shaping the field of business analytics
- The business analytics life cycle
- How business analytics supports key business functions
- Core concepts and principles of business analytics
- Methods for data collection and an overview of common data sources
- Foundations of data visualization
- Basic statistical and regression methods
- Fundamental concepts of optimization
- Basic methods for classification and clustering
- Introductory concepts of artificial neural networks
Skills
The student will be able to:
- Retrieve data from a variety of open and proprietary sources and import it into Excel spreadsheets or Pandas data frames
- Visualize data using tables, plots, and diagrams in Python
- Apply basic programming skills in Python
- Conduct statistical analyses using Python
- Implement optimization and simulation models in Python
- Discuss and critically reflect on the value and limitations of analytical tools for business decision-making
General competence
The student gains an understanding of how business analytics can be applied to address business challenges and support decision-making. The student becomes familiar with the processes involved in collecting, analyzing, and reporting business data, and develops the competence to use appropriate software tools to carry out business analytics tasks.
Learning methods and activities
Lectures (physical or digital), videos, written exercises, data exercises and essay writing.
Compulsory assignments
- Mandatory Essay
Further on evaluation
In the exam, the majority of exercises need to be implemented by means of Python. A PC with the necessary software will be provided and set up by NTNU. The exam follows the examination regulations of NTNU.
There is mandatory coursework requirement in the course, this must be approved in order to take the exam. Further information will be given at the start of the semester.
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.
Required previous knowledge
Elementary course in mathematics and statistics.
Course materials
The curriculum will be announced when the course commences.
Credit reductions
| Course code | Reduction | From |
|---|---|---|
| TDT4172 | 3.5 sp | Autumn 2025 |
Subject areas
- Economics and Administration