ØKA2013 Big Data Analytics
- Number of credits7,5
- Teaching semester2026 Spring
- Language of instructionEnglish
- CampusLillehammer
- Required prerequisite knowledge
Recommended prerequisites: course in statistics
- Definitions of big data analytics
- Application areas of big data analytics
- Business value of big data (value chain)
- Databases for big data analytics
- Data mining and data analytics
- Data visualization
- Big data architectures
- Case studies: Application of relevant software to solve real life, big data business problems
Learning Outcome
Upon passing the course, students have achieved the following learning outcomes:
The student
- can define what big data sets are based on the most commonly used definitions
- can refer to various application areas of big data analytics
- can describe how big data can be transformed into business value
- can present the architecture of big data
- can explain (in own words) how various techniques for analyzing big data sets work in practice
- can explain important issues related to privacy and ethical issues in the use of big data
The student
- can structure the process of performing big data analytics
- can apply basic techniques for gathering, storing, distributing, and processing big data sets
- can analyze relevant big data sets using appropriate analytical frameworks and software from various industries/areas including (but not limited to):
- Accounting
- Asset pricing / Trading / Banking
- Entertainment industry
- Sales
- Etc.
Lectures (live and video), workshops with case studies, problem solving, mandatory hand-ins.
| Form of assessment | Grading scale | Grouping | Duration of assessment | Support materials | Proportion | Comment |
|---|---|---|---|---|---|---|
Home exam | ECTS - A-F | Individual | 2 Day(s) |
| 100 |
Reading list
No reading list available for this course