TDT4305 - Big Data Architecture


Examination arrangement

Examination arrangement: Portfolio assessment
Grade: Letters

Evaluation form Weighting Duration Examination aids Grade deviation
work 25/100
Written examination 75/100 4 hours D

Course content

The course gives an overview of main aspects of Big Data. Central topics are frameworks for Big Data processing (MapReduce, Spark, Storm, etc.), mining Big Data, data streams and analysis of time series, recommender systems, and social network analysis.

Learning outcome

The candidate will get knowledge of:
- Big Data frameworks.
- Mining of Big Data.
- Processing of data streams.
- Analysis of time series.
- Recommender systems.
- Analysis of social networks.

- Understand important aspects of Big Data.
- Ability to apply acquired knowledge for understanding data and select suitable methods for processing and analyzing Big Data.

Learning methods and activities

Lectures, seminars and project.

Further on evaluation

Portfolio assessment is the basis for the grade in the course. The portfolio includes a final written test (75%) and project or seminar (25%). The results for the parts are given in %-scores, while the entire portfolio is assigned a letter grade.
If there is a re-sit examination, the examination form may change from written to oral.
In the case that the student receives an F/Fail as a final grade after both ordinary and re-sit exam, then the student must retake the course in its entirety. Submitted work that counts towards the final grade will also have to be retaken.

Course materials

Will be informed of at semester start.


Examination arrangement: Portfolio assessment

Term Statuskode Evaluation form Digital exam Weighting Examination aids Date Time Room *
Spring ORD work 25/100
Spring ORD Written examination INSPERA 75/100 D
  • * 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.