ITF301416 Big Data - Processing and Analysis (Autumn 2018)
Facts about the course
- ECTS Credits:
- 10
- Responsible department:
- Faculty of Computer Science
- Course Leaders:
-
- Cathrine Linnes
- Edgar Bostrøm
- Teaching language:
- See Forms of teaching and learning
- Duration:
- ½ year
The course is connected to the following study programs
Compulsory course in:
- Bachelor in Information Systems
- Bachelor in Computer Science
Elective course in other study programmes.
Recommended requirements
Knowledge equivalent to the courses Databases and Introduction to programming.
Lecture Semester
5th semester (Autumn).
The student's learning outcomes after completing the course
Knowledge:
On completion of the course, the student has:
- in-depth knowledge about the relational model, relational database systems, and alternatives to relational databases
- knowledge about how large data volumes are managed efficiently in relational form
- knowledge about data warehouses/ business intelligence and 'big data'
- knowledge about how to find patterns in large data volumes, and how this can be used for example in business strategies, marketing, social sciences, natural sciences and other subjects
Skills:
On completion of the course, the student is capable of:
- handling large data volumes, structured in different ways and on different platforms (LAN, WAN, the cloud)
- creating expressions in relational algebra and seeing the connection between this and optimisation
- creating simple stored procedures and triggers
- designing a data warehouse
- analysing large data volumes using different techniques
- working with different types of database systems
General Competence:
On completion of the course, the student:
- has good knowledge of how large data volumes can be structured, processed, analysed and presented, on different platforms
- has more knowledge about how to search for information in and become acquainted with new IT systems
Content
Relational databases and large data volumes:
Relational algebra and query optimisation, other forms of optimisation, distributed databases and replicating, triggers and stored procedures. Alternatives to relational databases.
Data warehouse / business intelligence:
Various ways of building a data warehouse, the transformation process, data mining.
"Big data":
Massive data volumes, acquisition, storage, processing, visualisation. Legal and ethical aspects of big data.
Forms of teaching and learning
The course will largely be based on a combination of lectures and project work. Lectures will not be given on some of the topics included in the projects. It is up to the students to familiarise themselves with these topics.
If students from international partners attend courses, the lectures will be conducted in English.
Workload
Approx. 240 hours.
4 hours of lectures + exercises per week.
Coursework requirements - conditions for taking the exam
Deliver four projects (individually or in groups).
The coursework requirements must be approved before students may sit the exam.
Examination
Written examination
Individual written examination lasting 4 hours. No support materials permitted.
Letter grading scale A?F.
Examiners
The exam is assessed by the course instructor and an internal or external examiner.
Course evaluation
This course is evaluated by a
- Mid-term evaluation (compulsory)
The responsible for the course compiles a report based on the feedback from the students and his/her own experience with the course. The report is discussed by the study quality committee of the faculty of Computer Sciences.
Literature
Updated February 8th 2018
Thomas M Connolly and Carolyn E Begg. Database Systems: A Practical Approach to Design, Implementation and Management - 6th Edition, 2015.
Extra resources:
«Booz Allen Field Guide to Data Science»,
https://www.boozallen.com/content/dam/boozallen/documents/2015/12/2015-FIeld-Guide-To-Data-Science.pdf
Distributed material and web-based resources - will be posted on the learning platform.