ITF301415 Big Data - Processing and Analysis (Autumn 2015)

The course is connected to the following study programs

Compulsory course in:

• Bachelor in Computer Science
• Bachelor in Information Systems

Elective course in other study programmes.

Prerequisites

The course builds on knowledge corresponding to the courses "Databases" and "Introduction to Programming".

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)
• setting up and operating a database system
• 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. Different forms of connections between client and server. 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.

Workload

4 hours of lectures + exercises per week.

Examination

Portfolio examination and written examination

The individual final grade is awarded on the basis of two partial exams. Students must pass each partial exam in order to pass the whole course.

Partial exam 1
Portfolio examination consisting of reports from four projects carried out in a group. The portfolio is assessed as a whole and one overall grade is awarded. The portfolio examination counts 49%. Individual grades are awarded.

Partial exam 2
Individual written examination lasting 3 hours that counts 51%. No support materials permitted.

Letter grading scale A?F.

In the case of resit and rescheduled examinations, each part of the examination may be retaken. In the case of resit and rescheduled examinations, the topic of the project assignments must be agreed with the course instructor.

Course evaluation

This course is evaluated as follows:

• Mid-semester evaluation (optional)
• Final evaluation (compulsory)

The course instructor prepares a course report on the basis of student feedback and on his/her own experiences of the course. Course reports are discussed with the Committee for Study Quality at the Faculty of Computer Sciences.

Literature

Thomas M Connolly and Carolyn E Begg. Database Systems: A Practical Approach to Design, Implementation and Management - 6th Edition.

Distributed material and web-based resources - to be published on Fronter.