x !
Archived Curricula Guide 2015–2017
Curricula Guide is archieved. Please refer to current Curricula Guides
Master's Degree Programme in Computational Big Data Analytics

Learning outcomes

Students will
-have a thorough command of their own specializations
-be familiar with scientific thinking and capable of applying scientific working methods in their own area of specialization
-be motivated for lifelong learning
-be capable of undertaking scientific postgraduate (doctoral) studies
-be capable of applying the knowledge acquired and of functioning in internationalizing working life
-be capable of communicating in scientific situations
-be conversant with the ethical norms of the field and apply these in their own work

Students having completed the Master?s degree in this programme will have the knowledge and skills to
-choose suitable data analysis methods for the analysis tasks at hand from a reasonably wide selection of methods, including methods that are necessary for integrating data from different data sources during data preprocessing and/or analysis
-apply these methods to analyze the data,
-use efficient computational and statistical methods to manage and analyze big data,
-visualize the data / analysis results.

Students also have the theoretical knowledge which allows them to
-apply the analysis methods in previously unknown situations,
-understand in which situations the methods may perform well.


Master of Science Degree (120 ECTS)
- General studies 1-22 ECTS
- Advanced courses 45 ECTS
- Master's thesis 40 ECTS
- Other and optional studies 13-34 ECTS


Bachelor's degree in a suitable field or equivalent studies, and a good knowledge of English. Suitable studies include:
-Basic knowledge in Statistics (~20 ECTS). Following courses or equivalent:
--MTTTP1 Introduction to Statistics,
--MTTTP4 Elementary probability,
--MTTTP5 Basics of Statistical Inference and
--MTTTA1 Basics of statistical methods.
-Basic knowledge in Computer Science and programming skills (~15 ECTS). Following courses or equivalent:
--TIEP1 High Level Programming I,
--TIEP5 High Level Programming II and
--TIEA2.1 Introduction to Object-Oriented Programming.

The students BSc studies also need to include the following studies, and if not, they need to be studied and can be included in the "Complementing studies" category:
--MTTTA14 Matrices for Statistics and Computational Methods 5 ECTS
--MTTTA4 Statistical Inference 1 5 ECTS
- Computer Science:
-- TIETA6 Data Structures 10 ECTS

Please note that the courses listed above are mainly lectured in Finnish. Materials in English (lecture notes, exercises) are provided for independent studying and exams of complementing studies (MTTTA14, MTTTA4, TIETA6).

Further information

Finnish speaking students can use Finnish in some exams and write the Master's Thesis in Finnish.

expand all

Master's Degree Programme in Computational Big Data Analytics

General Studies in Master's Degree Programmes given in English 2015-2018 1–22 ECTS
One element from below
General studies in the Master's degree programmes given in English are different depending on the student's educational background. Please choose below only one of the three options A, B or C.
B) General studies for students with education in Finnish and BSc degree taken outside SIS 9–18 ECTS
Compulsory studies 9–13 ECTS
9–13 ECTS
Swedish course is required only if no Swedish studies were taken in the Bachelor's degree.
Free-choice studies 0–5 ECTS
0–5 ECTS
C) General studies for students who have taken their BSc degree at SIS 1–11 ECTS
Compulsory studies 1 ECTS
Basics of Information Literacy 1 ECTS is not required, only Personal study planning 1 ECTS from SISYY005.
Free-choice studies 0–10 ECTS
0–10 ECTS
Scientific Writing is recommended if the Master's thesis is written in English.
KKENMP3 Scientific Writing, 5 ECTS
Other and optional Studies in Big Data Analytics Programme 13–34 ECTS
Complementing Studies
Complementing studies according to the students background.
Optional Studies
Optional studies from the list provided or from other subjects.
School of Information Sciences