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:
-Statistics:
--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).

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