The course will require, in the order of importance (most important first), the following:

• Satisfactory programming skills (including object-oriented programming). E.g. the introductory Java-based programming courses TIEP1 High Level Programming I and TIEP5 High Level Programming II.
• Basics of databases. E.g. the course TIEP3 Databases.
• Basics of statistics. E.g. the course MTTP1 Introduction to Statistics.

Priority is given to 1) the students in the Master's Degree Programme in Computational Big Data Analytics and 2) students in the degree programmes in Computer Sciences and Mathematics and Statistics.

a) Information practices
b) Information retrieval systems
c) Interactive information retrieval
d) Task-based information retrieval

New enrolments for the spring term: contact the teacher responsible.

Enrolments for the autumn/whole academic year:

Modes of study

- Lectures
- Exercises (independent work)
- Exam

If you missed the original deadline, please enroll by sending an email to the teacher.

Recommended preceding studies:

• TIETA6 Data Structures and
• MTTTP1 Introduction to statistics

Or equivalent courses.

The course will require, in the order of importance (most important first), the following:

• Satisfactory programming skills (including object-oriented programming). E.g. the introductory Java-based programming courses TIEP1 High Level Programming I and TIEP5 High Level Programming II.
• Basics of databases. E.g. the course TIEP3 Databases.
• Basics of statistics. E.g. the course MTTP1 Introduction to Statistics.

Priority is given to 1) the students in the Master's Degree Programme in Computational Big Data Analytics and 2) students in the degree programmes in Computer Sciences and Mathematics and Statistics.

a) Information practices
b) Information retrieval systems
c) Interactive information retrieval
d) Task-based information retrieval

New enrolments for the spring term: contact the teacher responsible.

Enrolments for the autumn/whole academic year:

Modes of study

- Lectures
- Exercises (independent work)
- Exam

If you missed the original deadline, please enroll by sending an email to the teacher.

This course covers a wide range of statistical models and methods for data that are collected at different spatial locations (and at different times).

These data are called spatial  (or spatio-temporal) data, which are prevalent in many disciplines such as forestry, climatology, geology, environmental and health sciences, and economy, etc.

Topics covered include the geostatistical techniques of kriging and variogram analysis, point process methods for spatial case control, and area-level analysis.

If more than 40 students enroll priority will be given to 1) the students in the Master's Degree Programme in Computational Big Data Analytics, 2) the students in the Master's Degree Programme in Software Development, and 3) the students in the Master's Programme in Information Studies and Interactive Media, Specialization in Information Practices.

Recommended preceding studies: TIEP3 Data Bases ITIA4 Introduction to information retrieval TIETA17 Introduction to Big Data Processing

Recommended preceding studies:

• TIETA6 Data Structures and
• MTTTP1 Introduction to statistics

Or equivalent courses.

a) Information practices
b) Information retrieval systems
c) Interactive information retrieval
d) Task-based information retrieval

This course is organized by TUT and UTA students must

1. Apply for study right through Service for Cross-Institutional Studies.
2. Make sure to apply for the right to study well in advance so that you have enough time to also activate the user account of TUT and
3. to sign up for the studies according to the schedules of TUT.

Read carefully the UTA instructions.

This course cannot be taken if student has done TIETS39 Machine Learning Algorithms.

Note. The prerequisite SGN -courses are not required from UTA students.

New enrolments for the spring term: contact the teacher responsible.

Enrolments for the autumn/whole academic year:

Note. You can not include both MAT-72006 Advanced Algorithms and Data Structures  7 ECTS (TUT) and this course in your M.Sc. degree (120 ECTS).

The course belongs to followig study modules

• in CBDA “Advanced Courses in Methods of Computational Data Analytics"
• in SDE “Optional Course Unit in Software Development”
• in CS "Advanced studies" in Computational methods and data bases and information retrieval.

Statistics workshop is intended for those who want to come to solve the exercises (especially the intermediate studies in Statistics) together with other students assisted by the teacher. The workshop does no accumulate the credits for the course examinations.

a) Information practices
b) Information retrieval systems
c) Interactive information retrieval
d) Task-based information retrieval

Recommended preceding studies: Data Structures, basics of statistics, basics of mathematics and linear algebra.

NOTE, contact teaching (lectures and excercises) is in Finnish. The lectures are based on study material which is available in English. Weekly exercises will be available in English and are to be sent to the teacher.

New enrolments for the spring term: contact the teacher responsible.

Enrolments for the autumn/whole academic year:

The course belongs to followig study modules

• in CBDA “Advanced Courses in Methods of Computational Data Analytics"
• in SDE “Optional Course Unit in Software Development”
• in CS "Advanced studies" in Computational methods and data bases and information retrieval.

Statistics workshop is intended for those who want to come to solve the exercises (especially the intermediate studies in Statistics) together with other students assisted by the teacher. The workshop does no accumulate the credits for the course examinations.