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Big Data ja tiedonlouhinta (5 cr)

Code: TX00CK73-3007

General information


Enrollment

02.05.2019 - 01.09.2019

Timing

26.08.2019 - 13.10.2019

Number of ECTS credits allocated

5 op

Mode of delivery

Contact teaching

Unit

(2016-2018) Tieto- ja viestintätekniikka

Campus

Myllypurontie 1

Teaching languages

  • Finnish

Seats

0 - 40

Degree programmes

  • Tieto- ja viestintätekniikan tutkinto-ohjelma

Teachers

  • Juha Kopu
  • Vesa Ollikainen

Groups

  • TVT17-O
    Ohjelmistotuotanto

Objective

Upon completion of the course the students have understanding of the capabilities of big data and data science methods, especially data mining. The participants have got hands-on experience in storage, retrieval, processing as well as data mining analysis methods and tools.

Content

- Big Data in ICT business: applicability, opportunities, models and processes, legal and ethical constraints.
- Acquisition and preprocessing of data.
- Data management solutions.
- The approaches for data mining (classification, association analysis, clustering, predicting numerical values) as well as their fields of applicability and usage
- Data mining software.
- Validation and visualisation of the results.
- Text mining and web mining.

Evaluation scale

0-5

Assessment criteria, satisfactory (1)

The student has achieved the course objectives fairly. The student will be able to identify, define and use the course subject area’s concepts and models. The student has completed the required learning exercises in minimum requirement level.

Assessment criteria, good (3)

The student has achieved the course objectives well, even though the knowledge and skills need improvement on some areas. The student has completed the required learning exercises in good or satisfactory level. The student is able to define the course concepts and models and is able to justify the analysis.

Assessment criteria, excellent (5)

The student has achieved the objectives of the course with excellent marks. The student master commendably the course subject area’s concepts and models. The student has completed the required learning exercises in good or excellent level. The student is able to make justified and fluent analysis.

Assessment criteria, approved/failed

The student has achieved the course objectives fairly. The student will be able to identify, define and use the course subject area’s concepts and models. The student has completed the required learning exercises in minimum requirement level.

Qualifications

No formal requirements. Data management and mathematics skills will be helpful in acquiring the contents of the course.
The course on Probalility Calculus and Statistics supports this course.