Data Analytics (3 cr)

Code: 5N00EI59-3003

General information

Enrolment period: 01.06.2021 - 03.09.2021; Registration for the implementation has ended.

Timing: 23.08.2021 - 24.12.2021; Implementation has ended.

Credits: 3 cr

Local portion: 3 cr

Mode of delivery: Contact learning

Campus: TAMK Main Campus

Teaching languages: Finnish

Seats: 0 - 50

Degree programmes: Degree Programme in ICT Engineering, students who began in 2014-2018

Teachers: Miika Huikkola

Person in charge: Miika Huikkola

Course: 5N00EI59

Objectives (course unit)

The student
- is able to handle data sets
- has basic knowledge of mathematics related to data-analysis
- is able to use and apply classical data analysis for solving technical problems
- is familiar with basics and methods of regression, classification and clustering

Content (course unit)

• Classical Data Analysis
• Classification, Decision Trees, Random Forests
• Clustering, K-means
• Regression, Linear Regression, Logistic Regression
• Basics of Neural Network

Assessment criteria, satisfactory (1-2) (course unit)

The student is able to handle data and knows the basics of data analysis and the related key methods. The student is able to calculate simple tasks related to the topics of the course, which are similar to the examples presented.

Assessment criteria, good (3-4) (course unit)

In addition to the above, the student is able to apply the course knowledge in new situations and justify his/her solutions. The student is able to use the concepts and methods related to the subjects of the course correctly. The student performs the given tasks independently.

Assessment criteria, excellent (5) (course unit)

In addition to the above, the student has a comprehensive understanding of the course topics and their use in problem solving, as well as the ability to present and justify his/her solutions logically.

Exam schedules

Loppukoe viimeisellä opetusviikolla Moodlessa ilmoitettavana ajankohtana

Uusintatentit
dd.1.2022 klo 17-20
dd.2.2022 klo 17-20

Evaluation methods and criteria

Opintojakson suorittaminen perustuu seuraaviin osa-alueisiin
Aktiivinen osallistuminen opetukseen
Harjoitustyö(t)
Harjoitustehtävät
Loppukoe / tentti

Assessment scale

0-5

Teaching methods

Lähiopetus / etäopetus, yhteisöllinen oppiminen, harjoitustehtävät, harjoitustyöt

Learning materials

Moodlessa ilmoitettu ja julkaistava materiaali

Student workload

Oppitunteja n. 30 h
Itsenäinen opiskelu n. 25 h
Harjoitustyö n. 25 h

Content scheduling

-Matlab perusteet
-Klassinen data-analyysi
-Data-analyysin menetelmiä
-Harjoitustyöt