Data analytics 1 (5op)

Arviointiasteikko

H-5

Sisällön jaksotus

On campus: 28.09.2020 - 16.10.2020

Tavoitteet

This module provides you the essential practical knowledge about the Data Analytics environments and tools. In this module, you focus on learning the main principles of programming Data Analytics tasks and the main libraries used. The prerequisite for this course is a successful completion of “Mathematics and Statistics 1 and 2” or the possession of the relevant subject matter knowledge and skills.

Sisältö

Tieto puuttuu

Aika ja paikka

On campus: 28.09.2020 - 16.10.2020

Opetusmenetelmät

Problem-based and team based learning may be applied where applicable. Students will seek information and solve problems related to subject guided. Different activating vocational teaching methods will be used depending on the group taught and the facilities available. If applicable, conventions from selected areas in software industry may be used as a part of teaching. Teacher guides the learning process by short introductory lectures and/or initial subject related material to be studied before practical work. Teacher prepares the setting for learning and provides coaching for the students. Teaching sessions may take place on campus and online. The main focus will be on guided knowledge searching and practical work on it.

Harjoittelu- ja työelämäyhteistyö

Software industry conventions are used. This course may include a case company selected by the university. In addition, students are able to propose your own case companies, whose business information and data analytics they would like to develop. Students must provide a free form commission agreement from their own case companies.

Toteutuksen valinnaiset suoritustavat

Before the course starts, students may propose to the course teachers their personal implementation plan. The plan must be realistic and result in verificable development in the targeted competence(s). In addition, guidance from MIGRI and student visa must be taken into account. Course teachers accept or reject student's plan based on their own consideration.

Opiskelijan ajankäyttö ja kuormitus

The student's estimated workload of this implementation is 135 h as follows:
Roughly half of which is Independent individual and teamwork guided when needed.
Rest of hours will be mostly learning the subject with guided practical and knowledge seeking exercises.

Arviointikriteerit, tyydyttävä (1)

Evaluation target: You are able to utilize specific Data Analytics tools and environments.

Satisfactory
You implement simple Data Analytics tasks in the programming environment, but you often need assistance and instructions.

Arviointikriteerit, hyvä (3)

Good
You implement typical Data Analytics tasks according to the requirements, and you can solve related challenges independently.

Arviointikriteerit, kiitettävä (5)

Excellent
You produce complex and efficient implementations of Data Analytics tasks independently and according to the requirements.

Esitietovaatimukset

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