Big Data Analytics (5cr)

Evaluation scale

H-5

Content scheduling

Contact sessions in AC

Objective

In this module, you learn the main principles, models and recent tools for working with Big Data and performing Big Data Analytics. The module is covering some theoretical knowledge of Big Data Analytics as well as practical skills of using relevant tools and environments. The prerequisite for this course is solid knowledge in Statistics, Machine learning, Data analytics, Data infrastructure and Programming.

Content

Tieto puuttuu

Location and time

11.10.2021 - 05.11.2021. Online studying possible.

Materials

To be announced in the beginning of the course.

Teaching methods

Problem-based and team based learning may be applied where applicable. Students will seek information and solve problems related to subject presented. 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.

Employer connections

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.

Completion alternatives

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.

Student workload

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

Assessment criteria, satisfactory (1)

Evaluation target: You know the current tools for working with Big Data and are able to utilize them effectively.

Satisfactory
You describe the most important principles of working with Big Data and you know the recent tools and can use them with supervision and guidance.

Assessment criteria, good (3)

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

Assessment criteria, excellent (5)

Excellent
You produce complex and efficient implementations of Big Data Analytics tasks independently and according to the requirements. You can also choose the appropriate tools and justify your choice.

Qualifications

NULL