Study Guide

Objectives (course unit)

The goal is that after completing the module, the student will be able to find different areas and principles of business competence in game industry. They are able to identify the tools and techniques of data science and analysis in game industry. The goal is getting to know business idea, business competence, markets, goals and funding.

Content (course unit)

Game Business,
Data Analytics,
Funding,
Game Markets.

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

The student is able to identify the tools and techniques of data science and analysis in game industry.
The student can describe key concepts of business plan.

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

The student is able to demonstrate how game market works.
The student can demonstrate understanding of funding a game.

Assessment criteria, excellent (5) (course unit)

The student is able to apply the learned skills for designing a data analyze toolset for a game.
The student is able to apply the learned skills for creating a plan to get funded as a game team.

Location and time

Data analytics part: C3-13b or remote education (see Moodle)

Exam schedules

Data analytics part: no exam, assessment based on working during the course (small tests, exercises, presentations, possibly participation)

Assessment methods and criteria

Data analytics part: grading based on points gathered during the data analytics part activities including tests, exercises and presentations (tentatively: [0%–50%[ -> 0, [50%–60%[ -> 1, [60%–70%[ -> 2, [70%–80%[ -> 3, [80%–90%[ -> 4, [90%–100%[ -> 5). For some activities, peer assessment and/or self-assessment may be applied.

Assessment scale

0-5

Teaching methods

Data analytics part: remote teaching (see Moodle), independent studying, problem-based learning, exercises, presentations, working in groups

Learning materials

Data analytics part: lecture slides/notes/examples/datasets, relevant books and web sources to be introduced during the course

Student workload

Data analytics part: planned student workload approximately 81 hours, distributed evenly over the first period

Completion alternatives

Data analytics part: n/a

Practical training and working life cooperation

Data analytics part: possibly a guest lecture given by a representative of a relevant branch of industry (not confirmed)

Assessment criteria - fail (0) (Not in use, Look at the Assessment criteria above)

Data analytics part: insufficient number of points accumulated from the course activities. Also, participating in peer assessment or self-assessment processes may be required in order to be able to pass the course.

Assessment criteria - satisfactory (1-2) (Not in use, Look at the Assessment criteria above)

Data analytics part: based on the number of points accumulated from the course activities.

Assessment criteria - good (3-4) (Not in use, Look at the Assessment criteria above)

Data analytics part: based on the number of points accumulated from the course activities.

Assessment criteria - excellent (5) (Not in use, Look at the Assessment criteria above)

Data analytics part: based on the number of points accumulated from the course activities.

Objectives (course unit)

The goal is that after completing the module, the student will be able to find different areas and principles of business competence in game industry. They are able to identify the tools and techniques of data science and analysis in game industry. The goal is getting to know business idea, business competence, markets, goals and funding.

Content (course unit)

Game Business,
Data Analytics,
Funding,
Game Markets.

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

The student is able to identify the tools and techniques of data science and analysis in game industry.
The student can describe key concepts of business plan.

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

The student is able to demonstrate how game market works.
The student can demonstrate understanding of funding a game.

Assessment criteria, excellent (5) (course unit)

The student is able to apply the learned skills for designing a data analyze toolset for a game.
The student is able to apply the learned skills for creating a plan to get funded as a game team.

Location and time

Data analytics part: Weeks 35–41: remote education (see Moodle)

Exam schedules

Data analytics part: no exam, assessment based on working during the course (small tests, exercises, presentations)

Assessment methods and criteria

Data analytics part: grading based on points gathered during the data analytics part activities including tests, exercises and presentations (tentatively: [0%–50%[ -> 0, [50%–60%[ -> 1, [60%–70%[ -> 2, [70%–80%[ -> 3, [80%–90%[ -> 4, [90%–100%[ -> 5). For some activities, peer assessment and/or self-assessment may be applied.

Assessment scale

0-5

Teaching methods

Data analytics part: remote teaching (see Moodle), independent studying, problem-based learning, exercises, presentations, working in groups

Learning materials

Data analytics part: lecture slides/notes/examples, relevant books and web sources to be introduced during the course

Student workload

Data analytics part: planned student workload approximately 81 hours, distributed evenly over the first period

Completion alternatives

Data analytics part: n/a

Practical training and working life cooperation

Data analytics part: possibly a guest lecture given by a representative of a game development company (not confirmed)

Assessment criteria - fail (0) (Not in use, Look at the Assessment criteria above)

Data analytics part: insufficient number of points accumulated from the course activities. Also, participating in peer assessment or self-assessment processes may be required in order to be able to pass the course.

Assessment criteria - satisfactory (1-2) (Not in use, Look at the Assessment criteria above)

Data analytics part: based on the number of points accumulated from the course activities.

Assessment criteria - good (3-4) (Not in use, Look at the Assessment criteria above)

Data analytics part: based on the number of points accumulated from the course activities.

Assessment criteria - excellent (5) (Not in use, Look at the Assessment criteria above)

Data analytics part: based on the number of points accumulated from the course activities.