Content Creation and Data (5op)

Osaamistavoitteet (Opintojakso)

After the course, students can
- critically evaluate the role, opportunities, and pitfalls of automated data collection and analytics in content creation and audience research
- understand how recommendation systems work and how they are applied
- compare and point out prominent data technologies in content creation
- develop content concepts that utilize data technologies

Sisältö (Opintojakso)

During the course, the participants study the basic concepts and processes of data analysis and information visualization. They critically analyze and experiment with the role, opportunities, and pitfalls of data technologies in content creation and audience research. They examine how automated data collection and user analytics impact on content creation. They explore how emerging data technologies, such as machine learning (ML) and artificial intelligence (AI), are used in content creation. As a synthesis of the exploration, students create and demonstrate an art and media concept that utilizes or reflects data technologies.

Topics
- Data analytics and information visualization
- Ethics of data analytics
- Audience metrics and content creation
- Machine learning and artificial intelligence
- Recommendation systems
- Personalization of services
- Emerging data technologies and content creation

Arviointikriteerit, tyydyttävä (1-2) (Opintojakso)

The student defines the basic concepts related to data analysis. S/he describes different ways data analysis influences content creation in her/his working field. S/he knows the common means of data analysis related to content creation and applies at least one of them in a practical case. S/he demonstrates ideas for applications that integrate content and data analysis. The student takes responsibility for her/his own work.

Arviointikriteerit, hyvä (3-4) (Opintojakso)

The student knows the basic concepts of data. S/he critically analyzes and provides various examples of how data analysis influences content creation in her/his field – now and in the future. S/he critically discusses the ethics of data analysis. S/he demonstrates creative ideas for applications that integrate content and data analysis. The student develops committedly her/his knowledge and skills in emerging media.

Arviointikriteerit, kiitettävä (5) (Opintojakso)

The student critically analyzes and provides alternative scenarios of how data analysis influences content creation in her/his field – now and in the future. S/he compares the situation across different industrial sectors and points out inter-dependencies and connections between them. S/he demonstrates creative and attractive ideas for applications that integrate content and data analysis.The student demonstrates excellent and open-minded attitude to her/his work, as well as towards fellow students’ knowledge and skills.

Arviointikriteerit, hyväksytty/hylätty (Opintojakso)

The student critically analyzes and provides alternative scenarios of how data analysis influences content creation in her/his field – now and in the future. S/he critically discusses the ethics of data analysis. S/he compares the situation across different industrial sectors and points out inter-dependencies and connections between them. S/he demonstrates creative and attractive ideas for applications that integrate content and data analysis. The student demonstrates an excellent and open-minded attitude to her/his work, as well as towards fellow students’ knowledge and skills.

Aika ja paikka

The study time of the online course is 10.1.-13.5.2022. There are three remote sessions in Teams:
1) Fri 21.1. 2022 13-16 pm, orientation + information visualization, visiting lecturer Dr. Harri Siirtola
2) Fri 11.2. 2022 13-16 pm visiting lecturers Ritva Leino, Professor of Practice at Tampere University: "Why content creation can’t be just data driven? Tools to focus on user needs" and Tapio Haaja, Head of Strategy and Development at Videolle (https://www.videolle.fi/en /): "How to create better video marketing based on data? What video metrics creatives should focus on?".
3) Fri 22.4.2022 13-16 pm lecturer(s) TBA

Arviointimenetelmät ja arvioinnin perusteet

During the course, the students are required to complete the tasks of six different units. As part of this course, the students will do some tasks of the Elements of AI open course by Helsinki University. Those tasks are assessed pass/fail. The tasks provided by the TAMK's course instructors are evaluated numerically (scale1-5).

Arviointiasteikko

0-5

Opiskelumuodot ja opetusmenetelmät

The course is carried out mainly as an asynchronous online Moodle course including course tasks and materials. There will be three three-hour remote sessions in Teams.

Oppimateriaalit

The online course consists of six units which materials are listed in Moodle.

Opiskelijan ajankäyttö ja kuormitus

1 cr consists of 27 hours of a student's work.

Sisällön jaksotus

The course consists of six units with respective online tasks. In general, there is 2-3 weeks time to complete the task(s) of each unit. The units are: 1) Data analysis and information visualization, 2) Ethics and data, 3) Audience research and content creation, 4) Machine learning and artificial intelligence, 5) Recommendation systems and personalization and 6) Implementation: intelligent assistants.

Toteutuksen valinnaiset suoritustavat

TAMK principles of recognition of elsewhere acquired competence (whole course or some parts of the course) apply to this course.

Harjoittelu- ja työelämäyhteistyö

TUNI multidisciplinary project collaboration.

Kansainvälisyys

International course materials.

Lisätietoja opiskelijoille

The study time of the online course in Moodle is 10.1.-13.5.2022. There are three remote sessions in Teams: 1) Fri 21.1. 2022 13-16 pm, 2) Fri 11.2. 2022 13-16 pm and 3) 22.4.2022 13-16 pm (Finnish time). The course consists of six units with respective online tasks. In general, there is 2-3 weeks time to complete the task(s) of each unit. The units are: 1) Data analysis and information visualization, 2) Ethics and data, 3) Audience research and content creation, 4) Machine learning and artificial intelligence, 5) Recommendation systems and personalization and 6) Implementation: intelligent assistants.

Arviointikriteerit - hylätty (0) (Ei käytössä, kts Opintojakson Arviointikriteerit ylempänä)

The student does not show evidence on the defined learning outcomes, e.g. there are missing or incomplete tasks.

Arviointikriteerit - tyydyttävä (1-2) (Ei käytössä, kts Opintojakson Arviointikriteerit ylempänä)

The student defines the basic concepts related to data analysis and discusses the ethics of data analysis. S/he describes different ways data analysis influences content creation in her/his working field. S/he knows the common means of data analysis related to content creation and applies at least one of them in a practical case. S/he demonstrates ideas for applications that integrate content and data analysis. The student takes responsibility for her/his work.

Arviointikriteerit - hyvä (3-4) (Ei käytössä, kts Opintojakson Arviointikriteerit ylempänä)

The student knows the basic concepts of data. S/he critically analyzes and provides various examples of how data analysis influences content creation in her/his field – now and in the future. S/he critically discusses the ethics of data analysis. S/he demonstrates creative ideas for applications that integrate content and data analysis. The student develops committedly her/his knowledge and skills in emerging media.

Arviointikriteerit - kiitettävä (5) (Ei käytössä, kts Opintojakson Arviointikriteerit ylempänä)

The student critically analyzes and provides alternative scenarios of how data analysis influences content creation in her/his field – now and in the future. S/he compares the situation across different industrial sectors and points out inter-dependencies and connections between them. S/he demonstrates creative and attractive ideas for applications that integrate content and data analysis.The student demonstrates excellent and open-minded attitude to her/his work, as well as towards fellow students’ knowledge and skills.