Forest Big Data (5 cr)
Code: 6M00CZ11-3001
General information
- Enrolment period
- 02.12.2017 - 18.03.2018
- Registration for the implementation has ended.
- Timing
- 01.01.2018 - 30.06.2018
- Implementation has ended.
- Credits
- 5 cr
- Virtual portion
- 3 cr
- Mode of delivery
- Blended learning
- Unit
- Forestry
- Campus
- TAMK Main Campus
- Teaching languages
- Finnish
- Seats
- 5 - 10
- Degree programmes
- Master's Degree in International Forestry
- Teachers
- Erkki Hietalahti
- Eeva Sundström
- Metsätalous Virtuaalihenkilö
- Person in charge
- Eeva Sundström
- Course
- 6M00CZ11
Objectives (course unit)
The student has a broad picture of the state-of-art of sources, production and uses of digitalization and big data in forestry and related businesses. The student is able to anticipate future trends related to big data and its effects on decision making and practices in forestry. Student is able to analyze the possibilities to use big data in forestry in different countries and implement convenient tools and methods.
Content (course unit)
Sources of data, producers of data, methods and tools for analyzing big data, applications of internet-of-things in forestry. Predictions of future trends
Exam schedules
No exam. The assessment will be based on the project work.
Assessment methods and criteria
The project report must be returned and passed.
Assessment scale
0-5
Teaching methods
Lectures
Visiting lecturers
Group Work and discussions
Use Case of Big Data, which will be reported
Learning materials
Articles
Ohlhorst, F. 2013. Turning Big Data into Big Money. E-book
Monino, Jean-Louis,Sedkaoui, Soraya 2016. Big Data, Open Data and Data Development. E-book.
Nataraj Dasgupta. 2018.Practical Big Data Analytics. E-book.
Student workload
Contact teaching 19 h. Rest of time students' own work.
Content scheduling
16.2.2018:
Pretask
16.3.2018:
Pretask results
Open data presentation
Planning the project work, discussing (via Tabula) about the issues during the planning process
Carrying out the project work
20.4.2018:
Stage of the project work
Visiting lectures
18.5.2018
Draft of the report
Visiting lecture
Practical training and working life cooperation
Visiting lectures
Project work related with work life.
Assessment criteria - fail (0) (Not in use, Look at the Assessment criteria above)
The student does not return the project report at all.
Assessment criteria - satisfactory (1-2) (Not in use, Look at the Assessment criteria above)
The student has defined some data sources over the issue, but the purpose and focus of the project work is defectively described. The influence and meaning on the work has not been included in the report.
Assessment criteria - good (3-4) (Not in use, Look at the Assessment criteria above)
The student has worked out the availability of data and has described the use of it.
The analysis of the data use is rather restricted and the description of the influence of big data use now and in the future is narrow.The student has covered his/her own understanding over the issue very narrowly or hardly at all.
Assessment criteria - excellent (5) (Not in use, Look at the Assessment criteria above)
The student has analysed the availability and possibilities of big data very extensively and has been able to reflect influence the use of big. The student can also analyse the need of skills and analysis the project work implementation might require. The student has also pondered the role of big data in the future and students own understanding over the issue.