IND320 Data to decision
Credits (ECTS):10
Course responsible:Kristian Hovde Liland
Campus / Online:Taught campus Ås
Teaching language:Engelsk
Course frequency:Annually
Nominal workload:Expected workload for 10 credits is 250 hours.
Teaching and exam period:This course has teaching/evaluation in the Autumn parallel.
About this course
The main emphasis will be on end-to-end data handling, analysis, and presentation for monitoring and decision support. Through various use cases, data will be accessed from databases, repositories, and/or sensors, filtered/cleaned, combined/analyzed, and displayed for the end user. Dashboards with plots and key performance indicators will be the main output.
The course supports the UN's sustainability goals 4 (good education - industrial relevance for students), 9 (industry, innovation and infrastructure - better utilization of industrial data, internal and external infrastructure), 10 (less inequality - use of open platforms, programming languages and tools) and 12 (responsible consumption and production - streamlining and improving production processes).
Learning outcome
Knowledge
Theoretical aspects such as database queries, APIs, algorithms, etc. will be highlighted where there is a need, while practical problems are the main topic.
Skills
The student will learn to plan, implement and test a system for data capture, data processing and making it available to the end user. She will learn about current data types, data quality, as well as adopting tools needed to provide decision support and improved automation.
General competence
By using mandatory tasks that build on each other and involve students at intersections through peer review, students will learn about short-term and long-term goals, the use of constructive criticism and continuous improvement of processes and products.
The lectures will be a mixture of theory and practical activities. Instructional videos can be included for some background theory so that classroom activities can be kept more practical. Discussions of, for example, the consequences of design and implementation choices, goals and goal achievement will be included in groups and plenary sessions.
A longitudinal semester project with implementation and documentation is a compulsory activity in the course. Milestones related to the topics in the lectures are peer-reviewed along the way, while final approval is made by the teacher or teaching assistant.
- The teacher will be available at the lectures, and a teaching assistant or teacher will be available at practice lessons. Padlet.com or a similar tool will be used for questions and answers between lectures so that teachers, teaching assistants and fellow students can communicate openly. Canvas and GitHub/GitLab will be used for sharing plans, lecture material and code.
- Programming similar to INF120
Programming similar to INF200.
Machine Learning similar to DAT200 or MLA210.
Databases similar to INF230.
- Oral or written exam (3,5 hours in the exam period) with grades A-F.
- The censor will be involved in the development of criteria for quality and assessment structure.
- Semester project with implementation and documentation. The project will be module-based, where each module builds on previous activity and is peer-assessed along the way before final approval by the subject teacher/teaching assistant to be able to take the exam.
- The number of students who will take the course is very difficult to estimate. Some of the learning activities will have to be adapted to the number of students participating. The exam will be oral if there are fewer than 11 students, otherwise.
Lectures: approx. 52 hours.
Exercises: approx. 26 hours.
Compulsory activity and self-study: approx. 172 hours.
- No