Process Automation (4 cr)
Code: DT10027-3001
General information
- Enrollment
- 01.10.2023 - 31.10.2023
- Registration for the implementation has ended.
- Timing
- 08.01.2024 - 07.04.2024
- Implementation has ended.
- Number of ECTS credits allocated
- 4 cr
- Local portion
- 4 cr
- Mode of delivery
- Contact learning
- Unit
- Tradenomi / Tietojenkäsittely (DD)
- Campus
- Wärtsilä Campus Karjalankatu 3
- Teaching languages
- Finnish
- Seats
- 5 - 50
- Degree programmes
- Degree Programme in Business Information Technology
- Teachers
- Jarmo Talvivaara
- Teacher in charge
- Jarmo Talvivaara
- Groups
-
DTNS22Information Technology (BBA), Full-time Studies, Fall, 2022
- Course
- DT10027
Evaluation scale
H-5
Objective
As a Student, you
- understand the basics, general concepts, objectives, benefits and challenges of process automation.
- know different applications of automation and are able to evaluate suitable implementation methods and techniques.
- understand and are able to evaluate different aspects of process automation (tasks, workflows, processes) and size classes (task-specific and holistic, assisted, non-assisted, autonomous and intelligent automation).
- are able to implement process automation with automation tools.
- are familiar with the limitations of deterministic automation and how to solve them by means of automation utilizing machine learning.
- are able to measure and evaluate the performance and functionality of the automation solutions of the implemented processes.
- are able to identify development targets for process automation and produce development measures that improve automation.
- understand the importance of data security in process automation solutions.
- are able to apply solutions that improve information security in process automation.
Content
Process automation; business processes, IT automation, application development automation, other application targets.
Robotic process automation.
Task Automation
Workflow Automation
Process Automation
Automation platforms; on-premises, cloud computing services and hybrid.
Orchestration.
Hyperautomation
Measurement of automation processes, KPIs, evaluation and development.
Basics of data-driven automation utilizing machine learning.
Mining of tasks and processes (task Mining, data Mining)
Automation security.