Training
The training will be for a period of three (3) consecutive days and will be held at the University of Technology Sydney (UTS) premises in Sydney, Australia. Participants will gain a comprehensive understanding of the CRISP-DM workflow, anomaly detection in Diesel Generators, and the essential role of data quality, enabling them to effectively support data scientists and ensure successful model deployment in electrical power generation systems.
Learning Outcomes
By the end of this course, participants will be able to:
- Understand the fundamentals of the CRISP-DM workflow and its application in anomaly detection.
- Demonstrate proficiency in supporting data scientists by providing domain-specific knowledge to create a multivariate anomaly detection model for Diesel Generators.
- Identify and address challenges faced by data scientists due to the lack of domain expertise.
- Evaluate data quality and its critical impact on model development, including techniques for data cleaning, handling missing values, and preprocessing.
- Debunk common myths about data science and big data by understanding the technical aspects and collaborative nature of data science projects.
Based on the specific requirements, available time, and prior knowledge of the participants the course contents can be adapted during the three days period and deviations are possible due to the configuration and implementation that are available in the reference system in Schiedam.