Fundamentals of the PhD education at SCIENCE - module 2 - K9
PhD School at the Faculty of SCIENCE at University of Copenhagen
The purpose of the module is to introduce the students into Data Science, Data Management, and Career Management:
• Many students will apply/develop Data Science methods (data analysis, statistics, and machine learning) directly in their own research; and all students should be aware of this potential.
• Some students will directly apply Data Management principles and all students should be aware of the general policies.
• All students should actively manage their careers.
The module consists of 10 morning/afternoon sessions during the on-campus module week (7 on Machine Learning and Statistics, 2 on Data Management, and 1 on Career Management).
The Data Science element will aim to ensure that PhD students will consider Data Science as a methodology and allow them to apply basic Statistics and Machine Learning methods when appropriate in their research. The element will cover:
• Machine learning foundations and methods
• Statistics foundations and methods
• Programming environments for Statistics and Machine Learning
• Data Science caveats and best practice
• Introduction into AI and high-performance computing
The aim of the Data Management element is to equip PhD students with knowledge and skills to:
• manage data and primary materials responsibly during their PhD projects.
• create open and reproducible research outputs.
The aim of the Career Management element is that PhD students start to explore their values, motivation, and the great variety of career options that are open to them after their PhD. The element will cover:
• Megatrends in the labour market and typical career paths for PhDs from the natural sciences
• Motivation, values, and career priorities
• Change of scientific environment
• Networking for career development
Learning outcome
The learning outcomes for the three elements are given below. Each outcome is marked K for Knowledge, S for Skills, or C for Competences.
Data Science Element:
• Know Data Science as a research methodology (K).
• Apply basic Statistics and Machine Learning computational frameworks and methods when appropriate in their research (S).
• Build a network for potential inter-disciplinary data science collaborations (C).
Data Management Element:
• Identify relevant legislation, requirements, and policies on data management applicable to research projects at UCPH (K).
• Recognize recommendations and requirements regarding open and reproducible research designs, data collection, and data publication (K).
• Classify data and conduct a risk assessment to ensure the secure storage of data (S).
• Assess how data can be preserved and shared to guarantee FAIR use of data (S).
• Assess when to use electronic lab notes (S).
• Apply recommendations and requirements regarding open and reproducible research designs, data collection, and data publication (S).
• Contribute to planning and conducting appropriate data and materials management in all phases of their PhD project (C).
• Be able to adhere to best practices of open and reproducible research (C).
Career Management Element:
• Differentiate typical career paths for SCIENCE PhDs (K).
• Describe selected understandings of motivation (K).
• Explore and explain their career priorities (S).
• Build a professional profile on LinkedIn (S).
• Assess how different uses of their change of scientific environment may impact their career (S).
• Integrate knowledge of typical career paths for SCIENCE PhDs with an understanding of their personal career priorities (C).
• Develop a personal networking strategy and build a professional network that supports their career interests (C).
Lecturers
The Data Management element may include guest lectures on reproducibility, electronic lab notebooks, and GDPR.
Remarks
The PhD School at the Faculty of SCIENCE is committed to building a learning environment that welcomes, includes, and empowers all its PhD students. By building a Faculty-wide peer community of PhD-students with the Fundamentals course, we secure that all PhD candidates are given adequate instruction in a range of essential competences that lie outside the core scientific research skills offered through supervision, tool-box and specialized PhD courses. Moreover, we build bridges between different research programmes at the Faculty of SCIENCE and offer diverse, multidisciplinary fora of exchange strengthening the PhD candidates’ scientific and social networks and laying the foundation for a strong alumni culture.