Professional & Academic Development - COM00195M

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  • Department: Computer Science
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2025-26

Module will run

Occurrence Teaching period
A Semester 2 2025-26

Module aims

This module prepares students for careers in computing and academic research. The module involves a multi-pronged approach to personal and professional development by providing students an opportunity to self-identify learning needs and skill development with the support of a mentor. Students will develop the competencies required for practising within the complex framework of accountability, ethical and professional boundaries in the multi-disciplinary workplace. The module captures the following key activities:

  1. Planning
  2. Assessing/reflecting
  3. Professional development/self-directed training
  4. Communication

Module learning outcomes

During this module, students will produce a short project in which they will demonstrate, and reflect on, professional skills. By the end of the module, they will be able to::

  • Demonstrate and critically reflect on their own and others' written and verbal communication styles for a broad range of professional, public and academic audiences
  • Describe and justify a project proposal that is grounded in existing academic literature and professional practice

  • Identify, describe and evaluate personal development opportunities and training needs through self-reflection and self-directed learning

Indicative assessment

Task % of module mark
Essay/coursework 100

Special assessment rules

None

Indicative reassessment

Task % of module mark
Essay/coursework 100

Module feedback

Feedback is provided throughout the sessions, and after the assessment as per normal University guidelines

Indicative reading

Key texts

  • J. Zobel, Writing for Computer Science. London: Springer London, 2014. doi: 10.1007/978-1-4471-6639-9
  • E. R. Tufte, The Visual Display of Quantitative Information, 2nd ed. Cheshire, Conn: Graphics Press, 2001. https://yorsearch.york.ac.uk/permalink/f/1kq3a7l/44YORK_ALMA_DS21202699750001381
  • D. Huff, How to Lie with Statistics, Repr. London: Penguin, 1991. https://yorsearch.york.ac.uk/permalink/f/1kq3a7l/44YORK_ALMA_DS21190573200001381
  • A. Watt, Project management, BCcampus Open Education Pressbooks, 2004
  • C. Ghezzi and G. Ghezzi, Being a researcher, Springer, 2020

Other relevant links:

  • The illustrated guide to a Ph.D., https://matt.might.net/articles/phd-school-in-pictures/
  • A Gentle Introduction to Statistical Power and Power Analysis in Python, https://machinelearningmastery.com/statistical-power-and-power-analysis-in-python/
  • Seeing through statistics, https://yorsearch.york.ac.uk/permalink/f/1d5jm03/44YORK_ALMA_DS21223399890001381
  • Statistics for experimenters : design, innovation, and discovery, https://yorsearch.york.ac.uk/permalink/f/1kq3a7l/44YORK_ALMA_DS21221776990001381
  • Heath, C., & Heath, D. (2008). Made to stick: Why some ideas take hold and others come unstuck. Random House