Interdisciplinary Research Computing

Learning the skills and practicalities of research computing  

Module details

  • Offered to 2nd Year students in Spring term
  • Mondays 16:00-18:00
  • Planned delivery: On campus (South Kensington)
  • 1-term module worth 5 ECTS 
  • Available to eligible students as part of I-Explore 

This module will introduce you to the fundamental concepts of research computing and the importance of research computing and data science in contemporary research. You do not need any prior research computing skills, just curiosity about the subject and a desire to learn new skills and techniques. The module will include an introduction to general research computing concepts and practical skills for applying research computing and some data science techniques. There will also be a variety of real-world scientific examples. 

Throughout the module, you will work on a group project with students from different departments. During the taught sessions as well as outside of the classroom, each group will develop a project idea and negotiate a feasible project design, timeline and work plan. Together, you will learn how to break down a real-world problem into achievable steps which can be resolved using research computing tools and to effectively communicate project progress and results. 

Please note: The information on this module description is indicative. The module may undergo minor modifications before the start of next academic year. 

Accordian

Learning outcomes

By the end of this module, you will better be able to: 

  • Reflect on the relevance and explain the importance of research computing and data science in contemporary research within your chosen discipline 
  • Reformulate a real-world problem as a research question and break it down into achievable steps that can be resolved using research computing tools 
  • Apply research computing tools to solve computational problems and apply principles of collaborative software development 
  • Critically evaluate and iteratively improve project proposals based on peer feedback 
  • Effectively communicate project progress and results to a multidisciplinary audience 

Indicative core content

  • Exposure to various research computing and data science skills (programming, version control, the command line, accessing high performance computing platforms) 
  • Examples of scientific application of research computing technologies  
  • Research computing project design  
  • Collaborative project development  
  • Effective communication of project outputs  

Learning and teaching approach

During class time, you will explore the concepts through short technical and scientific talks, and discussions with the speakers. You will also engage in:  

  • Hands-on exercises 
  • Group discussions 
  • Challenges 
  • Using online learning tools 

You will receive verbal feedback on formative exercises during the classes. Feedback on the project proposal draft will help you finalise an achievable plan for the group project as well as work division within the group. For the final assignments, you will receive written feedback within 10 working days. 

Assessment

Coursework:  

  • Demonstrate Python learning - short reflection (5%)
  • Scaffolded Python coding assignment (10%)
  • Design and propose final project (25%)  
  • Reflect on the module and on personal progress (30%) (must be passed)  

Practical: 

  • Communicate project results - poster and GitHub repository (30%) 

Key information

  • Requirements: It is compulsory to take an I-Explore module during your degree (you’ll take an I-Explore module in either your 2nd or 3rd year, depending on your department). You are expected to attend all classes and undertake approximately 105 hours of independent study in total during the module. Independent study includes for example reading and preparation for classes, researching and writing coursework assignments, project work and preparing for other assessments 
  • I-Explore modules are worth 5 ECTS credit towards your degree; to receive these you will have to pass the module. The numerical mark that you obtain will not be included in the calculation of your final degree result, but it will appear on your transcript  
  • This module is designed as an undergraduate Level 6 course 
  • This module is offered by the Graduate School