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Data science research project - CHE00046M

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  • Department: Chemistry
  • Credit value: 60 credits
  • Credit level: M
  • Academic year of delivery: 2023-24
    • See module specification for other years: 2024-25
  • Notes: This is an independent study module

Module summary

You will work with a project director in an application field to perform exploratory analysis, development and testing of a hypothesis, implementation of the analysis, interpretation of the results, critical evaluation and communication of the findings. This will build on the work you did with your director in the project preparation module. Projects will be offered by each of the contributing departments, although we are also open to students seeking projects from elsewhere in the institution.

Module will run

Occurrence Teaching period
A Summer Semester 2023-24

Module aims

The aim of the module is to give you experience of performing data science research on a novel research question, of organizing and performing a data science project, of documenting the process and of communicating the results in different formats. You will also learn how to work and communicate with subject domain specialists who may not have the same data science background as you. This will prepare you to implement your own data science projects outside of a learning environment.

Module learning outcomes

Students will be able to:

  • Execute a data science research project

  • Execute the different stages of data science research from preliminary analysis through to presentation and communication

  • Select appropriate data science methods to address a research question

  • Critically evaluate and compare the results of data science methods

  • Communicate project aims and findings through a written report and oral presentation

  • Present scientific data using appropriate visualisations

Module content

  • Planning and managing a research project

  • Support in applying data science methods to a research question

  • Support in communicating the results of a project in oral and written form

  • Norms of academic communication

Indicative assessment

Task % of module mark
Essay/coursework 75
Oral presentation/seminar/exam 25

Special assessment rules

None

Additional assessment information

Research project report

6000 word written report + computer program

75%

Oral presentation of the project

Oral presentation + questions 25 mins

25%

Indicative reassessment

Task % of module mark
Essay/coursework 75

Module feedback

Feedback will be provided on a draft of the project report and presentation

Indicative reading

  • Introduction to data science : a Python approach to concepts, techniques and applications
    Laura Igual, Santi Segui´. Springer 2017

  • Python for data analysis : data wrangling with Pandas, NumPy, and IPython
    Wes McKinney. O'Reilly 2017

  • Pro Git
    Scott Chacon, Ben Straub. Apress 2014

  • Python and Matplotlib essentials for scientists and engineers
    Matt A. Wood. Claypool Publishers 2015

  • Visualization for the Physical Sciences
    Lipsa et al. Computer graphics forum, 2012, Vol.31 (8), p.2317-2347

  • Introduction to scientific visualization
    Helen Wright. Springer 2007

  • Data Modeling Essentials
    Graeme Simsion, Graham Witt. Morgan Kaufmann 2004

  • Database design - Adrienne Watt, Nelson Eng. BC Open Textbook Project 2014


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.