Data Science Research Methods (970G1)

15 credits, Level 7 (Masters)

Autumn teaching

This module will provide you with the practical tools and techniques required to build, analyse and interpret 'big data' datasets. It will cover all aspects of the data science process including:

  • collection
  • munging or wrangling
  • cleaning
  • exploratory data analysis
  • visualisation
  • statistical inference
  • model building
  • implications for applications in the real world.

You will be taught how to scrape data from the internet, develop and test hypotheses, use principal component analysis (PCA) to reduce dimensionality, prepare actionable plans and present your findings. In the laboratory, you will develop your Python programming skills. You will introduced to a number of fundamental standard Python libraries/toolkits for data scientists including NumPy, SciPy, PANDAS and SCIKIT-Learn. In these sessions and yout coursework, you will work with real-world datasets and apply the techniques covered in lectures to that data.


50%: Lecture
50%: Practical (Laboratory)


100%: Coursework (Peer-review exercise, Portfolio, Report)

Contact hours and workload

This module is approximately 150 hours of work. This breaks down into about 44 hours of contact time and about 106 hours of independent study. The University may make minor variations to the contact hours for operational reasons, including timetabling requirements.

We regularly review our modules to incorporate student feedback, staff expertise, as well as the latest research and teaching methodology. We’re planning to run these modules in the academic year 2024/25. However, there may be changes to these modules in response to feedback, staff availability, student demand or updates to our curriculum. We’ll make sure to let you know of any material changes to modules at the earliest opportunity.