Data Science Research Methods (970G5)
15 credits, Level 7 (Masters)
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, visualization, statistical inference and model building and implications for applications in the real world.
During the module, 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 their findings. In the laboratory, you will develop your Python programming skills and be introduced to a number of fundamental standard Python libraries/toolkits for Data Scientists including NumPy, SciPy, PANDAS and SCIKIT-Learn. In these sessions and your coursework, you will work with real-world datasets and apply the techniques covered in lectures to that data.
50%: Practical (Laboratory)
100%: Coursework (Observation, 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.
This module is running in the academic year 2020/21. We also plan to offer it in future academic years. It may become unavailable due to staff availability, student demand or updates to our curriculum. We’ll make sure to let our applicants know of such changes to modules at the earliest opportunity.
This module is offered on the following courses: