Data Science Research Methods (L7) (970G1)
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Data Science Research Methods (L7)
Module 970G1
Module details for 2024/25.
15 credits
FHEQ Level 7 (Masters)
Module Outline
This module will provide students 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, they 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, students will develop their 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 their coursework, students will work with real-world datasets and apply the techniques covered in lectures to that data.
Library
Introduction to Data Science: a Python approach to concepts, techniques and applications – Igual and Segui (2017)
Doing Data Science: Straight Talk from the Front Line – Schutt and O’Neil (2013)
Data Science from Scratch: first principles with Python – Grus (2015)
Module learning outcomes
Analyse real-world 'big data' datasets using appropriate state of the art tools and techniques.
Design testable hypotheses and apply suitable experimental methods to determine whether those hypotheses are supported by the data.
Evaluate the applicability of different tools and techniques for data analysis and visualisation in different scenarios.
Summarise an analysis of big data and apply data visualisation tools and techniques to present data in an appropriate format
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 70.00% | |
| Coursework components. Equal weighting for all components. | ||
| Report | A1 Week 1 | |
| Coursework | 10.00% | |
| Coursework components. Weighted as shown below. | ||
| Peer review exercise | T1 Week 9 | 50.00% |
| Portfolio | T1 Week 11 | 50.00% |
| Coursework | 20.00% | |
| Coursework components. Weighted as shown below. | ||
| Report | T1 Week 8 | 100.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Autumn Semester | Lecture | 2 hours | 11111111111 |
| Autumn Semester | Laboratory | 2 hours | 11111111111 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
Dr James Van Yperen
Assess convenor, Convenor
/profiles/311115
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The University reserves the right to make changes to the contents or methods of delivery of, or to discontinue, merge or combine modules, if such action is reasonably considered necessary by the University. If there are not sufficient student numbers to make a module viable, the University reserves the right to cancel such a module. If the University withdraws or discontinues a module, it will use its reasonable endeavours to provide a suitable alternative module.