Big data is transforming almost every aspect of science and the humanities, driven by the emergence of a data society. This is a society in which increasingly comprehensive aspects of human behaviour and the economy are recorded as data. Employers are recognizing the need for a skilled workforce that can extract value from data, giving rise to the new job description of a data scientist. This course aims to provide economists and social scientist with a solid basis to overcome the deep technical deficit that has been identified among social scientists in the methodologies and practical tools of data science (Rebekah Luff, Rose Wiles and Patrick Sturgis, “Consultation on Methodological Research Needs in UK Social Science”, National Centre for Research Methods, March 2015.)
The aim of this module is provide students with a thorough understanding of the most common statistical methods related to high-dimensional data and machine learning techniques, with a particular focus to applications on economic and social data. The course will cover both the theory underpinning these methods and will also feature an intensive applied computing component.
Principal Learning Outcomes
The module should provide students with a theoretical understanding of the range of data science methods; critically evaluate the appropriateness of statistical methods and an ability to apply these methods to social and economic data.
The syllabus may cover, but is not limited to, the following areas:
• Data Science Use cases (e.g. in academia, business, public sector)
• Linear Methods
• Naïve Bayes
• General Linear models
• Model selection
• Random trees, forests
• Dimensionality reduction (Principal Component, Clustering)
• Supervised learning methods
• Unsupervised learning
• Applications using statistical packages (such as R or others)
- Optional Module
- L1P6 - Year 1, L1P7 - Year 1
- Pre or Co-requisites
- Probability and statistics as well as basic econometrics and maths (Algebra, Analysis). Programming skills are helpful but not a prerequisite.
- Assessment Method
- 2-hour exam (100%)
- Exam Timing
Time Allowed: 2 Hours
Answer ALL FOUR questions in Section A (10 marks each) and TWO questions from FOUR in Section B (30 marks each).
Approved pocket calculators are allowed.
Read carefully the instructions on the answer book provided and make sure that the particulars required are entered on each answer book. If you answer more questions than are required and do not indicate which answers should be ignored, we will mark the requisite number of answers in the order in which they appear in the answer book(s): answers beyond that number will not be considered.
Previous exam papers can be found in the University’s past papers archive. Please note that previous exam papers may not have operated under the same exam rubric or assessment weightings as those for the current academic year. The content of past papers may also be different.