EC994: Applications of Data Science
Nathan Canen
Module Leader
Introduction
EC994-15 Applications of Data SciencePrincipal Aims
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, 鈥淐onsultation on Methodological Research Needs in UK Social Science鈥�, 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
Subject Knowledge and Understanding:...demonstrate awareness and understanding of key methods available for statistical learning and dimensionality reduction (Lasso, SVM, Networks, Bagging, Clustering).
Subject Knowledge and Understanding:...demonstrate an understanding of how these methods may be used to in different contexts.
Subject-specific skills/Professional Skills:...gain an understanding for and an ability to differentiate the appropriateness of different statistical methods.
Subject-specific skills/Professional Skills An ability to apply data science methods to every day challenges.
Syllabus
The syllabus may cover, but is not limited to, the following areas:
鈥� Data Science Use cases (e.g. in academia, business, public sector)
鈥� Linear Methods
鈥� Na茂ve Bayes
鈥� General Linear models
鈥� Model selection
鈥� Bootstrapping
鈥� Random trees, forests
鈥� Dimensionality reduction (Principal Component, Clustering)
鈥� Supervised learning methods
鈥� Unsupervised learning
鈥� Applications using statistical packages (such as R or others)
Context
- Optional Module
- L1P6 - Year 1, L1P7 - Year 1, G300 - Year 3, G300 - Year 4, G1PF - Year 1, C8P7 - Year 1, C803 - Year 1, C8P8 - Year 1
- Pre or Co-requisites
- Probability and statistics, including linear regression/OLS. Basic maths (Algebra, Analysis). Programming skills are helpful but not a prerequisite.
Assessment
- Assessment Method
- Centrally-timetabled examination (On-campus) (100%)
- Coursework Details
- Centrally-timetabled examination (On-campus) (100%)
- Exam Timing
- May