EC994: Applications of Data Science

Nathan Canen
Module Leader

15 CATS - Department of Economics

Summer Module

Spring Module

Principal Aims

EC994-15 Applications of Data Science

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). The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars with applied modules, independent study. The summative assessment methods that measure the achievement of this learning outcome are: Examination.

Subject Knowledge and Understanding:...demonstrate an understanding of how these methods may be used to in different contexts. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars with applied modules, independent study. The summative assessment methods that measure the achievement of this learning outcome are: Examination.

Subject-specific skills/Professional Skills:...gain an understanding for and an ability to differentiate the appropriateness of different statistical methods. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars with applied modules, independent study. The summative assessment methods that measure the achievement of this learning outcome are: Examination.

Subject-specific skills/Professional Skills An ability to apply data science methods to every day challenges. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars with applied modules, independent study. The summative assessment methods that measure the achievement of this learning outcome are: Examination.

Syllabus

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

• Bootstrapping

• Random trees, forests

• Dimensionality reduction (Principal Component, Clustering)

• Supervised learning methods

• Unsupervised learning

• Applications using statistical packages (such as R or others)

Context

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

Assessment Method: In-person Examination (100%)
Coursework Details: In-person Examination (100%)
Exam Timing: May

Exam Rubric

Time Allowed: 2 Hours

Read all instructions carefully - and read through the entire paper at least once before you start entering your answers.

There are TWO sections in this paper. Answer ALL FOUR questions in Section A (10 marks each) and TWO questions in Section B (30 marks each).

Answer each whole question in a separate booklet.

Approved scientific (non-graphical) pocket calculators are allowed.

You should not submit answers to more than the required number of questions. If you do, we will mark the questions in the order that they appear, up to the required number of questions in each section.

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.