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ES50155: Data mining, machine learning and econometrics

[Page last updated: 02 August 2022]

Academic Year: 2022/23
Owning Department/School: Department of Economics
Credits: 10 [equivalent to 20 CATS credits]
Notional Study Hours: 200
Level: Masters UG & PG (FHEQ level 7)
Semester 2
Assessment Summary: CW 100%
Assessment Detail:
  • Coursework 1 (CW 50%)
  • Coursework 2 (CW 50%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Learning Outcomes: At the end of the unit students should be able to:

* Choose appropriate algorithms to detect previously unknown rules and patterns within data and infer their business implications;
* Create econometric models appropriate for the problems studied;
* Estimate and evaluate econometric models, and interpret and critically evaluate the results;
* Apply mathematical computing and econometric software;
* Create custom code either in econometric software or a suitable programming language;
* Apply key concepts, methods, and tools of machine learning;
* Evaluate the applicability of machine learning methods for empirical economic, business and policy analysis.

Aims: This unit aims to provide students with econometric methods and knowledge of mathematical computing and econometric software necessary to conduct empirical analysis over a range of economic, business and financial problems. It also introduces students to contemporary statistical and algorithmic methods for cleaning, processing and extracting hidden information and knowledge out of raw data. Finally, it also covers topics on the intersection of data mining, machine learning, and econometrics and introduces students to machine learning methods used for empirical economic analysis. There will be particular emphasis on the use of machine learning methods for estimating causal effects.

Ability to think algorithmically to extract rules and detect exceptions.

Ability to apply analytical and numerical techniques

Ability to gather and synthesize information

Ability to use state-of-the-art data mining, econometric and machine learning software.

Ability to assess the value of data, information, and knowledge.

Content: Data mining methods: exploratory data analysis, naïve Bayes model, clustering methods

Machine learning methods: decision trees, support vector machines, neural networks, LASSO

Estimation methods:
* Linear and generalized linear regression models
* General method of moments
* Instrumental variables

Panel data models

Time series models
Hypothesis testing and inference

Mathematical computing and econometrics software


Policy evaluation

Counterfactual prediction.

Programme availability:

ES50155 is a Designated Essential Unit on the following programmes:

Department of Economics
  • THES-AFM30 : MSc Economics for Business Intelligence and Systems


  • This unit catalogue is applicable for the 2022/23 academic year only. Students continuing their studies into 2023/24 and beyond should not assume that this unit will be available in future years in the format displayed here for 2022/23.
  • Programmes and units are subject to change in accordance with normal University procedures.
  • Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
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