ES50155: Data mining, machine learning and econometrics
[Page last updated: 23 October 2023]
| Academic Year: | 2023/24 |
| Owning Department/School: | Department of Economics |
| Credits: | 10 [equivalent to 20 CATS credits] |
| Notional Study Hours: | 200 |
| Level: | Masters UG & PG (FHEQ level 7) |
| Period: |
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| Assessment Summary: | CW 100% |
| Assessment Detail: |
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| Supplementary Assessment: |
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| Requisites: | |
| 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. |
| Skills: | 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 Cross-validation Policy evaluation Counterfactual prediction. |
| Course availability: |
ES50155 is a Designated Essential Unit on the following courses:Department of Economics
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Notes:
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