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University of Bath

MSc Economics for Business Intelligence and Systems 2020 entry compulsory units

View the compulsory units for the MSc Economics for Business Intelligence and Systems course if you're starting in September 2020.

Quantitative methods for economics and game theory

Title Description
Academic year 2020/2021
Unit code ES50152
Owning department Department of Economics
Credits 10
Notional study hours 200
Level FHEQ level 7
Period Semester 1
Assessment summary Assessed coursework 60% Written examination 40%

Aims

​This unit aims to enable students to present economic and business problems as mathematical problems and solve them using advanced quantitative techniques. It also aims to enable students to interpret intuitively mathematical solutions and develop policy and business recommendations.​​

Learning outcomes

At the end of the unit students should be able to:

  • Apply principles of economic-mathematical modelling to practical economic and business problems;
  • Evaluate economic and business problems by designing appropriate economic-mathematical models;
  • Solve, analytically or numerically, economic-mathematical models using advanced quantitative methods;
  • Develop analytically founded policy or business recommendations.

Construct programs using mathematical computing software in order to solve/simulate economic models capturing important real-world phenomena.​

Skills

Ability to identify and evaluate appropriate mathematical methods for economic modelling.

Ability to create logically rigorous arguments.

Ability to evaluate the suitability of economic models and methods for given real-world applications​.

Principles of programming and software engineering

Title Description
Academic year 2020/2021
Unit code CM50308
Owning department Department of Computer Science
Credits 10
Notional study hours 200
Level FHEQ level 7
Period Semester 1
Assessment summary Assessed coursework 100%

Aims

  • To introduce students to the development of computer software, including problem analysis, establishing requirements, designing, implementing and evaluating.
  • To provide students with the terminology and concepts of programming, irrespective of the language being used. To provide practical skills at reading and writing programs and producing programs to solve real world problems.
  • To make students feel confident about programming in the taught languages and about being able to learn different programming languages and programming paradigms.
  • To develop an understanding of how the principles behind software development are much more important than the chosen programming language, and how specification, design choices and development methodology may have a major impact on the correctness and suitability of the final software solution.
  • To develop a systemic understanding of software development paradigms for complex software system building.

Learning outcomes

At the end of this unit students should be able to:

  • Describe the design of a computer program separately from its implementation; Explain the basic concepts of procedural and object oriented programming in the design and implementation of computer programs;
  • Explain debugging and testing methods and how they contribute to robust code;
  • Design, construct and evaluate simple data structures and algorithms;
  • Plan, organise and implement program code to support reuse and maintainability of the software;
  • Demonstrate an understanding of the principles of software development paradigms and their relationship to the appropriateness of eventual software solutions.

Skills

Ability to use information technology

Ability to solve problems

Ability to think critically and communicate programs, code, and output effectively

Choosing appropriate design techniques​

Databases and business intelligence

Title Description
Academic year 2020/2021
Unit code MN50744
Owning department School of Management
Credits 10
Notional study hours 200
Level FHEQ level 7
Period Semester 1
Assessment summary Assessed coursework 50% Written examinations 50%

Aims

This unit introduces databases for the storage and retrieval of data as well as tabular and graphical methods of data presentation for deriving managerial insights.

Learning outcomes

​At the end of this unit, students will be able to:

  • ​Design a database that will best fit the needs of a business in terms of both flexibility and efficiency;
  • Use optimised queries to process data and derive managerial insights;
  • Visualise processed data in multiple dimensions to maximise its business value.

Skills

  • Intellectual skills: Identify the underlying data model of a business process; develop algorithmic thinking for rule extraction and exception detection; enhance perspective of knowledge discovery.
  • Practical skills: Present multi-dimensional data in tabular and graphical formats; apply business Intelligence software; simplify and convert data for analysis; apply state-of-the-art data mining software.
  • Transferable skills: Enhance perspective of data storage and retrieval; eliminate data duplication to achieve information consistency; improve assessment of the value of knowledge.​

Game theory, mechanism design, and experimental methods

Title Description
Academic year 2020/2021
Unit code ES50153
Owning department Department of Economics
Credits 10
Notional study hours 200
Level FHEQ level 7
Period Semester 2
Assessment summary Assessed coursework 60% Practical classes 40%

Aims

The aim of this unit is to provide students with knowledge and skills on how to apply mechanism design and ​experimental methods from economics and psychology to address practical economic, financial, and business problems.​​

Learning outcomes

At the end of this unit students should be able to:

  • Analyze markets and allocation problems with the tools of mechanism- and market design;
  • Outline and evaluate the main results in the literature of mechanism design;
  • Create solutions to real-life design problems by applying and adapting concepts and tools developed in the unit;
  • Detecting incentive problems in the presence of private information and constructing methods of eliciting private information in allocation and matching problems;
  • Design experiments to analyse properties of mechanisms in economic, financial, and business contexts;
  • Design and program experimental instructions using computer software;
  • Construct behavioural predictions drawing on economic theory, behavioural economics or finance, and psychology;
  • Empirically investigate experimental results and behaviour using econometric tools;
  • Evaluate findings using insights from economic and psychological research.​

Skills

Ability to apply theoretical, algorithmic, and empirical mechanism design techniques to practical problems

Ability to create logically rigorous arguments

Ability to analyse and synthesize information

Ability to plan, design, and execute programming tasks​

Financial derivatives

Title Description
Academic year 2020/2021
Unit code ES50154
Owning department Department of Economics
Credits 5
Notional study hours 100
Level FHEQ level 7
Period Semester 2
Assessment summary Assessed coursework 50% Written examinations 50%

Aims

To provide an introduction to derivative securities including futures and options, their valuation and management.​​

Learning outcomes

At the end of this course, students should be able to: structure and construct portfolios including a variety of financial instruments; apply principles of arbitrage, hedging and risk management; apply and evaluate the Black-Scholes pricing theory, and construct valuations based on the Black-Scholes analysis.​​

Skills

Written communication, Numeracy, Time management and Organisational skills, Data acquisition, handling and analysis, Problem solving, Working independently, Critical/analytical skills, Precise thinking, Accuracy and attention to detail.​​

Data mining, machine learning and econometrics

Title Description
Academic year 2020/2021
Unit code ES50155
Owning department Department of Economics
Credits 10
Notional study hours 200
Level FHEQ level 7
Period Semester 2
Assessment summary Assessed coursework 100%

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.

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.

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.

Programming applications

Title Description
Academic year 2020/2021
Unit code CM50309
Owning department Department of Computer Science
Credits 5
Notional study hours 100
Level FHEQ level 7
Period Semester 2
Assessment summary Assessed coursework 100%

Aims

  • ​To extend the notion of software development for more specific application areas. ​- To increase practical skills at reading and writing programs and producing programs to solve real world problems and to provide students with further domain specific problem solving techniques.
  • To provide students with further mechanisms to become independent learners who take control over their education rather than passively taking in information.

Learning outcomes

At the end of this unit students should be able to:

  1. ​Implement more advanced concepts of programming for specific areas of application.
  2. Design, construct, evaluate and analyse the efficiency of more complex data structures and algorithms.
  3. Appreciate the limits of computation.
  4. Recognize and judge the problems and responsibilities of system delivery, maintenance, administration and relationship to users.

Skills

Ability to use information technology

Ability to solve problems

Ability to think critically and communicate programs, code, and output effectively

Choosing appropriate design techniques​

Practice track

Title Description
Academic year 2020/2021
Unit code ES50156
Owning department Department of Economics
Credits 30
Notional study hours 600
Level FHEQ level 7
Period Dissertation
Assessment summary Assessed coursework 60% Oral examination 40%

Aims

The unit is intended to allow students to conduct research-based projects applying the skills, concepts, and techniques acquired in the taught programme to practice-based economic, business, and technical challenges, including: a case study involving a real-world challenge and a self-managed project.

As such, the unit allows students to relate economic, business, and technical challenges to the available academic literature, to define an associated and well-defined research question, research and review the relevant literature on the topic with the purpose of understanding and critiquing the current state of the research, and to develop a structured commentary leading to well-supported opinions and prescriptions.

First, the students will be presented with a current economic, business, or technical challenge for which they will conduct appropriate research, collect, clean, and analyse data and present a viable solution. The students will be required to run their project within a pre-determined timeframe. Thus, the unit not only encourages students to conduct research and address actual business/technical problems through an interdisciplinary quantitative lens – it also encourages reflection on issues such as project management and group dynamics.

The unit's objectives are designed for students to develop further their abilities to:

  • Collect, clean, and analyse data
  • Review literature
  • Make informed methodological choices
  • Present findings of their investigation and analyses
  • Present opinions and prescriptions.

Learning outcomes

By the end of the unit students should be able to:

  • Design and conduct a research project on practice based business issues
  • Evaluate and critically assess the particular challenges confronting actual businesses
  • Evaluate different sources of relevant data
  • Support their arguments with relevant empirical evidence
  • Interpret the significance of findings from relevant sources of quantitative and/ or qualitative evidence to inform decision making
  • Create viable alternatives and how they would contribute to business solutions and systems
  • Take initiative to act on perceived opportunities while considering various risk factors
  • Create a plan for an activity
  • Carry out a plan, acting on any deviations from the intended outcomes
  • Reflect critically on the outcomes of an activity and the processes that led to those outcomes
  • Present reflections in written and oral form Identify literature relevant to a chosen business/technology/consultancy issue, thereby appreciating the relationship between business/organisational practice and theory
  • Systematically research and review relevant literature
  • Develop an argument with references to appropriate theory and/or linking together arguments from disparate literatures or interdisciplinary perspectives

Skills

Intellectual skills

  • Ability to formulate a research question
  • Ability to select, analyse and present numerical or non-numerical data
  • Capability to handle complex data sets
  • Critical analysis and application of existing theories and concepts
  • Ability to synthesise interdisciplinary perspectives on the same problem
  • Ability to use academic literature alongside real life cases

Practical skills

  • Ability to engage with organisations
  • Ability to select and present material according to objectives and audience
  • Ability to produce work to agreed specifications and deadlines

Transferable skills

  • Time management
  • Communication skills
  • Presentation skills
  • Writing skills
  • Ability to work independently, without close supervision or guidance