SWAG合集

• Dr Jun Li

To introduce core Artificial Intelligence (AI) concepts, architectures, methods and tools. This will highlight the potential of AI for aiding innovation, enabling you to develop a practical knowledge of AI-enabled solutions development process for product and service innovation. Further this module will introduce you to machine learning for big data applications.

• AI Concepts
• Paradigms
• Intelligent Agents
• Intelligent Search
• Knowledge Representation
• Logic Programming
• Inference and Reasoning
• Planning
• Learning
• Reinforcement Learning
• Machine Learning
• Deep Learning (Neural Networks)
• AI Challenges (scalability, security, privacy, ethics)
• AI-driven Innovation in Products and Services – AI and ethics
• Machine Learning Theory & Methodology
• Machine Learning concepts and process
• Decision Tree Classifiers
• Support Vector Machines
• Bayesian Classification
• Clustering
• Linear regression
• Application case studies

Upon successful completion of this module, you should be able to:

1. Appraise the main concepts and paradigms of Artificial Intelligence and examine AI-enabled applications in different sectors.
2. Evaluate use cases of theoretical concepts through Artificial Intelligence thinking.
3. Evaluate key Artificial Intelligence architectures and relate them to use cases.
4. Evaluate the application of machine learning approaches to a wide set of regression and classification type problems.
5. Implement and deploy machine-learning techniques in data analysis systems.

• Professor Andrew Angus

Business analysts are frequently asked to gather, review and analyse business and industry data to produce robust, meaningful recommendations to senior managers. This requires the combination of a range of knowledge, skills and behaviours. For instance, the selection of quality data, building reliable databases and models, applying statistical models, deriving and communicating meaning from the findings.

This module aims to provide you with the ability to collect, process, analyse and present relevant data that will support evidence-based decision making. In addition, the module will also provide a platform which will help you engage with internal or external 鈥渃lients鈥�, undertake a project and, consequently, be able to make coherent and compelling recommendations to senior managers. The module will further develop programming skills using the R software environment.

The principles of business analytics:

• Literature reviews
• Research Strategies
• Research designs
• Planning a management project and formulating management questions
• Data collection and cleaning
• Data analysis in R software environment
• Research ethics

The nature of quantitative analysis:

• Statistical analysis of data and probability theory
• Sampling
• Structured interviews and questionnaires
• Hypothesis testing
• Correlation and regression analysis

On successful completion of this module you should be able to:

1. Critically evaluate the theoretical principles that underpin a range of analytical techniques.
2. Appraise the usefulness of different data analytics methods for inferring meaning from business data.
3. Create and communicate robust recommendations based on evidence.
4. Practice a high ethical standard when undertaking projects involving business data analytics.

• Professor Ying Xie

Prescriptive analytics has the power to help businesses use data to determine the optimal course of action. Prescriptive analysis works with data collected or generated from a wide range of descriptive and predictive sources and creates algorithms to facilitate decision making. It accounts for existing conditions, constraints and the results of each possible decision, while also evaluating potential consequences in different scenarios. Prescriptive analytics is a valuable tool that informs decisions and strategies and can be used alongside subjective judgement to find the best possible solutions among various options.

The module aims to provide you with a comprehensive understanding of prescriptive analytics techniques and their application within a business context. It aims to equip you with both knowledge and transferable skills necessary for making data-driven decisions and generating optimal solutions to complex business problems. This process will be facilitated through the use of spreadsheet-based software packages and Python software. You will have an opportunity to develop your own prescriptive models and apply them to various business problems in areas such as marketing, finance, operations and supply chain management, and HR.

• Introduction to Prescriptive Analytics
• Optimisation techniques (Unconstrained and constrained optimisation)
• Linear, Goal and Non-Linear Programming Models (theory and practice in Excel and Python)
• Decision Trees
• Multi-criteria Decision Making
• Simulations: Monte-Carlo, discrete and continuous simulation
• Scenario modelling
• Prescriptive analytics in Marketing
• Prescriptive analytics in Finance
• Prescriptive analytics in Operations and Supply Chain Management
• Prescriptive analytics in HR

On successful completion of this module you should be able to:

1. Explain various techniques used in prescriptive analytics, including unconstrained and constrained optimisation, linear, nonlinear programming, decision trees and multi-criteria decision making.
2. Deploy simulation to gain insights into complex systems, evaluate potential outcomes and make informed decisions.
3. Critically evaluate the limitations, strengths, and weaknesses of a range of prescriptive analytical techniques.
4. Appraise the options and select the appropriate technique to solve a given problem.
5. Design mathematical models comprising a decision objective and associated constraints, and use these models to solve decision problems and interpret the results.

This module is distinctive because it will provide you with the opportunity to gain experience of quantitative tools and techniques to solve realistic business problems using Excel and Python software packages.

• Dr Lakshmy Subramanian

The usefulness of the outputs of data analytics is dependent on the quality of data used in the analysis. In a world awash with data, it is critical that analysts understand how to recognise and harness appropriate data. Further, data visualisation is a key method of communicating important outcomes to stakeholders.

This module is designed to provide you with the knowledge, skills and behaviours for acquiring data and creating datasets that are fit-for-purpose. Using data, you will learn to apply a range of data visualisation techniques, such as scenario building, data mining and descriptive statistics, which will enable you to communicate research findings effectively to key stakeholders. The module will also introduce the R software environment and give you experience of using R to produce descriptive outputs.

• Types of data and data sources
• Research ethics
• Data cleaning
• Principles of scenario analysis
• Descriptive statistics
• Data mining
• Introduction to R software environment

On successful completion of this module you should be able to:

1. Design a robust programme of systematic data collection, management and curation.
2. Employ robust processes of data collection, cleaning, transformation and validation.
3. Evaluate the appropriateness of different data analytics methods for descriptive purposes.
4. Effectively communicate findings from data analytic outputs.

• Dr Vineet Agarwal

This module is designed to provide you with the required skills for structuring predictive research projects including conceptualising research questions and managing data. It explores the use of different methods for making predictions about future outcomes, using historical data. It also explores the validity of these empirical models and the nature of the uncertainty inherent in them.

• Formulating research questions
• Managing predictive data
• Ethical considerations in data management
• Cross-sectional and time-series regressions
• Time-series analysis (stationarity, unit roots, and cointegration)
• Generalised Method of Moments
• Application of predictive analytics in the R software environment
• Cluster analysis
• Neural networks
• Text analysis

On successful completion of this module you should be able to:

1. Conceptualise and formulate research questions relating to predictive analytics.
2. Appraise the suitability of a range of predictive methods for use in different management problems.
3. Critically evaluate model assumptions and their impact on the validity and reliability of results and recommendations.
4. Judge the performance of predictive models against outcomes, drawing conclusions about overall reliability and the nature of the uncertainty inherent in them.

• Dr Irene Moulitsas

The Python programming language has become a key language for business analysts and software developers in both desktop and internet/cloud network-based environments.

This module aims to provide you with the necessary skills and knowledge to develop software solutions to problems in these fields using Python. The principle and advanced elements of Python, associated libraries/toolboxes, programming methodologies and good design principles are covered. Hands-on programming exercises culminating in the construction of a fully functional three-tier application form an essential part of the course.

• Python program structure and flow, data types, basic and advanced language constructs
• Functional and object-oriented methodologies
• Built-in and third-party libraries for software development
• Software design principles and practices
• Development environments and documentation tools

On successful completion of this module you should be able to:

1. Evaluate object oriented and functional programming methodologies when implementing solutions to problems.
2. Solve a range of problems using Python.
3. Formulate a solution to a given problem based on good software design principles and programming practices.
4. Employ class and functional based libraries and other tools to assist in the development of a solution to a problem.
5. Deploy a working knowledge of a programming language.