MM281-Business Data Analytics

Module Provider: Business Informatics, Systems and Accounting
Number of credits: 20 [10 ECTS credits]
Level:5
Terms in which taught: Autumn term module
Pre-requisites: MM1F28 Business in Practice: Data analytics
Non-modular pre-requisites: For Data Analytics pathway students, this module should be taken together with MM282
Co-requisites:
Modules excluded:
Current from: 2023/4

Module Convenor: Dr Giannis Haralabopoulos
Email: i.haralabopoulos@henley.ac.uk

Type of module:

Summary module description:

Data-driven processes are becoming increasingly popular amongst organisations; quickly replacing qualitative assessments that were until recently based on experience and tacit knowledge. Machine learning is widely used in industry and business applications to provide recommendations, make predictions or extract knowledge. A good understanding of machine learning has, therefore, become a fundamental skill for anyone looking to work with organisation that plan to make strategic use of their data. In this module students will be introduced to key concepts related to machine learning and will become adept at managing and analysing data. Furthermore, students will gain experience with building predictive models that can lead to data-driven solutions. The workshops will provide students with the opportunity to develop programming skills using a state-of-the-art programming language (e.g., Python) and tools (e.g. Keras, Scikit, NLTK). 

Aims:

Ultimately, the aim of this course is to provide students with the skills and knowledge required to manage and analyse data, to develop predictive models that lead to data-driven business solutions. 

To satisfy this general aim, students will acquire key knowledge and skills in:

• Machine Learning Theoretical Concepts


• Accessing, storing, and handling univariate and multivariate data


• Machine Learning Applications


• Natural Language Processing


• Image Processing and Analytics


• Machine Learning Classification and Prediction

Assessable learning outcomes:

On completion of this module, the student should be able to:

• Select and use appropriate ML methods to analyse data


• Demonstrate effective use of ML applications


• Create and deploy interactive data-driven apps to demonstrate results of ML models


• Critically analyse a problem domain and apply the data analytics approach to support data-driven decision making 

Additional outcomes:

The student should:

• Become familiar with the industry standard Machine Learning tools


• Become familiar with software development tools and approaches surrounding data analytics


• Become adept at developing scripts and interactive data-driven apps using an industry-standard programming language (Python)

Outline content:

1. Basics


2. Perceptron and Features


3. Margin Maximization and Regression


4. Neural Networks and Convolutional Neural Networks


5. State Machines and Markov Decision Process


6. Reinforcement Learning and Recurrent Neural Networks


7. Recommender Systems, Decision Trees and Nearest Neighbours


8. ML Applications I 


9. ML Applications II


10. ML Application III

Brief description of teaching and learning methods:

This module will be a combination of lectures and practical workshops, within each two-hour session, that will enable students to acquire key concepts and practical skills in data analytics. It assumes no prior knowledge or experience in statistics except that taught in MM1F28. Data sets related to business problems will be provided as ‘case studies’ to individual students, who will then have to apply everything they learned to form data-driven recommendations. 

Summative assessment- Examinations:

None

Summative assessment- Coursework and in-class tests:

A three hour test, comprising Theory, Design and Technical Proficiency, which is submitted online in week 11 of the autumn term.

Formative assessment methods:

Students will be given feedback on the progress of their individual project through practical sessions.

Penalties for late submission:

The Support Centres will apply the following penalties for work submitted late:  

• 
  

  where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;  

  
  


• 
  

  where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded. 

  
  

The University policy statement on penalties for late submission can be found at: https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmission.pdf 

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.  

Assessment requirements for a pass:

40% in coursework 

 

To pass the module, students must demonstrate satisfactory understanding of concepts as well as demonstration of basic use of tools for data analytics and interpretation of results. 

 

For Merit level performance, students must demonstrate competence in formulating business data analytics solutions through suitable application of tools and critical appreciation of results. 

 

For Distinction level performance, students must demonstrate a high level of competence in critical formulation of business data analytics solutions and a highly competent application of appropriate tools and critical analysis when interpreting the results. 

Reassessment arrangements:

Resubmission of coursework during August resit period.

Additional Costs (specified where applicable):

Last updated: 12 April 2023