MM292-Data Science For Business

Module Provider: Business Informatics, Systems and Accounting
Number of credits: 20 [10 ECTS credits]
Level:5
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2019/0

Module Convenor: Prof Keiichi Nakata

Email: k.nakata@henley.ac.uk

Type of module:

Summary module description:

This module focuses on managerial aspects of data driven decision making through data analytics. It integrates data analytical techniques with problem-solving approaches for business such as predictive modelling and visualizing model performance. The course strives to develop skills for applying data analytics models, such as for capturing and monitoring unstructured data patterns using examples from online business and e-commerce data. This includes application and discussion of data analytic thinking and data engineering. The module explores decision analytic thinking and data driven strategy using case studies and illustration of statistical theory and techniques. It covers data science fundamentals and business strategy.

This module is delivered in University of Reading Malaysia only.

Aims:

The aim of this module is to develop understanding and knowledge of data driven decision making and business strategy. Students will develop skills of designing and evaluating data science tasks and techniques. They will also gain knowledge of theory and fundamentals of data analytics within the field of data science. 

Assessable learning outcomes:

On completion of the module, students will be expected to be able to demonstrate:

1. Knowledge of data science for business ranging from designing analytic models to application of data driven business decisions. 

2. The ability to diagnose and implement data analytic models as well as avoid common pitfalls such as over-fitting.  

3. Acquire skills to data management, model selection, data analytic thinking and data engineering through appreciation of application using case studies.  

Additional outcomes:

The module will support the development of:

• Data visualisation skills and machine learning applications in solving business problems 

• Business presentation skills in relation to group coursework and presentation. 

Outline content:

The main topics of this module are:

 

1. Introduction to data science – fundamentals of data science, business problems and solutions.

2. Big data analytics – statistics and theory, operations and business strategy decisions.

3. Data mining – basic data management, classification, clusters, machine learning and big data analytic techniques. 

4. Model development, selection and interpretation - data design, engineering and analytic thinking.

5. Predictive models – analytic models, case studies and data driven strategy.

6. Visualisation – graphical models, network models and artificial intelligence. 

Global context:

The module contains case studies of global companies and/or uses secondary data with international perspective. 

Brief description of teaching and learning methods:

This module is designed to focus on applying data analytic skills using data science fundamentals in solving business problems. In addition to classroom teaching, this module includes:

- Use of personal computer lab (or personal laptop) with R program installed.

- A list of secondary datasets, case studies and recommended online help (R community) will be used in hands on practical class. 

- 5 X 2 hours lab sessions in the Spring term.

- Assessed work that will be used to develop students’ skills and knowledge.

- An electronic discussion board will be available for students enrolled in this module. 

Contact hours:

	Autumn	Spring	Summer
Lectures		10
Demonstration		5
Practicals classes and workshops		10
Supervised time in studio/workshop		10
Fieldwork		20
External visits		10
Guided independent study:
Wider reading (independent)		10
Wider reading (directed)		20
Advance preparation for classes		10
Other		10
Preparation for presentations		15
Preparation for performance		5
Group study tasks		20
Essay preparation		35
Reflection		10
Total hours by term	0	200	0
Total hours for module	200

Summative Assessment Methods:

Method	Percentage
Written assignment including essay	100

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

One assessed group coursework of technical report without word limit (formatted in accordance with the Henley Business School’s Assessed Work Rules). Submission in Spring term, week 11.

Formative assessment methods:

One optional non-assessed technical in-class exercise without word limit (formatted in accordance with the Henley Business School’s Assessed Work Rules).

Penalties for late submission:
The Module Convener 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[1] (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: http://www.reading.ac.uk/web/FILES/qualitysupport/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:

The pass mark for the module is 40%.

Reassessment arrangements:

Resubmission of written assignment.

Additional Costs (specified where applicable):

Resources and Reading list:

Provost, F. and Fawcett, T. 2013. Data Science for Business: What you need to know about data mining and data-analytic thinking. O’Reilly. 

Knaflic, C.N. 2015. Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley, London. 

Taddy, M. 2019. Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions. MGraw-Hill Education. 

McNicholas, P.D. and Tait, P.A. 2019. Data Science with Julia. Chapman and Hall/CRC. 

Last updated: 11 April 2019