MM293-Business Analytics With R

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:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2019/0

Module Convenor: Dr Markos Kyritsis

Email: m.kyritsis@henley.ac.uk

Type of module:

Summary module description:

This module focuses on statistical modelling using R statistical program. It emphasizes computational modelling through algorithm and implementation in code rather than formal mathematics. This includes coding to develop model-based statistics relevant for today’s business problems. The module covers descriptive statistics, linear models and multivariate linear models by scripting statistical analyses. 



 



This module is delivered in University of Reading Malaysia only.


Aims:

The aim of this module is to develop data analytics skills through application of statistical modelling in the context of business decision making. Students will develop hands on experience using R statistical programming software for predictive modelling, and gain knowledge of implementing code. 


Assessable learning outcomes:

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



1. The fundamentals of statistical modelling for decision making in business. 



2. The model-based statistics and application for data driven decision making. 



3. Inferential statistics using frequentist approach and Bayesian methods. 


Additional outcomes:

The module will support the development of skills and knowledge related to:



• Business statistics and relevant data driven decision making models. 



• Effective use of visualisation tools for data exploration and modelling of business problems. 


Outline content:

The main topics covered in this module are:



1. Descriptive statistics 



2. Inferential statistics



3. Modelling simple linear models 



4. Modelling multivariate linear models



5. Introduction to inferential statistics using Bayesian methods 


Global context:

The secondary data sets of international organisations will be used as part of hands-on practical experience in data analysis. 


Brief description of teaching and learning methods:

This module is designed to be delivered using R statistical program in a computer laboratory. Students will have hands on experience of applying data analytics techniques to real big data and secondary data sources. 



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



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



- 5 X 2 hours lab sessions in the Autumn 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
    Preparation of practical report 10
    Completion of formative assessment tasks 5
    Group study tasks 20
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 Autumn 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 of the module is 40%.


    Reassessment arrangements:

    Resubmission of written assignment.


    Additional Costs (specified where applicable):

    Resources and Reading list:



    Crawley, M.J. 2014. Statistics: An Introduction Using R. Wiley, London. 



    Grolemund, G. and Wickham, H. 2016. R for Data Science. O’Reilly. 



    Crawley, M.J. 2007. The R Book. Wiley, London. 


    Last updated: 8 April 2019

    THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.

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