ULMS Electronic Module Catalogue
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Performance Management and Data Analytics
Code EXED549
Coordinator Prof IG McHale
Operations and Supply Chain Management
Ian.Mchale@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2020-21 Level 7 FHEQ Whole Session 15

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):
EXED522 Leadership 

Modules for which this module is a pre-requisite:
 

Programme(s) (including Year of Study) to which this module is available on a required basis:
 

Programme(s) (including Year of Study) to which this module is available on an optional basis:
 

Teaching Schedule
  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 7.5

         5

7.5

7.5

7.5

10

 45
Timetable (if known)              
Private Study 105
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
             
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Work with a dataset and research question to arrive at an appropriate way to visually represent the data analysis  -1000 words    40       
Individual Written Assignment  -1500 words    60       

Aims

Provide students with an understanding of the role of big data analysis in the current digital era.

To understand how big data can be used in the organisation’s decisions, such as marketing decisions based on analysis of customer behaviour or social media and strategic organisational decisions underpinned by analysing and forecasting trends.

To provide students with the key skills to mine data, store data, process data into a usable form, to analyse data and skills to identify how data and analytics can be used to support their decision making.

Enable students to articulate, visualise and explain the results of analytical analysis.

Learning Outcomes

(LO1) Be able to critically evaluate the role of analytics and challenges of using analytics in an organisation.

(LO2) Critically evaluate the application of data analytics to a particular problem.

(LO3) Perform and evaluate the effectiveness of basic data processing, analytic and visualisation tasks.

(LO4) Assess and critically appraise the limitations of data analytics.

(S1) Problem Solver

(S2) An excellent communicator

(S3) Organised and able to work under pressure

(S4) Numerate

(S5) IT Literate

(S6) Ethically Aware

(S7) Lifelong Learner

Teaching and Learning Strategies

Online (synchronous and asynchronous)

Case based learning (asynchronous online) x 7.5 hours
Case based learning (synchronous online) x 7.5 hours
Lecture (asynchronous online) x 7.5 hours
Lecture (synchronous either face to face or online) x 7.5 hours
Practical (asynchronous online) x 10 hours
Practical (synchronous either face to face or online) x 5 hours
Self-directed learning x 105 hours

Lectures are typically one to one and a half hour blocks and will provide students with the opportunities to engage and discuss.

A range of different examples will allow students to explore and analyse data and its use in different business situations.

Students will be introduced to key software packages which they will use in a range of tasks to develop key data analysis skills.

Students will be expected to complete schedule directed module pre-reading, to prepare for some exercises, tasks and assessments during the module and to research, dev elop and prepare a final assessment. In addition to the suggested readings, students might be expected to develop their own analytic skills and to work with data outside of the sessions, or to read and to prepare themselves for the final assessment.

Syllabus
 

Key topics:

Data in a Digital Age
Big data and analytics: an overview of current practices
Mining Data: Key Skills
Storing and processing big data: Amazon Web Server, MongoDB, Hadoop
Data analytics software: R/RStudio, Python Visualising data
Techniques for data analytics: machine learning (classification and clustering), regression
Using data to analyse customer behaviour
Using big data analysis in your organisation’s decision-making
Social Media Analysis
Effective Data visualisation and telling stories to others

Materials will be made available to students via VITAL and students will also be expected to read additional materials from the suggested and required reading list using online library resources

Recommended Texts
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.