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 | EXED547 | ||
Coordinator |
Prof IG McHale Operations and Supply Chain Management Ian.Mchale@liverpool.ac.uk |
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Year | CATS Level | Semester | CATS Value |
Session 2019-20 | Level 7 FHEQ | First Semester | 15 |
Pre-requisites before taking this module (other modules and/or general educational/academic requirements): |
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 |
15 |
15 15 |
45 | ||||
Timetable (if known) | |||||||
Private Study | 105 | ||||||
TOTAL HOURS | 150 |
Assessment |
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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 |
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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 |
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(LO1) Be able to critically evaluate the role of analytics and challenges of using analytics in an organisation. |
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(LO2) Critically evaluate the application of data analytics to a particular problem. |
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(LO3) Perform and evaluate the effectiveness of basic data processing, analytic and visualisation tasks. |
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(LO4) Assess and critically appraise the limitations of data analytics. |
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(S1) Problem Solver |
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(S2) An excellent communicator |
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(S3) Organised and able to work under pressure |
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(S4) Numerate |
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(S5) IT Literate |
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(S6) Ethically Aware |
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(S7) Lifelong Learner |
Teaching and Learning Strategies |
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Lectures are no longer than one hour blocks and will provide students with the opportunities to engage and discuss. Case studies- A range of different examples will allow students to analyse data and its analysis and 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, develop 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 |
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Key topics: Data in a Digital Age 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 |
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Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module. |