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 | Big Data Management | ||
Code | EBUS622 | ||
Coordinator |
Dr C Iris Operations and Supply Chain Management C.Iris@liverpool.ac.uk |
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Year | CATS Level | Semester | CATS Value |
Session 2023-24 | 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 |
20 |
5 |
25 | ||||
Timetable (if known) | |||||||
Private Study | 125 | ||||||
TOTAL HOURS | 150 |
Assessment |
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EXAM | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
Examination. There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. | 2 | 40 | ||||
CONTINUOUS | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
Individual report There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. | 0 | 60 |
Aims |
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To demonstrate in-depth understanding and knowledge of the concepts, theories and developments associated with the subject area, and critically and analytically discuss outcomes in a methodological, structured, logical and in-depth manner; To demonstrate ability to answer specific questions on the subject area fully, critically, analytically in suitable depth and at the appropriate level. |
Learning Outcomes |
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(LO1) Understanding what Big Data is and its relevance to Business. |
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(LO2) Recognise potential for use of Big Data analytics and output to Business areas, such as Marketing and Operations. |
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(LO3) Identify new business opportunities and business models for adopting Big Data initiatives, and challenges associated with their implementation. |
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(LO4) Understanding the legal and wider ethical issues involved in the gathering and use of personal information associated with Big Data, such as from Social Media applications and internet websites. |
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(LO5) Have some knowledge of systems and tools used for data mining and analysis. |
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(S1) Adaptability |
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(S2) Problem solving skills |
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(S3) Commercial awareness |
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(S4) Organisational skills, communications skills |
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(S5) IT skills |
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(S6) International awareness |
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(S7) Lifelong learning skills |
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(S8) Ethical awareness |
Teaching and Learning Strategies |
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2 hour lecture x 10 weeks Students will be expected to undertake independent research, guided reading and wider reading around the subject. |
Syllabus |
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Introduction to big data management; Big data analytics for competitive advantages; Application of big data to support managerial decision-making; Data management and organisational competencies to deploy big data; Big data innovation: Artificial Intelligence, Machine learning, etc.; Big data strategies for businesses. |
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. |