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 Analytics for Business | ||
| Code | EBUS633 | ||
| Coordinator |
Dr KS Lam Marketing and Operations hugolam@liverpool.ac.uk |
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| Year | CATS Level | Semester | CATS Value |
| Session 2018-19 | Level 7 FHEQ | Second Semester | 15 |
Pre-requisites before taking this module (other modules and/or general educational/academic requirements): |
| None |
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: |
| MSc Big Data Management (BLMD) |
Programme(s) (including Year of Study) to which this module is available on an optional basis: |
| MSc Digital Business Enterprise Management (BLBE) MSc Operations & Supply Chain Management (BLOP) MSc Programme & Project Management (BLPM) |
Teaching Schedule |
| Lectures | Seminars | Tutorials | Lab Practicals | Fieldwork Placement | Other | TOTAL | |
| Study Hours |
12 6 x 2 hour lectures |
12 6 x 2 hour seminars |
24 | ||||
| Timetable (if known) |
Lectures will be presented by academic members of staff across relevant areas and also by industry practitioners in the area of Big Data Analytics, Management and Systems.
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Computer lab-based seminars will introduce Big Data Analytics tools, such as Apache Hadoop and Terracotta (SOFTWARE AG).
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| Private Study | 126 | ||||||
| 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 |
| Coursework | 3500 words | 2 | 100 | Yes | Standard UoL penalty applies | Assignment Report Notes (applying to all assessments) - none |
Aims |
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Learning Outcomes |
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Analyse the role of big data analytics in an organisation. |
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Identify tools and techniques for big data analytics. |
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Perform basic big data processing and visualising tasks. |
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Develop case studies on how big data science aids and hinders business intelligence. |
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Teaching and Learning Strategies |
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Lecture - 6 x 2 hour lectures Lectures will be presented by academic members of staff across relevant areas and also by industry practitioners in the area of Big Data Analytics, Management and Systems. |
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Seminar - 6 x 2 hour seminars Computer lab-based seminars will introduce Big Data Analytics tools, such as Apache Hadoop and Terracotta (SOFTWARE AG). |
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Syllabus |
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| 1 |
1. Big data analytics: an overview [Exploring the progression from big (data) science to business intelligence and the implications for research/practice] 2. Big data analytics: tools and technologies [Overview of Data analytics capabilities of techn ologies such as Hadoop Distributed File System, HBase, Amazon S3] 3. MapReduce [Introducing the functions, specifications and algorithm design] 4. Storing big data [Outline of data stores and redundant data] 5. Processing big data [Mapping, linking, transforming big data] 6. Clustering big data [Mean Shift, Hierarchical, K-Means] 7. Visualising big data [Management Consoles, dashboards and reporting tools] 8. Big data analytics: trends and projections [Discussing the opportunities and limitations of big (data) science] |
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. Explanation of Reading List: Available in module handbook on VITAL. |
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