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 | Business Analytics | ||
Code | EBUS205 | ||
Coordinator |
Professor T Bektas Operations and Supply Chain Management T.Bektas@liverpool.ac.uk |
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Year | CATS Level | Semester | CATS Value |
Session 2024-25 | Level 5 FHEQ | Second Semester | 15 |
Pre-requisites before taking this module (other modules and/or general educational/academic requirements): |
ECON154 BUSINESS STATISTICS |
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 |
12 |
6 |
6 12 |
36 | |||
Timetable (if known) | |||||||
Private Study | 114 | ||||||
TOTAL HOURS | 150 |
Assessment |
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EXAM | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
Assessment 2: Examination Assessment Type: Written Unseen Examination Duration: 2 hours Weighting: 60% Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL Penalty Applies Ano | 2 | 60 | ||||
CONTINUOUS | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
Assessment 1: Group Report Assessment Type: Coursework Size: 1250 words Weighting: 40% Reassessment Opportunity: Yes, students will submit an individual report. Penalty for Late Submission: Stand | 0 | 40 |
Aims |
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The module aims to provide the students with the key principles and concepts of Business Analytics to enable them to develop technical skills in being able to apply a range of quantitative techniques to problems arising in business environments, for gaining improved insight and to support making better decisions. |
Learning Outcomes |
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(LO1) Students will be able to understand what Business Analytics is and to assess its relevance to business environments. |
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(LO2) Students will be able to appreciate the wider issues around the use, collection, and display of data. |
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(LO3) Students will be able to evaluate the potential of Business Analytics tools to support decision-making in businesses. |
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(LO4) Students will be able to understand the application of analytical business models and evaluate challenges associated with their implementation. |
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(LO5) Students will be able to interpret the solutions yielded by Business Analytics tools with relevance to practical applications. |
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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 |
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(S5) Communication Skills |
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(S6) IT Skills |
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(S7) International Awareness |
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(S8) Lifelong Learning Skills |
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(S9) Ethical Awareness |
Teaching and Learning Strategies |
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Teaching Method 1: Lecture Teaching Method 2: Computer Lab Teaching Method 3: Group Study Self-Directed Learning Hours: 114 Skills and Attribute Mapping Skill 1: Adaptability Skill 2: Problem Solving Skills Skill 3: Commercial Awareness Skill 4: Organisational Skills Skill 5: Communication Skills Skill 6: IT Skills Skill 7: International Awareness Skill 8: Lifelong Learning Skills Skill 9: Ethical Awareness |
Syllabus |
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This module will develop the content introduced in ECON154 Business Statistics but also introduce new topics that are listed below. The module will also provide good background for EBUS306 Sustainable Supply Chain Management. 1. Big Data and models in Business Analytics Students are expected to consult a variety of resources as background and supplementary reading, including the relevant parts of the core and recommended texts, and state-of-the-art journal articles. These resources will be accessible via the library. Students will also be expected to identify their own resources by undertaking a literature search as part of the module assessment. The module will adopt a ‘hands-on’ appr oach where students will practise and apply, either through hand calculations, or by using computer software, the quantitative techniques that are introduced in the module. Excel and R software will be introduced to the students in the computer labs that form part of the module. These packages will be used for data visualisation, data exploration, statistical analysis, and data mining. |
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. |