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
Year CATS Level Semester CATS Value
Session 2022-23 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

  12

6

36
Timetable (if known) 60 mins X 1 totaling 12
 
    60 mins X 1 totaling 6
 
  60 mins X 1 totaling 6
 
 
Private Study 114
TOTAL HOURS 150

Assessment

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    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    40       

Aims

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

(LO1) Students will be able to understand what Business Analytics is and to assess its relevance to business environments.

(LO2) Students will be able to appreciate the wider issues around the use, collection, and display of data.

(LO3) Students will be able to evaluate the potential of Business Analytics tools to support decision-making in businesses.

(LO4) Students will be able to understand the application of analytical business models and evaluate challenges associated with their implementation.

(LO5) Students will be able to interpret the solutions yielded by Business Analytics tools with relevance to practical applications.

(S1) Adaptability

(S2) Problem Solving Skills

(S3) Commercial Awareness

(S4) Organisational Skills

(S5) Communication Skills

(S6) IT Skills

(S7) International Awareness

(S8) Lifelong Learning Skills

(S9) Ethical Awareness


Teaching and Learning Strategies

Teaching Method 1: Lecture
Scheduled Directed Student Hours: 12
Unscheduled Directed Student Hours: 12
Attendance Recorded: Yes

Teaching Method 2: Computer Lab
Scheduled Directed Student Hours: 6
Attendance Recorded: Yes

Teaching Method 3: Group Study
Description: Student-led sessions in which each group is assigned a journal paper, one that is relevant to the group assignment, to review and discuss.
Scheduled Directed Student Hours: 6
Attendance Recorded: No

Self-Directed Learning Hours: 114
Description: Students will spend approximately 75% of their self-directed learning time on activity associated with lectures, and 25% on activity associated with seminars. Students will research library resources for their lectures, and access on-line resources and training materials for seminars.

Skills and Attribute Mapping

Skill 1: Adaptability
How is it developed: Students will develop their adaptability through case stu dy work and assignments in order to understand the application of quantitative techniques to business environments.
How is it assessed (if applicable): Report

Skill 2: Problem Solving Skills
How is it developed: Students will develop their problem-solving skills through practical exercises and through undertaking assignments.
How is it assessed (if applicable): Report and Examination

Skill 3: Commercial Awareness
How is it developed: Students will develop knowledge of commercial contexts associated with data and information technologies.
How is it assessed (if applicable): Report and Examination

Skill 4: Organisational Skills
How is it developed: Students will develop time management skills to meet deadlines for class discussion tasks and assignments.
How is it assessed (if applicable): Report and Examination

Skill 5: Communication Skills
How is it developed: Students will develop communication skills by engaging with case studies , report writing and working in groups.
How is it assessed (if applicable): Report and Examination

Skill 6: IT Skills
How is it developed: IT skills will be developed during practical lab sessions.
How is it assessed (if applicable): Report

Skill 7: International Awareness
How is it developed: Students will develop their international awareness through case study work associated with business and technologies in an international context.
How is it assessed (if applicable): Examination

Skill 8: Lifelong Learning Skills
How is it developed: Students will develop their lifelong-learning skills through preparation for their assessments and self-directed study of cases in preparation for class discussions.
How is it assessed (if applicable): Report

Skill 9: Ethical Awareness
How is it developed: Students will develop their awareness of ethical issues through research and preparation for assessment
How is it assessed (if applicable) : Examination


Syllabus

 

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
2. Descriptive Analytics (visualisation, exploring data, descriptive measures, and sampling)
3. Predictive Analytics (regression analysis and forecasting techniques, data mining and simulation)
4. Prescriptive Analytics (optimisation modelling)

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

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