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 AND BIG DATA MANAGEMENT
Code EBUS305
Coordinator Dr Y Zhan
Marketing and Operations
Yuanzhu.Zhan@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2018-19 Level 6 FHEQ First Semester 15

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):
ULMS151 n/a 

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:
Programme: NX00 Year 3 Programme: NX01 Year 4 75% Honours Select Business pathway Year 3

Teaching Schedule
  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 24
Two hour weekly lectures
9
3 x 3 hour seminars or demonstrations
        33
Timetable (if known)   Through instructor-led seminars or demonstrations, students will review business analytics solutions and applications. Seminars or demonstrations may include presentations by industry participants,
  		         
Private Study 117
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Unseen Written Exam  120  Semester one  60  Yes  Standard UoL penalty applies  Examination Notes (applying to all assessments) Coursework - a report with a maximum of 2000 words. Examination - a closed book examination, typically involving four equally weighted examination questions, three of which must be answered. The examination is aimed at assessing the student's overall progress and their ability to collate and synthesise the subject matter in a critical and analytical manner. The students should be able to express the often complex interactions associated with Business Analytics and Big data in an academically rigourous manner, with the ability to clearly expain the challenges and intricacies associated with the acquisition, management and processing of data, in contemporary business and organisational scenarios. 
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Coursework  2000 words (maximum)  Semester one  40  Yes  Standard UoL penalty applies  Coursework 

Aims

Upon completion of this module, students will be able to:

  1. Demonstrate understanding what Big Data is and to assess its relevance to business environments;
  2. Evaluate potential for use of Big Data analytics and its potential to give positive insights into decision-making and business intelligence;
  3. Understand potential for new business opportunities and business models for adopting Big Data initiatives, and evaluate challenges associated with their implementation.
    4. < li>
    Appreciate the legal and wider ethical issues involved in the gathering of personal information (e.g. from social media applications and internet websites), and understand the wider ethical, security and privacy considerations associated with general data protection regulations (GDPR), as well as cybercrime, hacking-data breaches, etc.
  4. Understand types of solutions and applications used for data mining and data analysis.

Learning Outcomes

Students will be able to understand what Big Data is and to assess its relevance to business environments.

Students will be able to evaluate potential for use of Big Data analytics and analyse its output to business areas, such as Marketing and Operations.

Students will be able to apply new business opportunities and business models for adopting Big Data initiatives, and evaluate challenges associated with their implementation.

Students will be able to understand the legal and wider ethical issues involved in the gathering and evaluate the use of personal information associated with Big Data, such as from social media applications and internet websites. In particular, understand ethical, security and privacy considerations in terms of fast developing international trends, such as: cybercrime, hacking-data breaches, general data protection regulations (GDPR), social media expansion, etc.

Students will be able to apply some knowledge of systems and tools used for data mining and analysis.

Teaching and Learning Strategies

Lecture - Two hour weekly lectures

Seminar - 3 x 3 hour seminars or demonstrations

Through instructor-led seminars or demonstrations, students will review business analytics solutions and applications.

Seminars or demonstrations may include presentations by industry participants, usually from related systems applications or software development companies or organisations engaged in business analytics projects or developments.


Syllabus
1 

Introduction to Big Data: What Big Data Means for Business;

Big Data Strategies based on New Digital Connections-The Internet of Things. ''Social Media'' applications and marketing. [Sharing and exchanging information and ideas in virtual communities and networks. Creating and managing user-created content];

Big Data Innovation. M2M-machine to machine, ''RFID enabled systems and operations. [RFID in commerce, distribution, organisations (NHS), industry, logistics, etc];

Big Data Strategies for Business and IT. ''Big data ERP Cloud Enterprise Systems'' [Advances in Enterprise Resources Planning systems using ''Cloud'' technologies and applications.];

Key Trends in Big Data Technologies, In-Memory Computing, ''Cloud Computing'' architectures and models. [On-demand models, including SaaS, Paas and Iaas]. ''Open Source'' Systems. [Universal access and Distribution: benefits and limitations];

Big Data Analytics, - Statistical Processing - Artificial Intelligence, ''Big Data'' and Data Mining Applications. [‘Big Data’: systems, arc hitectures, technologies, markets, and research. Focus on using ''Big Data'' in business, commerce, private organisations and public organisations (e.g. United States Federal Government)];

Infonomics: The New Economics of Information. Knowledge driven competitive advantage;

Privacy, Security and Ethical Risks of Big Data. [Clarity on Practices: knowing when/how you’re being tracked, Simplicity of Settings: setting levels of privacy and Using and adhering to Privacy Guidelines].

Recommended Texts
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.
Explanation of Reading List:

Big Data And Business Analytics [electronic book] Jay Liebowitz, Published by Boca Raton: CRC Press, 2013, ISBN: 9781466565791 (ebk) - recommended text.

Students may supplement the recommended text reading with current journal articles, industry white papers and/or related contemporary publications available from the University''s library resources.

Students who have an interest in any of the specific areas around Business Analytics and Big Data will find a large amount of related reading materials as an optional resource.