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University of Liverpool EBUS633 Resit

 EBUS633 Big Data Analytics for Business

· 案例展示

Page 1 of 2
EBUS633 Resit Assignment Requirements

 

Module:EBUS633 Big Data Analytics for Business
Academic Year:2019/20
Submission date:10th August 2020 (12:00pm noon)
Submission
method:
This resit coursework requires online submission only. You do not need to
submit a hard copy of the coursework. You should submit your coursework
via Turnitin, which is a plagiarism and collusion detection system. If you do
not submit to Turnitin your work will not be marked.
Penalty for late
submission:
Standard UoL penalty applies
Word limit:3500 words
(This resit assignment consists of two parts. The word limits for parts 1
and 2 are 1500 words and 2000 words, respectively.)
Weighting:100%
(This assignment consists of two parts. The weightings for parts 1 and 2
are 40% and 60%, respectively.)
Requirements for
part 1:
In part 1, you need to select and discuss one analytic technique we learnt
from the lectures (i.e., classification, regression, clustering, association
rules). When describing your answers, include:
x Problem: A brief description of the problem that your selected
analytic technique can assist in solving.
x Input data: Comment in detail on the required input data, it can be
already available as presented in exhibits or your data can be
required. If latter describe how the data would be collected.
x Data output: What does the output of your selected analytic
technique consist of and how can it be utilised?
x Validation: Discuss how you would validate your technique or how
you will evaluate its performance.
x Discuss the limitations and foreseen challenges of your technique
as well as any assumptions that you have made.

Page 2 of 2

 

Requirements for
part 2:
In part 2, you need to provide a critical comparison of two specific tools or
techniques for big data analytics. You can choose two big data tools or
techniques taught in the seminars or based on your own private study.
The following information provides some guidelines about what should be
included in this part.
¾ Provide a brief introduction of the two big data tools or techniques.
¾ Explain, demonstrate, and document how to use the two big data
tools or techniques (e.g., installation, data processing/analysis, output
results, etc.). You should show your own (noW oWhers¶) sWeps of Xsing
the two big data tools or techniques. A direcW cop\ of oWhers¶ sWeps
(e.g., the steps shown in the seminar slides) without any modification
will be given zero mark for this part.
¾ Provide a critical comparison of the two big data tools or techniques,
especially regarding their corresponding strengths and weaknesses.
Important notes:¾ You need to provide relevant references to support your explanation
and arguments across your report. References can be from various
sources such as books, journal articles, and newspapers.
¾ The Harvard referencing style is currently used by the Management
School (http://libguides.liverpool.ac.uk/referencing/harvard). Make
sure your referencing style is correct and consistent.
¾ Your submitted report will be scanned by Turnitin, a plagiarism and
collusion detection system. Make sure you comply with the Academic
Integrity Policy adopted by the University of Liverpool
(https://www.liverpool.ac.uk/media/livacuk/tqsd/code-of-practice-on
assessment/appendix_L_cop_assess.pdf).
¾ This assignment requires online submission only, and you are
required to submitted your report once only (i.e., your first submission
is the final submission).
¾ Your report should be submitted as a single file, which includes both
parts 1 and 2.
¾ If you have any questions about this assignment, contact Dr Yuanzhu
Zhan (Yuanzhu.Zhan@liverpool.ac.uk) for part 1, and Dr Hugo Lam
(hugolam@liverpool.ac.uk) for part 2.

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