Postgraduate coursework, Information School
INF6032 Big Data Analytics (2021-22)


1. Introduction

The assessment for “INF6032 Big Data Analytics” consists of a piece of individual coursework to assess your
ability 1) to understand the challenges and opportunities in dealing with Big Data including which situations are
appropriate for Big Data analytics and 2) to describe and select the most appropriate big data infrastructure
solutions.
You should write a 3,000 word structured report (see Section 3) that presents a discussion of given use cases
(see Section 2) and explains the use of big data analytics tools and techniques.
This assessment is worth 100% of the overall module mark for INF6032. A pass mark of 50 is required to pass the
module. Submission deadline: Monday 6th June 2022 via Turnitin. See Section 4 for more general information
about Coursework Submission Requirements within the Information School.

2. Use Cases

Discuss the following use cases (approx. 1000 words per use case), describing a) the type of data infrastructure you
would adopt and why (approx. 500 words per use case) and b) illustrate which languages (e.g., pyspark, SQL, Hive,
etc.) are most suitable to extract insights for each use case (approx. 500 words per use case). The use case
descriptions do not provide all the necessary details. You will need to describe your assumptions on the use cases in
the report to complement the information given to you for each use case. Describe the assumptions you are making
for these scenarios in terms of data availability and provide example queries of interest.

Use Case 1: COVID-19 Monitoring Platform. COVID-19 is affecting population world-wide. Online global data
is collected daily about the number of infections reported. A global health organisation wishes to develop an early
warning system which could continuously monitor the wide geographical areas (say a combination of several cities
in a country or whole country) and predict the next wide geographical area where the infection will be likely to
spread.

Use Case 2: Smart Repairing System: Modern cars contain a massive number of sensors, which communicate
with a centralised controlling device. These controlling devices are remotely controlled through the base stations of
car manufacturers, where the team can receive information from these units and can remotely find the faults in the
car. However, this system is not standardised and is very expensive. Lawmakers want to see an online autonomous
system implemented, whereby faults are automatically reported to nearby garages, which then can contact the
driver for support.

3. Report structure

You are required to produce a structured report that includes all the sections detailed in Table 1. Overall, 90 marks
will be awarded based on the content of your report. In addition, 10 marks will be awarded based on the
presentation of the report and how well you communicate your findings. You must state the word count on the first
page of the report. As there is a word count limit you should aim to make your writing as concise and informative
as possible. Note also that your work will be assessed taking into account the word limit; therefore, we are not
expecting detailed multiple analyses in the report; rather the emphasis should be on the clarity, accuracy and quality
in communicating your findings.

Table 1: Required content of the structured report.

Section Description Maximum
allocated
marks
Learning Objective
Structured
abstract
This should provide a summary of
your report in a structured manner.
This is not included in the word count.
Required,
but 0 marks

Table of
contents
This should include section titles and
page numbers. This is not included in
the word count.
Required,
but 0 marks

Introduction
(about 500
This section should briefly describe the
area of big data analytics and motivate
10 marks
words) the need for distributed system
solutions with practical examples on
why these solutions are needed.
Use cases
(about 2000
words long)
This section should contain two sub-
sections discussing the two use cases
described in this document. Use about
1000 words for each use case to reach
an overall length of 2000 words for
this section (this is not a strict word
count limit). Each use case discussion
should cover the following aspects:
1. You should discuss which
data infrastructure solution is
most appropriate for each use
case and why. You need to
explain your assumptions on
the use case (what are the data
specifications, what are the
main challenges, and what
you imagine the users need in
terms of analytics). Based on
these assumptions, briefly
describe the data
infrastructure which is most
appropriate for the use case
and your own interpretation
of it. Describe which systems
you would use and how these
would meet your assumed
requirements. Justify your
choices using supporting
evidence from academic
literature. This part should be
approx. 500 words.
2. You should provide a few
example queries for each use
case to illustrate which
insights could be extracted
using big data analysis
techniques. Illustrate which
language or tools are most
suitable and provide example,
e.g., in the form of source
code. Justify your choices
using supporting evidence
from academic literature. This
part should be approx. 500
words.
60 marks Understand the challenges and
opportunities in dealing with Big
Data including which situations
are more or less appropriate for
Big Data analytics.
Have an understanding of the on-
going development of Big Data
infrastructure solutions for
Volume, Variety, and Velocity
including industry-driven and
open-source solutions.
Be able to select the most
appropriate solution and design a
Big Data infrastructure solution
given a use case where to deploy
Big Data solutions.
Reflections
and lessons
learned
(about 250
words)
In this section, you should focus on
your engagement with the module.
You should discuss your contributions
(e.g., during the practicals) and lessons
learned during the weekly activities.
10 marks
Discussion
and
conclusions
(about 250
words long)
In this section, you should summarise
and discuss your report and outline
lessons learned.
10 marks
Appendix Optional,
and 0 marks





4. Information School Coursework Submission Requirements

It is the student's responsibility to ensure no aspect of their work is plagiarised or the result of other unfair means. The
University’s and Information School’s Advice on unfair means can be found in your Student Handbook, available via
http://www.sheffield.ac.uk/is/current

Your assignment has a word count limit. A deduction of 3 marks will be applied for coursework that is 10% or more
above or below the word count as specified above or that does not state the word count.

It is your responsibility to ensure your coursework is correctly submitted before the deadline. It is highly recommended
that you submit well before the deadline. Coursework submitted after 10am on the stated submission date will result in
a deduction of 5% of the mark awarded for each working day after the submission date/time up to a maximum of 5
working days, where ‘working day’ includes Monday to Friday (excluding public holidays) and runs from 10am to
10am. Coursework submitted after the maximum period will receive zero marks.

Work submitted electronically, including through Turnitin, should be reviewed to ensure it appears as you intended.
Before the submission deadline, you can submit coursework to Turnitin numerous times. Each submission will
overwrite the previous submission. Only your most recent submission will be assessed. However, after the submission
deadline, the coursework can only be submitted once.

Details about the submission of work via Turnitin can be found at http://youtu.be/C_wO9vHHheo

If you encounter any problems during the electronic submission of your coursework, you should immediately contact
the module coordinator and one of the Information School Teaching Support Team is-teaching- support@shef.ac.uk.
This does not negate your responsibilities to submit your coursework on time and correctly.