Postgraduate coursework
DATA7201 Data Analytics at Scale (2023)
Project Report – Report on Dataset Analytics (Coursework)
1. Introduction
This assessment for “DATA7201 Data Analytics at Scale” consists of a piece of individual coursework. Given a dataset
(see Section 2), you should use big data analytics techniques to explore the data and to draw some conclusions that
inform decision makers. You will also need to select the most appropriate techniques and justify your choices using
supporting evidence from academic literature.
You should write a 1,500 word structured report (see Section 3) that describes the approach you have taken to
analyse the chosen dataset using big data analytics techniques. The report should focus on summarising your
approach on the chosen dataset and presenting your main findings. You should pay particular attention on clearly
communicating the results of your analysis and on helping the reader interpret your findings. Charts, tables, and
appendices are not included in the word count.
This assessment is worth 40% of the overall course mark for DATA7201. Submission deadline: 4pm Friday 19th May
2023 (Week 12) via Turnitin.
2. Given dataset: Facebook Ad Library API
The dataset to be used in this assessment is a collection of sponsored political posts on Facebook targeted at US users
during 23 months (03/2020-01/2022). This includes the period preceding the latest US Presidential election in
November 2020. A description of the data structure is available starting from:
https://www.facebook.com/ads/library/api/. The dataset covers 23-month worth of data collected from this API. The
format in which the data is provided by Facebook is JSON files. Each file is the result of a request for active ad
campaigns performed every 12 hours during the 23 months period, thus a lot of ad campaigns are duplicated across
files (i.e., if ad campaigns run for more than 12 hours) and should be properly managed during pre-processing. Given
the limited size of this dataset, it is expected that projects would analyse most of the available data. You can find the
data on the data7201 cluster HDFS under /data/ProjectDatasetFacebook.
You can integrate the dataset with external data if you want (e.g., with weather data via time information and
mentioned locations), although this is not mandatory. The emphasis of this coursework assignment is on how you
engage with big data analytics techniques, select appropriate big data analytics technologies, and on how well you
communicate your analysis and findings. You are allowed to use any other data analytics tool (e.g., for producing
visualisations or data summaries) as long as you also use, in some steps of your analysis (e.g., to pre-process the entire
dataset to select a relevant sample of the data), the cluster where the data lies (e.g., Pig, Python, SQL, etc.).
Examples of possible analysis include, but are not restricted to, the following:
• Look at ad volume over time for a certain topic.
• Focus on certain accounts (e.g., Facebook pages supporting a certain party and see which demographic
segments they target most).
• Look at URLs included in ads to understand which internet domains are most popular during the campaign.
• Look at a specific event or hashtag and look at who is talking about it.
• Look at spend per demographic group during the US Presidential election campaign.
• Look at the duration of ad campaigns over topics and political alignment.
You should investigate the dataset using tools on the DATA7201 cluster and write up your findings into a report also
providing the code/scripts/queries (if any) you used as an appendix. You will be evaluated according to the learning
objectives of the module as specified in the report structure (Section 3).
3. Report structure
You are required to produce a structured report that includes all the sections detailed in Table 1. You can structure
sub-sections as you prefer. 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 somewhere in 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 multiple detailed 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 This section should briefly describe the
general area of big data analytics and
motivate the need for distributed
system solutions with practical
examples on why these solutions are
needed.
15 marks 1. Solve challenges and
leverage opportunities in
dealing with Big Data
Dataset Analytics This section should provide a brief
description of the dataset used in your
report and the pre-processing steps
you took (e.g., focus on ads about a
certain topic). You should also list any
additional datasets you used (e.g.,
weather data), if any.
Describe all steps performed to analyse
the data and present the results of
your analysis. You can select in which
way to analyse your data (e.g., Pig,
Python, SQL, etc.) using the DATA7201
cluster, what specific dimensions to
look at, and what questions to
investigate. You should use at least one
of the tools available on the cluster
and you can use additional external
tools, If desired.
50 marks 3. Apply data analytics
infrastructures to best
support data science
practices for non-technical
stakeholders (e.g.,
executives).
5. Judge in which situations
Big Data analytics solutions
are more or less
appropriate.
6. Design the most
appropriate Big Data
infrastructure solution
given a use case where to
deploy Big Data solutions.
Discussion and
conclusions of the
analysis
In this section, you should summarise
and discuss the main findings of your
analysis and lessons learned. You
should state the main message the
reader should come away with from
your data analysis.
25 marks 3. Apply data analytics
infrastructures to best
support data science
practices for non-technical
stakeholders (e.g.,
executives).
Appendix Include the code/scripts/queries you
used as an appendix. The code quality
will not be assessed.
Optional, and 0
marks