程序代写案例-DATA7201
Postgraduate coursework
DATA7201 Data Analytics at Scale (2021)

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
communicating clearly 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 50% of the overall course mark for DATA7201. Submission deadline: 4pm Monday 24th May
2021 (Week 13) via Turnitin.

2. Given datasets: Twitter (1% API) or Facebook (Ad Library API)
The dataset to be used in this assessment is to be chosen among two possible options: A Twitter and a Facebook
dataset. You need to choose only one among these two options and perform the analysis on it.
The first option is a collection of tweets available through the free Twitter API which covers a 1% random sample of
the entire Twitter stream over 6 months (07/2014-12/2014). A description of the data structure is available starting
from: https://developer.twitter.com/en/docs and https://developer.twitter.com/en/docs/twitter-api/v1/data-
dictionary/overview. The dataset you should use covers 6-month worth of data collected from this API. The format in
which the data is provided by Twitter is compressed JSON files. There is no need to analyse the entire dataset and it is
instead acceptable to focus on a specific subset (e.g., based on a period of time around an event) for your analysis.
You can find the data on the data7201 cluster HDFS under /data/ProjectDatasetTwitter.
The second option is a collection of sponsored political posts on Facebook targeted at US users during 8 months
(03/2020-11/2020) preceding the US Presidential election in 2020. A description of the data structure is available
starting from: https://www.facebook.com/ads/library/api/. The dataset covers 8-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 8 months period, thus a lot of ad campaigns are duplicated
across files (i.e., if they 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 chosen 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 tweet volume over time for a certain hashtag.
• Focus on certain users (e.g., edu.au users and see which academics from which university are most active).
• Look at URLs included in tweets to understand which internet domains are most popular on Twitter.
• Look at a specific event or hashtag and look at who is tweeting about it.
• Look at sponsored content spend per demographic group during the US Presidential election in 2020.
• Look at the duration of ad campaigns over topics and political alignment during the US Presidential election in
2020.

You should investigate the chosen 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 tweets
containing a certain hashtag). 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



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