Description
Background
A web server has been setup at http: // comp20008-jh. eng. unimelb. edu. au: 9889/ main/
containing a number of media reports on Rugby games. As data scientists, we would like to
extract information from those reports and use that information to improve our understanding
of team performance.
Rugby scores
Understanding the rugby scoring system is important in order to be able to extract scores
from match reports. A rubgy score is listed as x-y where x and y are the number of points
obtained by each team. For example, the following are all valid scores:
10-8
16-0
4-12
Learning outcomes
The learning objectives of this assignment are:
To gain practical experience in written communication skills for data science projects.
To practice a selection of processing and exploratory analysis techniques through visualisation discussed in lectures and workshops.
To practice crawling and scraping data from the Internet.
To practice using widely used Python library for data processing and gain experience
using library functions which may be unfamiliar and which require consultation of additional documentation from resources on the Web.
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COMP20008 2020 SM2
Your tasks
You are to perform a small data science project including some data processing and analysis
using Python. Your responses to Tasks 1-5 must be contained in a single .py file. Specifically,
you have the following tasks:
Task 1 (2 marks)
Crawl the http: // comp20008-jh. eng. unimelb. edu. au: 9889/ main/ website to find a complete list of articles available.
Produce a csv file containing the URL and headline of each the articles your crawler has found.
The CSV file should have two column headings url and headline and be called task1.csv.
Note: You might want to start with a smaller website to test your crawling implementation
with this site (http: // comp20008-jh. eng. unimelb. edu. au: 9889/ sample/ ).
Task 2 (4 marks)
For each article found in Task 1,
a) extract the name of the first team mentioned in the article. You can find a list of team
names as part of the rugby.json file provided. We will assume the article is written
about that team (and only that team). (2 marks)
Note: Your implementation must make use of the list of teams in rugby.json. We
will run your program with a different rugby.json file and expect to find all the articles
that refer to the teams listed in the modified file. The file we use will follow the same
format, but may have different teams.
b) extract the largest match score identified in the article. You will need to use regular
expressions to accomplish this. We will assume this score relates to the first named
team in the article. (2 marks)
Produce a csv file containing the URL, headline, first team mentioned and largest complete
match score of each the articles your crawler has found. The csv file should have four column
headings url, headline, team and score and be called task2.csv.
Note: Some articles may not contain a team name and/or a match score. These articles can
be discarded.
Task 3 (1 mark)
For each article used in Task 2, identify the absolute value of the game difference. E.g. a
14-6 score and a 5-13 score both have a game difference of 8. The value is referred to as the
game difference
Produce a csv file containing the team name and average game difference for each team that
at least one article has been written about. The csv file should have two column headings
team and avg game difference and be called task3.csv.
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COMP20008 2020 SM2
Task 4 (2 marks)
Generate a suitable plot showing the five teams that articles are most frequently written
about and the number of times an article is written about that team.
Save this plot as a png file called task4.png
Task 5 (2 marks)
Generate a suitable plot comparing the number of articles written about each team with their
average game difference. Ignore any teams that have no articles written about them.
Save this plot as a png file called task5.png
Task 6 (14 marks)
Write a 3-4 page report to communicate the process and activities undertaken in the project,
the analysis, and some limitations. Specifically, the report should contain the following information:
A description of the crawling method and a brief summary the output for Task 1.
(2 marks)
A description of how you scraped data from each page, including any regular expressions
used for Task 2 and a brief summary of the output. (3 marks)
An analysis of the information shown in the two plots produced for Tasks 4 & 5, including a brief summary of the data used. The plots are to be shown (included) along
with your analysis. (4 marks)
A discussion of the appropriateness of associating the first named team in the article
with the first match score. (2 marks)
At least two suggested methods for how you could figure out from the contents of the
article whether the first named team won or lost the match being reported on and a
comment on the advantages and disadvantages of each approach. (2 marks)
A discussion of what other information could be extracted from the articles to better understand team performance and a brief suggestion for how this could be done.
(1 mark)
Submission instructions
Your responses to Tasks 1 – 5 must be contained in a single python script (.py) file. As the
output of this file will be verified automatically, it is essential that the program runs without
producing errors. For this assignment you may NOT install any additional packages that
aren’t present on the JupyterHub server, e.g. by using the pip install command. Doing so
will cause your submission to fail our marking scripts.
Submission is via the LMS. Two submission links will be provided, one for the .py file
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COMP20008 2020 SM2
containing your responses to Tasks 1 – 5 and a second for a .pdf or .docx file containing
your response to Task 6.
Extensions and late submission penalties
If requesting an extension due to illness, please submit a medical certificate to the lecturer.
If there are any other exceptional circumstances, please contact the lecturer with plenty of
notice. Late submissions without an approved extension will attract the following penalties
0 < hourslate <= 24 (2 marks deduction)
24 < hourslate <= 48 (4 marks deduction)
48 < hourslate <= 72: (6 marks deduction)
72 < hourslate <= 96: (8 marks deduction)
96 < hourslate <= 120: (10 marks deduction)
120 < hourslate <= 144: (12 marks deduction)
144 < hourslate: (25 marks deduction)
where hourslate is the elapsed time in hours (or fractions of hours).
This project is expected to require 30-35 hours work.
Academic honesty
You are expected to follow the academic honesty guidelines on the University website
https://academichonesty.unimelb.edu.au
Further information
A project discussion forum has also been created on the Ed forum. Please use this in the
first instance if you have questions, since it will allow discussion and responses to be seen by
everyone. There will also be a list of frequently asked questions on the project page.
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