Description
Problem statement
This project concerns the well-known Fisher’s Iris data set [3]. You must research the data set
and write documentation and code (in Python [1]) to investigate it. An online search for
information on the data set will convince you that many people have investigated it
previously. You are expected to be able to break this project into several smaller tasks that
are easier to solve, and to plug these together after they have been completed.
You might do that for this project as follows:
1. Research the data set online and write a summary about it in your README.
2. Download the data set and add it to your repository.
3. Write a program called analysis.py that:
• outputs a summary of each variable to a single text file,
• saves a histogram of each variable to png files, and
• outputs a scatter plot of each pair of variables.
It might help to suppose that your manager has asked you to investigate the data set, with a
view to explaining it to your colleagues. Imagine that you are to give a presentation on the
data set in a few weeks’ time, where you explain what investigating a data set entails and how
Python can be used to do it. You have not been asked to create a deck of presentation slides,
but rather to present your code and its output to them.
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Minimum Viable Project
The minimum standard is a GitHub repository containing a README, a Python script, a
generated summary text file, and images. The README should contain a summary of the
data set and your investigations into it. It should also clearly document how to run the
Python code and what that code does. Furthermore, it should list all references used in
completing the project.
A better project will be well organised and contain detailed explanations. The analysis will
be well conceived, and examples of interesting analyses that others have pursued based on
the data set will be discussed. Note that the point of this project is to use Python. You may
use any Python libraries that you wish, whether they have been discussed in class or not.
You should not be thinking of using spreadsheet software like Excel to do your calculations.
Submissions
GitHub must be used to manage your project submission. Your GitHub repository will form
the main submission of the project. You must submit the URL of your GitHub repository using
the link on the course Moodle page before the deadline. You can do this at any time, as the
last commit before the deadline will be used as your submission for this project.
Any submission that does not have a full and incremental git history with informative commit
messages over the course of the project timeline will be accorded a proportionate mark. It is
expected that your repository will have lots of commits, with each commit relating to a
reasonably small unit of work.
In the last week of term, or at any other time, you may be asked by the lecturer to explain the
contents of your git repository. While it is encouraged that students will engage in peer
learning, any unreferenced documentation and software that is contained in your submission
must have been written by you. You can show this by having an incremental commit history
and by being able to explain your code.
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Marking scheme
This project will be worth 50% of your mark for this module. The following marking scheme
will be used to mark the project out of 100%. Students should note, however, that in certain
circumstances the examiner’s overall impression of the project may influence marks in each
individual component.
Mark Heading Description
25% Research Investigation of each notebook and script as
demonstrated by references, background
information, and approach.
25% Development Clear, well-written, and efficient code with
appropriate comments.
25% Consistency Good planning and pragmatic attitude to work as
evidenced by commit history.
25% Documentation Concise descriptions and explanations of theoretical
and practical aspects of problems.
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Advice for students
• Your git commit history should be extensive. A reasonable unit of work for a
single commit is a small function, or a handful of comments, or a small change
that fixes a bug. If you are well organised you will find it easier to determine
the size of a reasonable commit, and it will show in your git history.
• Using information, code and data from outside sources is sometimes
acceptable — so long as it is licensed to permit this, you clearly reference the
source, and the overall project is substantially your own work. Using a source
that does not meet these three conditions could jeopardise your mark.
• You must be able to explain your project during and after its completion. Bear
this in mind when you are writing your README. If you had trouble
understanding something in the first place, you will likely have trouble
explaining it a couple of weeks later. Write a short explanation of it in your
README, so that you can jog your memory later.
• Everyone is susceptible to procrastination and disorganisation. You are
expected to be aware of this and take reasonable measures to avoid them. The
best way to do this is to draw up an initial straight-forward project plan and
keep it updated. You can show the examiner that you have done this in several
ways. The easiest is to summarise the project plan in your README. Another
way is to use a to-do list like GitHub Issues.
• Students have problems with projects from time to time. Some of these are
unavoidable, such as external factors relating to family issues or illness. In such
cases allowances can sometimes be made. Other problems are preventable,
such as missing the submission deadline because you are having internet
connectivity issues five minutes before it. Students should be able to show that
up until an issue arose, they had completed a reasonable and proportionate
amount of work and took reasonable steps to avoid preventable issues.
• Go easy on yourself – this is one project in one module. It will not define you
or your life. A higher overall course mark should not be determined by a single
project, but rather your performance in all your work in all your modules. Here,
you are just trying to demonstrate to yourself, to the examiners, and to
prospective future employers, that you can take a reasonably straight-forward
problem and solve it within a few weeks.
References
[1] Python Software Foundation. Welcome to python.org. https://www.python.org/.
[2] GMIT. Quality assurance framework. https://www.gmit.ie/general/quality-assuranceframework.
[3] UC Irvine Machine Learning Repository. Iris data set.
https://archive.ics.uci.edu/ml/datasets/Iris.
