COSC 2670辅导、辅导Python编程语言
- 首页 >> Database作业 COSC 2670/2738 Practical Data Science (with Python)
Project Assignment 3, Semester 2, 2022
Marks : This assignment is worth 30% of the overall assessment for this course.
Due Date : Wed, 26 October 2022, 11:59PM (Week 14), via canvas. Late penalties
apply. A penalty of 10% of the total project score will be deducted per day. No
submissions will be accepted 5 days beyond the due date.
Objective
The key objectives of this assignment are to learn how to compare and contrast
several recommendation system algorithms. There are three major components of the
assignment – a completed Jupyter notebook used to run your experiments, a written
report, and a short video presentation where you describe what you did and your key
findings.
The dataset you will use will be a sample of the Netflix Prize data. The problem is
movie recommendation, and the data is already split into a training and validation set
that you can use to run all of your experiments.
Provided files
The following template files are provided:
SXXXXX-A3.ipynb : The primer Jupyter notebook file you should use to stage
and run all of your experiments.
netflix-5k.movie-titles.feather : The movie title dataframe that can be used to
map a movieID to a title, as well as a list of genres.
netflix-5k.train.feather : The training tuples for 5,000 users, where each tuple
is userID,movieID,rating?.
netflix-5k.validation.feather : A predefined set of validation tuples for the
same users that can be used by you to benchmark the performance of various
algorithms.
A3.pdf : This specification file.
Creating Your Workspace
Once again, you should rename the file SXXXXX-A3.ipynb appropriately based on
your student ID.
Creating Your Anaconda Environment
In order to create your anaconda environment for this project, you should run the
following command in a terminal shell:
conda create -n PDSA3 python=3.8
conda activate PDSA3
pip install jupyterhub notebook numpy pandas
pip install matplotlib scikit-learn seaborn
1
pip install kneed scikit-surprise
Note that both kneed and scikit-surprise can be finicky on some systems. For
example on my machine, an error was thrown during the compile of scikit-surprise
(Macbook Pro M1) but the install still worked when it tried a fallback install method.
So if you find that it really fails for you using pip, then and only then resort to conda.
This would break requirements.txt, but it should work reliably for everyone albeit not
very reproducible. The magic commands would be:
conda install -c conda-forge kneed
conda install -c conda-forge scikit-surprise
You can type “pip freeze” to see a list of the packages that are correctly installed
in your environment. You can also install scikit-learn-intelex and/or psutil if
you want to use the Intel-based optimisations or debug memory management as shown
in the sample Jupyter notebook.
If you also wish the timing and other jupyter extensions to be enabled in your
notebook (optional but may be useful depending on how you decide to present your
results), you need to run the following additional commands:
pip install jupyter_contrib_nbextensions
pip install jupyter_nbextensions_configurator
jupyter contrib nbextension install --user
jupyter nbextensions_configurator enable --user
Now you just need to type “jupyter notebook” to start up jupyter correctly with
access to the libraries you just installed.
Also, recall that if you ever stop working in your environment and come back later.
You must open a terminal, run “conda activate PDSA3” and then “jupyter notebook”,
otherwise you will not be working in the virtual environment you created above, and
most things you try to do will probably start failing.
You should not use any other libraries to complete your assignment beyond the
ones shown above without written permission from the course coordinator (Shane).
1 The Jupyter Notebook Primer (5 marks)
I have included the jupyter notebook I walked through at the end of the Week 10
Lectorial. This notebook will provide you with everything you need to correctly load
the dataframe files from feather, and also includes an example of how to do both a
grid search and randomised search for parameter tuning on one of the recommendation
system algorithms included in Surprise. You should spend some time reading the API
documentation and tutorial for this library provided at https://surpriselib.com.
This will be critical information you should use to stage your experiments.
2 The Report (15 marks)
The main component of your assignment will be to carefully write up your key findings.
Your report should not be more than 5 pages using 11pt font. You may also have one
2
additional Appendix page containing additional graphs or tables. Your final report
must be submitted as a PDF file. You can use Microsoft Word, but I would strongly
encourage you to consider writing up your report using LATEX (https:\overleaf.
com). Writing in LATEX may seem daunting at first, but Overleaf provides plenty of
tutorials and examples, and it is pretty easy once you get the hang of it. This spec
file was written using LATEX. Microsoft Word is not a good tool for writing technical
documents, and in fact most Computer Science Conferences all require papers to be
written in LATEX. The quality of of the presentation layer is easily discernible by most
people in Computer Science. Write a paragraph or two with a graph or diagram in
overleaf and then in MSWord and compare the two – I bet you’ll see the difference
immediately!
Regardless of which tool you use to generate your final PDF, the format of the
report should be:
1. Your name and student number at the top of page 1.
2. Introduction (usually no more than 1/2 page).
3. Methodology (usually about 1 page) - This would contain a clear description of
each recommendation system algorithm you are using and a rationale as to why
it is being used.
4. Experiments ( 3 pages) – This is the main component of your report. Here you
should document all of the parameters and algorithms used, Tables, Figures, or
Graphs that you create in order to compare and contrast all of the algorithms you
have benchmarked and a discussion about what you have discovered. There is
no reason include images of code snippets as you are submitting your Jupyter
notebook already – you should include images of graphs you create – assuming
they are important to the story you are telling.
5. Conclusion (1/2 page) – A clear summary of your key findings.
6. References (Separate page) – You can include as many references as you see fit
and need in your report, and this is not counted against the 5 page limit.
7. Appendix (Separate page) – Any additional graphs or tables that you think are
important but that you could not include into the main document because of
space constraints.
Summary – a report that has a 5 page body, 1+ pages of citations, and 1 optional
page at the end as an Appendix.
You must compare at least four different algorithms from the surprise library in
your report. Note that one algorithm (e.g. SVD) with two different parameter settings
does not count as two different algorithms. That is one algorithm with two different
parameter settings, i.e. one algorithm. You can certainly include results for multiple
parameter settings for each of the four algorithms in your experiments, just make sure
you are using 4 different algorithms in total in your shootout.
In week 8, I provided several evaluation classes you can use to compute a wide
variety of evaluation measures, and we encourage you to explore as many as you
can, as doing so will provide additional evidence that your “winning” algorithm is
really a winner. The “official” metric will be RMSE as this is the metric used in the
original competition, but you should compare the 4 algorithms using a minimum of
3
three different evaluation metrics. You should aim to have at least one algorithm that
can achieve an RMSE score≤ 0.800. This should be achievable with a little parameter
tuning, if you choose good algorithms from surprise, and you may even decide that the
algorithm that got the best RMSE score is not necessarily the “best” algorithm overall
based on the experiments you have ran. Hint: Think carefully about what “good”
movie recommendations really mean to you. We all know what it is, so what do you
think is the best way to prove or disprove recommendations from Algorithm A are
clearly better than the ones you get from Algorithm B. We covered a wide variety of
evaluation measures in the Week 8 Lectorial, so go look at what was in that notebook
and think about it.
Other key hints – (1) If you have a Figure, Table, or Diagram in the report, it must
have a caption and you must reference it in your report and discuss it. By that I mean
“Table 1 contains a table of RMSE results for Algorithms A-E. We can see that ...” (2)
If you use ideas or code from somewhere else, you must include it in your references.
A typical “bibtex” citation for something taken from the web would look something
like:
@misc{StackA,
title = {{Stackoverflow Discussion on X},
howpublished = {\url{http://www.example.com}},
note = {Accessed: 2022-10-05}
The preferred referencing style for Computer Science is usually APA. See https:
//libguides.murdoch.edu.au/APA/sample for an example. There are lots of
tutorials available online on APA referencing, so if you have never seen it, just
search for tutorials on APA referencing and you’ll find more than you could ever
read/watch. (3) If it isn’t clear, you must include graphs, tables, and/or diagrams in
your experimental section, which are to be used as evidence to back any claims about
algorithm performance that you make.
3 The Video Presentation (10 marks)
Your final task it to create a 5-10 minute video recording of you presenting your
findings. You must create slides, and I would strongly encourage you to use PIP
(Picture in Picture Mode) in your video. If you have no idea what I am talking about,
have a look at this video created by our guest speaker when he was a PhD student
at RMIT – https://www.youtube.com/watch?v=jFhhL1mkziQ The video format
should be MP4. No other video formats will be accepted.
A good presentation will have visually appealing slides, a clear story line, and
the presenter will not just be reading what is on the slide or from a piece of paper –
they will be describing what the current slide is showing. If you want to know more,
check out the talk by S. P. Jones on “How to Give a Great Research Talk” – https:
//www.youtube.com/watch?v=sT_-owjKIbA
You might say to yourself, I’m not giving a research talk – but you are. You are
doing experimental research in Data Science on Recommendation Systems. So, treat it
like one.
If you have no idea how to create a PIP video of a presentation quickly and eas-
ily, check out this video – https://www.youtube.com/watch?v=rKDKniBmwe0&
feature=youtu.be. If you don’t like this technique just search for how to create a
4
picture in picture presentation using Zoom or Microsoft Teams. It is surprisingly easy
to do using Microsoft Teams, Zoom, or the open source program OBS. I’m sure there
are many other ways to do it, but there are definitely simple and quick ways to do it
once you see how. The point is that I don’t want “technology” to be an impediment
in creating a great presentation. It is much more useful spending time on getting the
content right and doing a few practice runs so that you can create an interesting talk.
Submission
All submissions must be made on the canvas shell for the course. A link will be
provided within canvas, under the Assignments tab for submissions. Assignments
submitted through any other method will not be marked.
Pay close attention because the final submission must be done differently than
the first two assignments. The rationale for this is so that each of you have access
to a Turnitin report of your submission when you submit. Since you are writing a
report, this is a valuable tool for you to ensure that you do not get dinged for academic
misconduct. Believe it or not, we do take this seriously, and every semester several
students get reported to the university for failing to comply with the plagiarism rules.
So make sure you submit early enough that you can action it, otherwise, you’ll find
yourself in a really uncomfortable meeting with a university panel and you won’t get a
mark for the course until it is resolved!
This is an individual project. Do your own work.
You should submit four separate files in canvas.
[Your Student Number]-A3-notebook.ipynb
[Your Student Number]-A3-notebook.pdf
[Your Student Number]-A3-report.pdf
[Your Student Number]-A3-presentation.mp4
Do not put them in a single zip file or you will not get the plagiarism report as it
cannot run on archived files. If you do submit a single zip file, you will lose a mark,
and we will still run the Turnitin report, but you will not get a copy of it, unless you
end up getting flagged for plagiarism, and none of us wants that to happen. So for your
own sanity and ours please submit the four files individually, named as shown above.
If you do, you should be able to see the Turnitin results for your report and for your
notebook.
GETTING HELP. There is a discussion board available in the course canvas. Please
do not post code snippets in this forum. Do ask questions if you have them! We will
do our best to answer them as quickly as we can. This is the best medium to ask a
question, as there is a very strong chance that if you are confused about something,
your classmates are too. We will also be running office hours in Week 13 from 3-4PM
on Thursday in Collaborate Ultra if you want to stop by and ask a question.
Plagiarism Warning
University Policy on Academic Honesty and Plagiarism: It is University policy that
cheating by students in any form is not permitted, and that work submitted for
5
assessment purposes must be the independent work of the student concerned. Please
see the RMIT policy for more details: https://www.rmit.edu.au/students/
my-course/assessment-results/academic-integrity.
THIS IS NOT A GROUP PROJECT. Students are reminded that this assignment is
to be attempted individually. Plagiarism of any form will result in zero marks being
given for this assessment, and can result in disciplinary action. We routinely use
plagiarism software on projects! Please, please don’t do it. Be aware that paying
someone on a coding site to do it for you is a form of plagiarism. If you are submitting
work that is not your own, regardless of how you got it – you are in breach of this
policy.
Extension Policy
Individual extensions are very unlikely to be considered or granted by
the PDS team for the final assignment. We have this rule in place simply
because we set the deadline as late in the semester as we possibly could
based on the deadlines we have to submit your final grades. If you have
a strong case for needing one, you must request it 48 hours before the
deadline, and you should submit the form provided on canvas along
with supporting documentation to me. Be aware, there is no wiggle
room on the Assignment 3 deadline. We have a very short period of
time to mark 300 projects and submit the final marks to the University
Panel for approval. So, if you do get an extension from me, it will be
very short – maybe 1-2 days at most.
If you find yourself in a situation where you really need an extension
of a week or more (it can happen to any of us), you will need to apply
formally through RMIT for Special Consideration. This isn’t because
we are heartless academics, it is because we cannot change a mark for
a course after the university deadline without documentation from SC.
Even with the appropriate documentation, it can take many weeks to
get grade changes approved after the cutoff, which means in practical
terms, if you miss the cutoff, you won’t be able to graduate if it your
last semester, or you won’t be able to register for any classes that have
PDS as a prerequisite when enrolments open for next semester. So, the
moral of the story is, seriously, don’t be late on the final assignment. If
you are, there is very little we can do to turn it around and get a final
mark for you before the RMIT grade entry and approval deadline.
There is a University process in place to grant extensions. Our
preference is that you go this route if you must, as they have very
clear criteria to grant exemptions. We will always honour these –
assuming you provided the correct information to them. For more
information about applying for Special Consideration, see the rules and
regulations at https://www.rmit.edu.au/students/my-course/
assessment-results/special-consideration-extensions/
special-consideration.
6
Getting Help
There is a discussion group available in the course canvas. Please do not post code
snippets in this forum. Do ask questions if you have them! We will do our best to
answer them as quickly as we can. This is the best medium to ask a question, as there
is a very strong chance that if you are confused about something, your classmates are
too. We will also be running office hours in Week 13 from 3-4PM on Thursday in
Collaborate Ultra if you want to stop by and ask a question.
Email us.
Use the discussion board.
Ask a question in a Lectorial or a practical.
There is help available if you need it. We do strongly urge that you start with the
discussion board because, as mentioned above, if you are confused, there is a very
good chance someone else has exactly the same question, and it is not a great use of
the team’s time to keep answering the same question over and over.
Project Assignment 3, Semester 2, 2022
Marks : This assignment is worth 30% of the overall assessment for this course.
Due Date : Wed, 26 October 2022, 11:59PM (Week 14), via canvas. Late penalties
apply. A penalty of 10% of the total project score will be deducted per day. No
submissions will be accepted 5 days beyond the due date.
Objective
The key objectives of this assignment are to learn how to compare and contrast
several recommendation system algorithms. There are three major components of the
assignment – a completed Jupyter notebook used to run your experiments, a written
report, and a short video presentation where you describe what you did and your key
findings.
The dataset you will use will be a sample of the Netflix Prize data. The problem is
movie recommendation, and the data is already split into a training and validation set
that you can use to run all of your experiments.
Provided files
The following template files are provided:
SXXXXX-A3.ipynb : The primer Jupyter notebook file you should use to stage
and run all of your experiments.
netflix-5k.movie-titles.feather : The movie title dataframe that can be used to
map a movieID to a title, as well as a list of genres.
netflix-5k.train.feather : The training tuples for 5,000 users, where each tuple
is userID,movieID,rating?.
netflix-5k.validation.feather : A predefined set of validation tuples for the
same users that can be used by you to benchmark the performance of various
algorithms.
A3.pdf : This specification file.
Creating Your Workspace
Once again, you should rename the file SXXXXX-A3.ipynb appropriately based on
your student ID.
Creating Your Anaconda Environment
In order to create your anaconda environment for this project, you should run the
following command in a terminal shell:
conda create -n PDSA3 python=3.8
conda activate PDSA3
pip install jupyterhub notebook numpy pandas
pip install matplotlib scikit-learn seaborn
1
pip install kneed scikit-surprise
Note that both kneed and scikit-surprise can be finicky on some systems. For
example on my machine, an error was thrown during the compile of scikit-surprise
(Macbook Pro M1) but the install still worked when it tried a fallback install method.
So if you find that it really fails for you using pip, then and only then resort to conda.
This would break requirements.txt, but it should work reliably for everyone albeit not
very reproducible. The magic commands would be:
conda install -c conda-forge kneed
conda install -c conda-forge scikit-surprise
You can type “pip freeze” to see a list of the packages that are correctly installed
in your environment. You can also install scikit-learn-intelex and/or psutil if
you want to use the Intel-based optimisations or debug memory management as shown
in the sample Jupyter notebook.
If you also wish the timing and other jupyter extensions to be enabled in your
notebook (optional but may be useful depending on how you decide to present your
results), you need to run the following additional commands:
pip install jupyter_contrib_nbextensions
pip install jupyter_nbextensions_configurator
jupyter contrib nbextension install --user
jupyter nbextensions_configurator enable --user
Now you just need to type “jupyter notebook” to start up jupyter correctly with
access to the libraries you just installed.
Also, recall that if you ever stop working in your environment and come back later.
You must open a terminal, run “conda activate PDSA3” and then “jupyter notebook”,
otherwise you will not be working in the virtual environment you created above, and
most things you try to do will probably start failing.
You should not use any other libraries to complete your assignment beyond the
ones shown above without written permission from the course coordinator (Shane).
1 The Jupyter Notebook Primer (5 marks)
I have included the jupyter notebook I walked through at the end of the Week 10
Lectorial. This notebook will provide you with everything you need to correctly load
the dataframe files from feather, and also includes an example of how to do both a
grid search and randomised search for parameter tuning on one of the recommendation
system algorithms included in Surprise. You should spend some time reading the API
documentation and tutorial for this library provided at https://surpriselib.com.
This will be critical information you should use to stage your experiments.
2 The Report (15 marks)
The main component of your assignment will be to carefully write up your key findings.
Your report should not be more than 5 pages using 11pt font. You may also have one
2
additional Appendix page containing additional graphs or tables. Your final report
must be submitted as a PDF file. You can use Microsoft Word, but I would strongly
encourage you to consider writing up your report using LATEX (https:\overleaf.
com). Writing in LATEX may seem daunting at first, but Overleaf provides plenty of
tutorials and examples, and it is pretty easy once you get the hang of it. This spec
file was written using LATEX. Microsoft Word is not a good tool for writing technical
documents, and in fact most Computer Science Conferences all require papers to be
written in LATEX. The quality of of the presentation layer is easily discernible by most
people in Computer Science. Write a paragraph or two with a graph or diagram in
overleaf and then in MSWord and compare the two – I bet you’ll see the difference
immediately!
Regardless of which tool you use to generate your final PDF, the format of the
report should be:
1. Your name and student number at the top of page 1.
2. Introduction (usually no more than 1/2 page).
3. Methodology (usually about 1 page) - This would contain a clear description of
each recommendation system algorithm you are using and a rationale as to why
it is being used.
4. Experiments ( 3 pages) – This is the main component of your report. Here you
should document all of the parameters and algorithms used, Tables, Figures, or
Graphs that you create in order to compare and contrast all of the algorithms you
have benchmarked and a discussion about what you have discovered. There is
no reason include images of code snippets as you are submitting your Jupyter
notebook already – you should include images of graphs you create – assuming
they are important to the story you are telling.
5. Conclusion (1/2 page) – A clear summary of your key findings.
6. References (Separate page) – You can include as many references as you see fit
and need in your report, and this is not counted against the 5 page limit.
7. Appendix (Separate page) – Any additional graphs or tables that you think are
important but that you could not include into the main document because of
space constraints.
Summary – a report that has a 5 page body, 1+ pages of citations, and 1 optional
page at the end as an Appendix.
You must compare at least four different algorithms from the surprise library in
your report. Note that one algorithm (e.g. SVD) with two different parameter settings
does not count as two different algorithms. That is one algorithm with two different
parameter settings, i.e. one algorithm. You can certainly include results for multiple
parameter settings for each of the four algorithms in your experiments, just make sure
you are using 4 different algorithms in total in your shootout.
In week 8, I provided several evaluation classes you can use to compute a wide
variety of evaluation measures, and we encourage you to explore as many as you
can, as doing so will provide additional evidence that your “winning” algorithm is
really a winner. The “official” metric will be RMSE as this is the metric used in the
original competition, but you should compare the 4 algorithms using a minimum of
3
three different evaluation metrics. You should aim to have at least one algorithm that
can achieve an RMSE score≤ 0.800. This should be achievable with a little parameter
tuning, if you choose good algorithms from surprise, and you may even decide that the
algorithm that got the best RMSE score is not necessarily the “best” algorithm overall
based on the experiments you have ran. Hint: Think carefully about what “good”
movie recommendations really mean to you. We all know what it is, so what do you
think is the best way to prove or disprove recommendations from Algorithm A are
clearly better than the ones you get from Algorithm B. We covered a wide variety of
evaluation measures in the Week 8 Lectorial, so go look at what was in that notebook
and think about it.
Other key hints – (1) If you have a Figure, Table, or Diagram in the report, it must
have a caption and you must reference it in your report and discuss it. By that I mean
“Table 1 contains a table of RMSE results for Algorithms A-E. We can see that ...” (2)
If you use ideas or code from somewhere else, you must include it in your references.
A typical “bibtex” citation for something taken from the web would look something
like:
@misc{StackA,
title = {{Stackoverflow Discussion on X},
howpublished = {\url{http://www.example.com}},
note = {Accessed: 2022-10-05}
The preferred referencing style for Computer Science is usually APA. See https:
//libguides.murdoch.edu.au/APA/sample for an example. There are lots of
tutorials available online on APA referencing, so if you have never seen it, just
search for tutorials on APA referencing and you’ll find more than you could ever
read/watch. (3) If it isn’t clear, you must include graphs, tables, and/or diagrams in
your experimental section, which are to be used as evidence to back any claims about
algorithm performance that you make.
3 The Video Presentation (10 marks)
Your final task it to create a 5-10 minute video recording of you presenting your
findings. You must create slides, and I would strongly encourage you to use PIP
(Picture in Picture Mode) in your video. If you have no idea what I am talking about,
have a look at this video created by our guest speaker when he was a PhD student
at RMIT – https://www.youtube.com/watch?v=jFhhL1mkziQ The video format
should be MP4. No other video formats will be accepted.
A good presentation will have visually appealing slides, a clear story line, and
the presenter will not just be reading what is on the slide or from a piece of paper –
they will be describing what the current slide is showing. If you want to know more,
check out the talk by S. P. Jones on “How to Give a Great Research Talk” – https:
//www.youtube.com/watch?v=sT_-owjKIbA
You might say to yourself, I’m not giving a research talk – but you are. You are
doing experimental research in Data Science on Recommendation Systems. So, treat it
like one.
If you have no idea how to create a PIP video of a presentation quickly and eas-
ily, check out this video – https://www.youtube.com/watch?v=rKDKniBmwe0&
feature=youtu.be. If you don’t like this technique just search for how to create a
4
picture in picture presentation using Zoom or Microsoft Teams. It is surprisingly easy
to do using Microsoft Teams, Zoom, or the open source program OBS. I’m sure there
are many other ways to do it, but there are definitely simple and quick ways to do it
once you see how. The point is that I don’t want “technology” to be an impediment
in creating a great presentation. It is much more useful spending time on getting the
content right and doing a few practice runs so that you can create an interesting talk.
Submission
All submissions must be made on the canvas shell for the course. A link will be
provided within canvas, under the Assignments tab for submissions. Assignments
submitted through any other method will not be marked.
Pay close attention because the final submission must be done differently than
the first two assignments. The rationale for this is so that each of you have access
to a Turnitin report of your submission when you submit. Since you are writing a
report, this is a valuable tool for you to ensure that you do not get dinged for academic
misconduct. Believe it or not, we do take this seriously, and every semester several
students get reported to the university for failing to comply with the plagiarism rules.
So make sure you submit early enough that you can action it, otherwise, you’ll find
yourself in a really uncomfortable meeting with a university panel and you won’t get a
mark for the course until it is resolved!
This is an individual project. Do your own work.
You should submit four separate files in canvas.
[Your Student Number]-A3-notebook.ipynb
[Your Student Number]-A3-notebook.pdf
[Your Student Number]-A3-report.pdf
[Your Student Number]-A3-presentation.mp4
Do not put them in a single zip file or you will not get the plagiarism report as it
cannot run on archived files. If you do submit a single zip file, you will lose a mark,
and we will still run the Turnitin report, but you will not get a copy of it, unless you
end up getting flagged for plagiarism, and none of us wants that to happen. So for your
own sanity and ours please submit the four files individually, named as shown above.
If you do, you should be able to see the Turnitin results for your report and for your
notebook.
GETTING HELP. There is a discussion board available in the course canvas. Please
do not post code snippets in this forum. Do ask questions if you have them! We will
do our best to answer them as quickly as we can. This is the best medium to ask a
question, as there is a very strong chance that if you are confused about something,
your classmates are too. We will also be running office hours in Week 13 from 3-4PM
on Thursday in Collaborate Ultra if you want to stop by and ask a question.
Plagiarism Warning
University Policy on Academic Honesty and Plagiarism: It is University policy that
cheating by students in any form is not permitted, and that work submitted for
5
assessment purposes must be the independent work of the student concerned. Please
see the RMIT policy for more details: https://www.rmit.edu.au/students/
my-course/assessment-results/academic-integrity.
THIS IS NOT A GROUP PROJECT. Students are reminded that this assignment is
to be attempted individually. Plagiarism of any form will result in zero marks being
given for this assessment, and can result in disciplinary action. We routinely use
plagiarism software on projects! Please, please don’t do it. Be aware that paying
someone on a coding site to do it for you is a form of plagiarism. If you are submitting
work that is not your own, regardless of how you got it – you are in breach of this
policy.
Extension Policy
Individual extensions are very unlikely to be considered or granted by
the PDS team for the final assignment. We have this rule in place simply
because we set the deadline as late in the semester as we possibly could
based on the deadlines we have to submit your final grades. If you have
a strong case for needing one, you must request it 48 hours before the
deadline, and you should submit the form provided on canvas along
with supporting documentation to me. Be aware, there is no wiggle
room on the Assignment 3 deadline. We have a very short period of
time to mark 300 projects and submit the final marks to the University
Panel for approval. So, if you do get an extension from me, it will be
very short – maybe 1-2 days at most.
If you find yourself in a situation where you really need an extension
of a week or more (it can happen to any of us), you will need to apply
formally through RMIT for Special Consideration. This isn’t because
we are heartless academics, it is because we cannot change a mark for
a course after the university deadline without documentation from SC.
Even with the appropriate documentation, it can take many weeks to
get grade changes approved after the cutoff, which means in practical
terms, if you miss the cutoff, you won’t be able to graduate if it your
last semester, or you won’t be able to register for any classes that have
PDS as a prerequisite when enrolments open for next semester. So, the
moral of the story is, seriously, don’t be late on the final assignment. If
you are, there is very little we can do to turn it around and get a final
mark for you before the RMIT grade entry and approval deadline.
There is a University process in place to grant extensions. Our
preference is that you go this route if you must, as they have very
clear criteria to grant exemptions. We will always honour these –
assuming you provided the correct information to them. For more
information about applying for Special Consideration, see the rules and
regulations at https://www.rmit.edu.au/students/my-course/
assessment-results/special-consideration-extensions/
special-consideration.
6
Getting Help
There is a discussion group available in the course canvas. Please do not post code
snippets in this forum. Do ask questions if you have them! We will do our best to
answer them as quickly as we can. This is the best medium to ask a question, as there
is a very strong chance that if you are confused about something, your classmates are
too. We will also be running office hours in Week 13 from 3-4PM on Thursday in
Collaborate Ultra if you want to stop by and ask a question.
Email us.
Use the discussion board.
Ask a question in a Lectorial or a practical.
There is help available if you need it. We do strongly urge that you start with the
discussion board because, as mentioned above, if you are confused, there is a very
good chance someone else has exactly the same question, and it is not a great use of
the team’s time to keep answering the same question over and over.