School of Mathematics and Statistics
Applied Data Science (MAST30034) 2023
An End-to-End Data Science Project
Due date: Monday 21st of August 09:00 am
Project Weight: 30%
Author: Akira Takihara Wang
Project Overview
This project aims to make a quantitative analysis of the New York City Taxi and Limousine
Service Trip Record Data. The dataset covers trips taken in various types of taxi and for-hire
vehicle services in the New York City area. The data in parquet format is directly downloadable
from here, with corresponding usage guide linked here. You will need to choose a minimum
of 6 months if working with PySpark or 3 months if working with pandas from 2016 or later
(ensure your data includes Zones, not coordinates). We strongly recommend you use PySpark
as practice for your future career.
Students will be required to prepare a self-contained report which must be 6-8 pages including
figures, excluding references, and written using Latex. Please do not submit any other
format written in Word or Google Docs, we are expecting a compiled pdf written in Latex.
There are no exceptions.
Project Expectations
Please refer to the Canvas Subject Overview for expectations and further information.
We understand that the page limit is strict and quite short. This project aims to get students
to be able to concisely summarise information professionally. This is because the results of
Project 1 will be used to allocate which project student groups get for Project 2 (Industry
Project).
Lastly, we know that the best way to learn new tools is to use and apply them in a project,
consider this to be “the project”. Please try your best, the tutor team will be here to support
you where possible. Sample solutions will be released at the start of Week 2.
Project Assumptions
• Students are free to choose any software, language, or package that is deemed useful to
complete this project, although it is strongly recommended that Python and PySpark
be used.
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• A Latex report template will be provided and students are not allowed to change
the margins or font size. Students who prepare their document templates will be
required to add margin commands to adhere to the requirements. Otherwise, there will
be penalties. We have been very clear here so do not submit any other document
that was not written up in Latex.
• Students must maintain a GitHub repository with an appropriate and documented
README.md file. A template repository has been provided for your benefit under Canvas
→ Modules → Project 1 Links → Templates via GitHub Classrooms.
• Students have the freedom of choice to select their timeline to analyze, the type of Licensed
Taxi you wish to focus on (i.e Yellow vs Green Taxi, Taxi vs For-Hire Vehicles), and the
choice of attributes for their area of study. Once again, make sure the time frame chosen
is 2016 onward.
• Students should use any external datasets which are deemed sufficiently relevant to
support the analysis and attributes of the study.
• The timeline and dataset must be sufficiently “large” and justified to support your research
goal. Students may subsample the data when visualizing or fitting a model
(you must state and justify this in the report or you will be penalised), but,
must use the full distribution when analyzing the distribution, aggregating
attributes, or performing outlier analysis.
Report Format
The report must be between 6-8 pages (including figures, excluding references, written in
Latex), covering at least, but not limited to, the following items:
• First and foremost, there should be no code present in the report. Please see the
sample solutions for examples.
• Identify the taxi datasets, external datasets, attributes, dataset shape, data timeline,
target audience, and relevant research goal. Clear and convincing justification is
required for each point.
• Outline the high-level methodology and preprocessing for visualization and statistical
modelling for the research goal. We will be reading your code for the detailed preprocessing
steps, so make sure you include comments, docstrings, and markdown cells so it is readable
even to a five year old child.
• Preliminary data analysis with interpretation and discussion.
• A modelling section with at least two contrasting models and approaches with
relevant and correct evaluation metrics.
• Make practical and realistic recommendations based on the final results for the identified
audience. Recommendations must be supported by the analysis completed in the report
and should not be generic. For example:
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◦ “Taxi drivers can earn more money by taking more trips” is not a good example.
◦ “Taxi drivers should aim to work their shifts at the airport during the day as we saw
in our analysis above. This is due to XYZ and ABC reasons...” is a good example.
• Tables and figures should be referenced where appropriate. Here are some examples:
◦ “From (Figure 3) we can see ...”
◦ “... the Gini Impurity Metric [3] suggests that ...”
◦ “(Table 3) shows the ...”
• Ensure that figures are reasonably placed and readable, as ineligible figures or tables will
be ignored.
◦ If you look at the image in the report and it looks squashed, blurry, or hard to read,
do not add it in. If you cannot read it, we cannot either.
◦ If the font size is too small we will ignore it.
• Less is more, choose the information you present carefully. Irrelevant information will
make the report hard to follow and lead to significant reductions in marks.
• Finally, the report should be proofread several times before submission to minimise
grammatical and spelling errors. You have a plethora of online tools available, as long as
you do not plagiarise we are okay.
The Latex template is available via Overleaf or found under Canvas → Modules → Project 1
Links → Templates. You can download the source code and upload the main.tex to Overleaf or
copy the project under Menu → Actions → Copy Project (located top left corner) on Overleaf.
If you wish to use your Latex template, ensure your margins and document class adhere to our
requirements by adding the following commands:
• \documentclass[11pt]{article}
• \usepackage[top=0.9in, left=0.9in, bottom=0.9in, right=0.9in]{geometry}
GitHub Requirement
The GitHub repository template is available via GitHub Classrooms or found under Canvas
→ Modules → Project 1 Links → Repo Template. You must use GitHub Classrooms and not
your own personal repository.
All repositories will be cloned, executed (run), and referred to during marking, so please ensure
the code is reproducible and readable. For example, if a student uses Python and uses
external libraries, then a requirements.txt for a pip installation should be provided, such
that anyone can run the command, install the packages, and run the code without errors. If
you are unsure about this, search it up and follow a guide or ask your tutor. Repositories that
fail to run will incur a penalty.
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Hurdle Requirement
There is a hurdle requirement for you to submit a working GitHub repository and report. We
have provided a template GitHub and Latex report for your benefit. Please ensure you do not
leave this until the last minute to sort out as the submission deadline is strict.
Assessment
This project is worth 30% of your final grade with the following requirements:
1. If no external dataset is used OR the student has chosen an insufficient dataset size, then
the maximum number of marks is capped at 22.5/30 marks.
◦ For example, if a student achieved 28/30 overall without meeting the requirements,
their mark will be reduced to a maximum of 22.5/30.
◦ If for some very unexpected reason you are unable to parse more than 6 months of
data with PySpark (or 3 months with pandas), you must let us know in advance via
email with your reasoning.
◦ We will provide a JupyterHub server to students with insufficient resources in a
first-in, best-dressed manner.
2. If the chosen external datasets are relevant, justified, and used to complement the research
goal, then full marks are awarded.
◦ Some examples of suitable external datasets may be ongoing sports events, protests,
weather forecasts (such as the impact of snow), vehicle crashes, etc.
◦ There are several sources and some may require web scraping or direct contact with
the owner of the dataset. It is up to students to choose and find one.
Strictly speaking, more marks will be available for students who perform additional analysis,
with the highest marks available for students who perform exceptional analysis by drawing
upon several external resources.
Marking Scheme
This is an approximate marking scheme. Students who just “tick the boxes” will not get
full marks. We have provided this to be transparent on the marking process and expectations.
Introduction (3 marks)
• 2 marks for an appropriate choice of timeline, type of Licensed Taxi, attributes to study,
dataset shape, and target audience, with convincing justification for each of the
choices made above.
◦ As part of this assessment, it is your job to convince us that your choice is correct
and valid, not the other way around.
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◦ No marks will be awarded if there is a lack of convincing justification. Please come
up with a good reason even if you do not believe in it as it is critical that you can
convince us that your choices are correct.
◦ If you are using an external dataset, you must also clearly specify the details and
provide a link to the dataset as a reference.
◦ All assumptions about the dataset or business rules must be introduced and stated
in the Introduction.
• 1 mark for detailing the high-level overview of your methodology. Essentially, a “TLDR”
of your contribution. It is on you to be clear and specify all details, not on us to infer it
and connect the dots.
Preprocessing (2.5 marks)
• 2.5 marks for suitably conveying the high-level preprocessing steps in dot points. Remember,
keep it short and concise and only list out the important stuff.
◦ If you are filtering, removing, or joining datasets, you must specify the dataset shape
at each step. This will be verified with your code.
◦ If you are removing records, you must justify and explain it or you will be penalised.
For example, if you are removing all credit card payments because you are analysing
tipping, then cite the business rule from the provided Data Dictionary and TLC
Website.
Analysis and Geospatial Visualisation (5.5 marks)
• 5.5 marks in total for analysis of the attribute(s) related to your area of study. You
should carefully consider the story you want to tell and the relevancy of the presented
information. For instance, if your main message is that pickup locations play important
roles in taxi drivers’ overall revenue, then presenting analysis about only tips is irrelevant
and a poor choice. Ensure you justify every step taken for full marks. Your report will
need to include the following aspects:
◦ 1.5 marks for outlier analysis, discussion of the distribution, relevant imputations for
NULL values, and summarised findings of interest for your chosen attribute(s).
◦ 2 marks for describing the important relationship(s) present between attribute(s) of
“interest”. You may consider interaction for this part.
◦ 2 marks for discussion, particularly, if a certain visualization raises an “interesting”
area for further analysis or results in the lack of anything “interesting” for further
analysis.
◦ Hint: For analysis and discussion, you should try following a “From (Figure X)
we can see ABC, therefore DEF, perhaps because of XYZ” formula. We don’t
need students to explain the obvious, we are interested in reading your analysis and
reasoning.
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Statistical Modelling (5 marks)
• 1 mark for clearly specifying and justifying at least two chosen models with your chosen
input and output attribute(s).
◦ List out your assumptions and ensure the attribute(s) you have chosen are suitable.
◦ Be very careful with the attributes i.e continuous vs ordinal vs categorical. Ensure
your model is suitable. We will penalise incorrect usage of features.
◦ If your model or algorithm was covered in a subject listed in the Subject Overview,
you only need to reference and state it. Otherwise, please provide a brief introduction
to your model with appropriate references.
◦ Predictions should use future data (i.e using 2018 to predict 2019) i.e do not use
something like k-fold Cross Validation across 2019 and 2020 on a random shuffle.
• 4 marks for analyzing their area of research by combining the findings from two models.
◦ 2 marks for analysis and/or comparison of models. This can be done through proper
model refinement, feature selection, error analysis, model evaluation, or any suitable
technique.
◦ 2 marks for interpretation and discussion of the model for your study goal. If you
conduct predictions, you should discuss their implications here.
◦ Hint: We are not interested in hearing about your model improving accuracy by
a few percent. We want to hear how your model is going to support your target
audience. For example, a taxi driver is not going to care about a Neural Network
that predicts tipping. A taxi driver will however, be interested in a model that tells
them where to go for more money.
Recommendations (4 marks)
• 4 marks for at least two sound recommendations for your target audience with the
supporting evidence from previous sections.
◦ You should try and combine the findings of your analysis and model to give recommendations.
◦ Recommendations should be practical and not generic. If it does not make sense in
real life to implement the recommendation, do not recommend it.
◦ Please do not provide generic recommendations that can be inferred using common
sense. You have done hard work and analysis, make sure to use those findings to
back your recommendations!
Report Writing and Code (10 marks)
• 2 marks for figures and tables with appropriate font size, figure sizes, choice of colour,
etc.
• 3 marks for being able to convey the ideas and analysis (i.e through the use of correct
and consistent grammar, spelling, citations, references, and report structure). Please
proofread and use online tools to make sure your report is of high quality.
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• 2 marks for quality code that runs without any issues and is maintained in a neat
repository.
• 3 marks for excellent and readable code (i.e markdown cells, in-line comments, good
variable names, docstrings for functions, reasonably adheres to PEP8 pylint or flake8).
This will be assessed especially for the preprocessing and modelling steps.
Possible Additions (Maximum of +2 marks, capped at 32/30)
There will also be certain areas of possible additions for outstanding reports:
• Exceptionally well-written reports that make the reader go “damn that’s actually good”.
• Recommendations and/or analyses that are realistic and feasible to deliver and be considered
for production.
• Outstanding visualizations that are consistently great throughout the report.
• High code quality and readability that makes it easy to understand and run with no
issues.
Possible Deductions (Maximum of -3 marks, capped at 0/30)
There will also be certain areas of possible deductions depending on the issues found:
• Insufficient quality of report writing (i.e the flow of the report was verbose or confusing,
difficult-to-read sections, etc).
• Incorrect logic or breach of a business rule that has been stated in the Data Dictionaries
and TLC Website.
• Overly verbose paragraphs in report and code.
• Insufficient commenting and detail in code for preprocessing and modelling sections.
• Figure sizes that are far too small.
• Significantly difficult-to-read code.
If you would like feedback on your code or report, please ask your tutor at the end of the
tutorial during question time.
Submission Details
• Report submissions must be made via Turnitin on Canvas in PDF format written using
Latex. We will not be accepting and marking any other format.
• Your final code must be in the GitHub repository and hyperlinked in the report. Any
submission without a GitHub link will fail this component.
• Late submissions will incur a deduction of 10% (3 marks) per 24 hours past the submission
deadline. If you submit late, you must email the head tutor with your reason.
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Extension Policy
If you have a valid reason with proof to request an extension, you must email the head tutor
sufficiently before the submission deadline. Requests for extensions are not automated and
will be carefully considered on a case-by-case basis. You must provide sufficient supporting
evidence such as a medical certificate. Additionally, we will consider your git commits from
your repository to illustrate the progress made on the project until the date of your request.
Academic Honesty
You are reminded that all submitted project work and code in this subject is to be
your work. Automated similarity checking algorithms will be applied to compare submissions
against all students, previous works, and known public sources. It is the University policy that
cheating by students in any form is not permitted and that work submitted for assessment
purposes must therefore be the independent work of the student concerned. Failure to comply
may result in an Academic Honesty meeting with the faculty, with further escalation to the
Academic Board depending on the severity.
To mitigate the risks of breaching Academic Integrity, please cite and attribute all references
and code functions where applicable. For your report, you may choose any citation style
listed on The University of Melbourne Recite page so long as you use it consistently.
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Getting Started
Okay, so this project spec is quite diabolical and intense. We understand you will be quite
overwhelmed, and that’s fine, the tutor team is here to make sure you get through it and learn
everything you need before you graduate. It’s part of the learning process so you are actually
ready for your future career. So, we’ve written a short guide on how you might get started.
1. Start off by going through the Week 1 Tutorial workshop and looking at the data.
We recommend you also read the FAQs on the TLC Website and download the data
dictionaries.
2. Then, carefully read through this spec and highlight or underline the important parts so
you understand the requirements.
3. Then, figure out what kind of story you want to tell. For example, who is going to care
about this analysis? Who do you want to help? Who’s going to be paying you a salary
to do this work? As a graduate of the Data field, it’s really good to pretend to be your
audience so you can analyse the data and help them.
4. For example, if your target audience is a Taxi Driver, then you should consider what life
is like for them. What are my working hours? Where do I live? Which areas should I go
for more potential rides? Do I want to avoid traffic?
5. Once you have figured it out, then you can start considering your timeline. For example,
do I want to consider the impact of COVID? How about only post-COVID? How about
only pre-COVID?
6. Then, you should consider okay, what are the attributes that related to my research area?
What other information should I bring through? For example, should I look at sporting
events, raves and music festivals, national public holidays, weather, and so on. These will
be your external datasets.
7. Now, you can try aggregate your dataset timeline and present them on a geospatial
visualization, compute descriptive statistics, and analyze summary statistics for your
chosen attributes.
8. Then, dive deeper. Do some reaserch, talk to your fellow students, ask questions. Do
some critical thinking and analysis. Always remember, is my target audience going to
care about this analysis? If the answer is no, rethink your area of analysis.
9. Following this, you can build a Statistical Model to statistically explain relationships
between your input and response variables or use a Machine Learning model to classify/predict
an attribute of choice.
10. Afterwards, you might investigate the correlation and feature relevance between your
attributes, refine your model, and highlight key findings backed by your statistical analysis.
11. Finally, you should summarise and give recommendations to your identified clients or
stakeholders.
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In the event your results are unexpected or lead to unanticipated results, you should aim to
discuss why they occurred and what it entails. This scenario happens quite commonly, so it’s
still in your best interest to make recommendations that support your unexpected results!
Additional Tips
If you’re still unsure of how to start the project, try going through some of the materials and
methods covered in the prerequisite subjects. Depending on the choice of Statistical Model or
Machine Learning Algorithm, you may need to perform some creative feature engineering or
transformation on the dataset.
For example, consider the scenario where your data is linearly separable through the use of a
transformation or kernel trick:
• Consider performing some descriptive analysis before fitting your model to identify issues
with your data such as linear separability, missing values, outliers, etc.
• For supervised learning models, consider the linear separability of your data. When there
is linear separability, some models perform well (i.e SVM), whereas some models (i.e
Logistic Regression) can fail to converge. The kernel trick may be used to induce linear
separability.
• You should also correctly standardize/normalize your dataset depending on the model
used.
• Penalised Regression Models such as Ridge (ℓ2) and LASSO (ℓ1) tend to perform poorly
if the feature space is much smaller than the number of instances or if the attributes are
not standardized.
• Consider performing feature engineering to generate more useful features. Do not perform
it excessively though as it may lead to overfitting.
Final Tips
• Start this Project as soon as possible. It is up to you to spend as little or as much time
as possible on this subject. You are here to learn and develop life-long skills.
• You should aim to write your report professionally, assuming that an employer or client
is paying you a salary or daily rate to conduct this analysis.
• Make sure you use a virtual environment or a new clean environment for development.
Students are recommended to either use macOS or Linux for development. Windows
users are recommended to use Windows Subsystem for Linux (WSL2).
• If you have too much data in a visualization, you can conduct sub-sampling to help
increase the scope of data you can cover. Remember, you shouldn’t have to describe your
visualization in an overly verbose manner.
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• Explain your handling of missing/unreasonable data and why any missing data does not
undermine the validity of your analysis. You should report and justify the approximate
size of data that has been removed.
• When you are trying to make comparisons between figures and tables, make sure your
measurement is of the same scale (i.e do not compare miles to kilometres).
• Always tell the reader what to look for in tables and figures. Be as factual and concise as
possible when reporting your findings with references where appropriate.
• If necessary, define unfamiliar concepts and provide the appropriate background information
with references to aid your work.
• Good luck!