Python Programming

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ECA – July Semester 2019
End-of-Course Assessment – July Semester 2019
Python Programming
1. This End-of-Course Assessment paper comprises SIX (6) pages (including the cover page).
2. You are to include the following particulars in your submission: Course Code, Title of the
ECA, SUSS PI No., Your Name, and Submission Date.
3. Late submission will be subjected to the marks deduction scheme. Please refer to the
Student Handbook for details.
ECA Submission Deadline: 4 September 2019, 12 noon
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ECA – July Semester 2019
ECA Submission Guidelines
Please follow the submission instructions stated below:
A – What Must Be Submitted
You are required to submit the following TWO (2) items for marking and grading:
• A report (you should submit this item first as it carries the highest weightage).
• The Python notebook file & the used datasets
B – Submission Deadline
• The TWO (2) items of report, Python code and used datasets are to be submitted by 12
noon on the submission deadline.
• You are allowed multiple submissions till the cut-off date for each of the TWO (2) items.
• Late submission of any of the TWO (2) items will be subjected to mark-deduction scheme
by the University. Please refer to Section 5.2 Para 2.4 of the Student Handbook.
C – How the TWO (2) Items Should Be Submitted
• The Report: submit online to Canvas via TurnItIn (for plagiarism detection).
o please ensure that your Microsoft Word document is generated by Microsoft Word 2007
or higher.
o the report must be saved in .docx format.
• The Python code and used datasets:
o write all your code in one file (.ipynb file)
o compress the .ipynb file and used datasets as a .zip file
o submit the .zip file to Canvas
Avoid using a public WiFi connection for submitting large video files. If you are using public
wireless (WiFi) connection (e.g. SG Wireless at public areas), you might encounter a break in
the connection when sending large files.
Please verify your submissions after you have submitted the above TWO (2) items.
D – Please be Aware of the Following:
Submission in hardcopy or any other means not given in the above guidelines will not be
accepted. You do not need to submit any other forms or cover sheets (e.g. form ET3) with your
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ECA – July Semester 2019
You are reminded that electronic transmission is not immediate. The network traffic may be
particularly heavy on the date of submission deadline and connections to the system cannot be
guaranteed. Hence, you are advised to submit your work early. Canvas will allow you to
submit your work late but your work will be subjected to the mark-deduction scheme. You
should therefore not jeopardise your course result by submitting your ECA at the last minute.
It is your responsibility to check and ensure that your files are successfully submitted to
E – Plagiarism and Collusion
Plagiarism and collusion are forms of cheating and are not acceptable in any form in a
student’s work, including this ECA. Plagiarism and collusion are taking work done by others
or work done together with others respectively and passing it off as your own. You can avoid
plagiarism by giving appropriate references when you use other people’s ideas, words or
pictures (including diagrams). Refer to the APA Manual if you need reminding about quoting
and referencing. You can avoid collusion by ensuring that your submission is based on your
own individual effort.
The electronic submission of your ECA will be screened by plagiarism detection software. For
more information about plagiarism and collusion, you should refer to the Student Handbook
(Section You are reminded that SUSS takes a tough stance against plagiarism or
collusion. Serious cases will normally result in the student being referred to SUSS’s Student
Disciplinary Group. For other cases, significant mark penalties or expulsion from the course
will be imposed.
Find a news article (that is of interest to you) from any trusted sources published in the last
month. Formulate a research question in order to support, object to, or expand on the claim(s)
in the selected news article. Find two (2) or more publicly accessible datasets on the web which
can be used to answer your research question.
You may need to go through the following tasks (a)-(d) multiple times in order to arrive at a
meaningful research question and findings.
(a) Identify the key claim(s) in the selected news article. Identify two (2) or more datasets
that are publicly accessible. Analyse the dataset and answer the following questions.
What kind of information is present in the datasets? How is the data organised and what
common features can be used to relate the datasets? Are there data quality issues in the
datasets (such as erroneous data, missing data, etc.)? Do you need to prepare (such as
clean, transform, or manipulate) the raw data for analysis?
(b) Describe the measures that you need to calculate in order to answer your research
question. Refer to Appendix 1 for an example.
(c) Apply your Python programming skills to generate summary statistics and graphical
plots (or other form of visualisations) to address the stated research question. Explain
your findings and observations.
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ECA – July Semester 2019
(d) Identify some possible limitations of your findings. For example, are they limited to a
certain city or country? Justify your assumptions about the data, if any.
Present your work for tasks (a)-(d) in your report using the template provided in Appendix
2. Provide screenshots of the relevant Python codes for each task and its output(s) where
appropriate. Keep your report concise and coherent as a self-contained entity. The
evaluation criteria also include logical flow of your explanation, variety of the
visualisations employed and appropriate summary statistic used. More marks will be
awarded to answers with in-depth analyses and practical recommendations. Limit your
report file size to a maximum of 4M Bytes.
For a breakdown of the marks, please refer to Appendix 2.
(80 marks)
Write the codes you used for implementing tasks (a)-(d) in an .ipynb file. The program should
have sufficient comments to describe the corresponding steps and analyse the logical flow for
each task.
(20 marks)
Up to 25 marks of penalties will be imposed for inappropriate or poor paraphrasing. For serious
cases, they will be investigated by the examination department. More information on effective
paraphrasing strategies can be found on
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ECA – July Semester 2019
Appendix 1 Proposing Research Questions
Example: Do university towns have their mean housing prices less affected by recessions?
• The following datasets could be used:
• From the Zillow research data site ( there is
housing data for the United States. In particular, the datafile for all homes at a city
level (,
has median home sale prices at a fine-grained level.
• From the Wikipedia page on college towns is a list of university towns in the
United States
• From Bureau of Economic Analysis, US Department of Commerce, the GDP over
time ( of the United States in current
dollars (use the chained value in 2009 dollars), in quarterly intervals.
• You may, for example, compare the ratio of the mean price of houses in university
towns the quarter before the recession starts compared to the recession bottom. A
recession is defined as starting with two consecutive quarters of GDP decline, and
ending with two consecutive quarters of GDP growth. A recession bottom is the
quarter within a recession which had the lowest GDP.
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ECA – July Semester 2019
Appendix 2 Report Template & Marks Breakdown
Title of the ECA project
your name, SUSS PI No. and submission date
Abstract – Briefly describe the dataset, research question, methods, and findings.
(maximum 150 words)
(10 marks)
1. Datasets
Document your work for Task (a) here.
Provide the web links to retrieve the used datasets.
Provide screenshots of the relevant Python code and its output.
(15 marks)
2. Research Question
Provide the web link to retrieve the news article of your interest.
Briefly describe the key claims and supporting reasons in the article. Explain how your
research question supports, objects to or expands the claims in your selected news article.
Document your work for Task (b) here.
(10 marks)
3. Analysis
Document your work for Tasks (c) here.
Provide screenshots of the relevant Python code and its output.
(30 marks)
4. Conclusions
Document your work for Task (d) here.
(10 marks)
(5 marks)
Appendix (optional)