ANL251
End-of-Course Assessment – July Semester 2020
Python Programming
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INSTRUCTIONS TO STUDENTS:
1. This End-of-Course Assessment paper comprises EIGHT (8) 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.
IMPORTANT NOTE
ECA Submission Deadline: 2 September 2020, 12 noon
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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).
● A Jupyter notebook file named “eca.ipynb” & 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 a 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 (“eca.ipynb” file)
o compress the “eca.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 a 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 ECA.
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
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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 Canvas.
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 5.2.1.3). 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.
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Question 1
In the recent COVID-19 pandemic, many disruptions to various aspects of our daily life have been created and show up visibly in many areas when data analytics is applied. You are to identify and investigate one such area that exemplifies the disruptions caused by the virus. Some examples include the economy, society, climate, energy consumption, internet usage, and so on. In this ECA, you are required to formulate a research question and find two or more publicly available datasets that can be used to address the question. Apply the techniques of data processing, visualization and analytics to support your claims and conclusions.
Marks will be awarded according to the following parts:
(a) Write a 200-word abstract to describe a project which applies Python programing to analyse some data in the given context. Use up to 100 words to formulate the research question, and provide the scope and depth of the question.
The research question should require a reasonable breadth and depth of investigation that necessitates the use of multiple datasets (at least two) and various perspectives of the data in order to be addressed adequately. For example, a suitable research question could be: Can the COVID-19 pandemic lead to the downfall of retail REITs in Singapore?
To address this question, you can use datasets that may include consumer spending in malls and online retail, profitability/annual reports of shops/malls, tenant occupancy rates and rents of shop spaces, number of persons employed in the retail sector and REIT stock prices, etc. You could then address the research question from different perspectives provided by the trends and observations generated from the various datasets.
(Refer to Appendix 1 for examples suitable for this assignment.)
(15 marks)
(b) Provide a 300-word description of the datasets, and include details such as data quality analysis and data preparation.
A description of what the datasets contain; how and why they are employed to address the research questions. They should be inspected for data quality issues such as missing values, errors, duplicate values, outliers or extreme values, etc. As different datasets could come in different formats, preparation and pre-processing work such as data cleaning, imputation, transformation and/or merging operations should also be presented. As far as possible, data preparation should be done using Python programming. However, if you face a lot of difficulty in using Python, you can also prepare the data by other means (e.g., using MS Excel), but make sure you give your reasons.
Please make sure that the total size of all datasets is 10MB or less.
(Refer to Appendix 2 for some possible sources of datasets, but not limited to those
sources.)
(15 marks)
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(c) Perform data visualization and analyses.
For analyses of the datasets, summary statistics and visualizations should be generated to illustrate trends, patterns or anomalies to provide an answer to the research question. Logical and thoughtful analyses linking the observations to explanations that address the research question should then be provided. The discussion should lead to sound predictions or practical recommendations. Extra marks are awarded for elucidating important insights that are not obvious or counter-intuitive but have far-reaching consequences.
(30 marks)
(d) Provide Conclusions and References.
The main findings are to be summarized clearly leading to important conclusions of the investigation. The research question is then addressed with final concluding statements. The report makes use of a good range of references and is cited properly in a recognized format.
(15 marks)
(e) Provide Python code in Jupyter notebook.
Develop your code in “eca.ipynb” and submit it along with the datasets used in a single zip folder. The program should also have sufficient comments to describe the code steps, explain any functions used, and analyse any control flow logics. Include your full name and PI number at the start of your code.
(25 marks)
Present your work for parts (a)-(d) above in your report using the template provided (Appendix 3). Note that you will have to keep the size of your word document to 4 MB or less upon submission.
—– END OF ECA PAPER —–
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Appendix 1 Proposing Research Questions
Example 1: Does the COVID-19 pandemic increase unemployment in Singapore?
Too Narrow. A single summary statistic on unemployment rate will answer the question.
Example 2: How will the COVID-19 pandemic affect globalisation?
Too Broad. The range of data required for a comprehensive analysis is too wide. There are too many countries to analyze for the scope of this assignment.
Example 3: Can the COVID-19 pandemic lead to the downfall of retail REITs in Singapore?”
Suitable for this assignment. To address this, the multiple datasets that could be analyzed include consumer spending in malls and online retail, profitability/annual reports of shops/malls, tenant occupancy rates and rents of shop spaces, number of persons employed in the retail sector and REIT stock prices etc. One could then address the research question from different perspectives provided by the trends and observations generated from the various datasets.
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Appendix 2 Sources to look for datasets
● Singapore’s Public Data, https://data.gov.sg/
● Smart Nation Singapore, https://www.smartnation.sg/resources/open-data
● The United Nations, http://data.un.org/
● The World Bank, http://data.worldbank.org/
● The Global Open Data Index http://index.okfn.org/place/
● US Government Data, https://www.data.gov/
● UK Government Data, https://data.gov.uk/
● Canada’s Open Data Exchange, https://codx.ca/
● World Health Organization, http://www.who.int/gho/en/
● Wikipedia, https://en.wikipedia.org/wiki/Main_Page
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Appendix 3 Report Template
Title of the ECA project
your name, SUSS PI No. and submission date
Abstract – Briefly describe the datasets used, research question, methods, and main findings. (maximum 150 words)
1. Research Question
Document your work for part (a) here.
2. Data Preparation and Pre-Processing
Document your work for part (b) here.
Provide screenshots of the relevant Python code and its output wherever appropriate.
3. Data Visualization and Analyses
Document your work for part (c) here.
Provide screenshots of the relevant visualizations to aid your explanations wherever appropriate. For ease of referencing, insert the figures/charts directly before/after the explanatory paragraphs. Do not consolidate your figures/charts in the Appendix.
4. Conclusions
Document your work for part (d) here.
Summarize your findings and identify possible limitations.
References Appendix (optional)
ANL251 Copyright © 2020 Singapore University of Social Sciences (SUSS) ECA – July Semester 2020
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