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:

• Write a 200-word abstract to describe a project which applies Python programming to analyze 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.

• 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.

• 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.

• 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 the final concluding statements. The report makes use of a good range of references and is cited properly in a recognized format.

• 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 analyze any control flow logic. Include your full name and PI number at the start of your code.