IFN509: COVID-19 - Data Manipulation and Cleansing - Finance Assignment Help

Assignment Task:

Overview 
COVID-19 (previously known as "2019 novel coronavirus") is dominating the news. As you are well aware, this novel virus emerged a few months ago and is spreading around the world. While it is a major public health emergency, it also provides us with an excellent learning opportunity. Scientists and researchers across the world are collecting and analyzing data on the virus. Most of it is publicly available. 
In this assignment, you will work with a portion of that data. You will use this partial dataset to demonstrate your skills on a number of tasks, detailed below. 

• The data was made available by Johns Hopkins University, strictly for educational and academic research purposes. They rely on publicly available data from multiple sources, that do not always agree. This is worth keeping in mind in your analysis. 
• You can download the data from Blackboard, in the Assignment Item 1 folder, under Assessment. You will get information on all reported cases up until March 8th. The data is organized in daily CSV files, from January 22nd to March 8th. 
• To ensure that you all work with the same dataset, it is important that you download it from Blackboard, 
• rather than trying to access the data directly from Johns Hopkins. After the assignment is completed, you will still be able to extend your analysis by accessing new data if you want to. The data on Blackboard is simply a snapshot. 
• This is the first time we are experiencing COVID-19, and obviously the first time we are running this assignment. While the intent and objectives of the assessment item will not change, details may evolve as new information comes to light. Any changes to the details will be communicated via Blackboard and Slack. This may be a release of additional data, or some tweaks to specific tasks. 

Tasks 
1. Data manipulation and cleansing 
The dataset is structured in a way that makes updates very convenient: every day, a new file is simply added to the repository. In the snapshot we are using for the assignment, this means working with 47 separate CSV files. This is not suitable for an efficient analysis. You need to represent your data in memory in a way that will make your analysis possible. In doing so, you need to keep in mind that: There may be inconsistencies in the formats used, or errors in the data. How are you handling that? It is good practice to separate code and data. Your solution has to work for these 47 files, but should also be usable if you had 32 files, or 51, or 64. This is especially important as we may end up using an expanded dataset, as explained earlier. 

2. Descriptive analysis and initial data exploration 
Please address the following questions: 
How many countries have reported at least 10 cases? Which are the five countries with the highest number of active cases? What is the current rate of increase in the total number of cases, based on the last week of data? 

3. Data normalization 
The data is reported by country (and sometimes by state or province), as well as by date. This is a useful starting point, but this has two limitations: 
Not all countries have the same population. 5,000 cases in a country with 6 million inhabitants is not the same as 5,000 cases in a country with 95 million inhabitants. The outbreak did not start on the same day in every country. On any given day, one country might be at the peak of the outbreak, while in another community transmission is only just starting to occur. 

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