Analytical Tips for visualizing Coronavirus dataset

Artificial Intelligence, Machine Learning, and Data Science are the ruling technology of the future. Before the hype of machine learning, artificial intelligence, and big data analysis, there were statistics. We cannot do or assume the above technology without data. Data is necessary at every step. But can we take any data and start working on it.

Surely the answer is No… First, we have to take the correct data from a better source, understand it, and convert it properly so we can perform an operation on it. For this, we need good data Scientists.

So here are some tips for visualizing coronavirus data which are characteristics of a good data Scientist.

We are in the middle of an information overload about the novel coronavirus, with hourly case updates and endless streams of information. How do you find the signal in the noise? What information do you need to make decisions in the face of this pandemic? visualizations can be powerful tools for communicating information, but can also mislead, misinform, and — in the worst cases — incite panic.

1. Understanding the dataset:- Firstly we have to better understand the dataset and what are the things provided and which are required in the operation. A deluge of COVID-19 data is recorded, but it isn’t always perfect or consistent. A common mistake is to underestimate how these limitations can skew the message of a visualization.

2) What derives from Coronavirus data? :- we need to think of the datasets we can draw from the Coronavirus data you want to visualize. What are some of the common figures in Coronavirus datasets:-

· New Cases

· Cumulative Cases

· Deaths

· Active Cases

· Case fatality ratio

3) What do you consider when choosing the right visualization? :- Choosing the right visualization can oftentimes be an overwhelming decision. You will find yourself with a lot of options to choose from. Firstly, knowing who your audience is will help you to determine what data you need. Knowing what story you want to tell — analyzing the data — tells you which data visualization type to use.

4) Choose the right type of graph for your presentation:- The average person may not know the difference between a bar graph and a line graph, but the distinction between different types of charts can have a huge impact on the clarity of your data.

5) Keep your charts proportional:- In a perfect world, there would be no such thing as a misleading data chart. But, here in the real world, deceptive or confusing graphs are extremely common. The main culprit? Not keeping the graphical presentation proportional to the numerical value of the data.

6) Relationship Types:- The best way to narrow it down is to identify the relationship between the variables you want to highlight. What is the relationship between the variables you want to highlight? Some of the relationship types that you could use include;

· Change Over Time

· Comparison

· Distribution

· Part-to-Whole

· Geospatial

7) What are the design considerations you would make? :- The way you would showcase Coronavirus pandemic data has the potential to shape your audience’s perception of risk and safety. The design choices to make an influence on how your audience interprets your underlying data. This is because, through design, you choose to emphasize certain aspects of the data while potentially obscuring others. While there are no hard and fast rules for making design choices, the following are a few considerations to think about when creating COVID-19 data visualizations;

· The color connotations of your visualization

· Use of linear and logarithmic scales

· Use of relative and absolute values

8) How would you visualize Coronavirus uncertainty? :- It is a fact that there is still a great deal of uncertainty around Coronavirus data, and this is present in the data. You will want to try to find ways to show this uncertainty in the visualization of your choice. For instance, you could choose to add a simple phrase ‘we know of X cases’ to help pass a critical message that the data displayed is not complete.

9) Clarity of your visualization and Keep your charts and your message clear:- Finally, remember that a cluttered graph, map, or scale will not communicate any information. Keep in mind that data and its meaning comes before aesthetics and user experience. A visualization that is not clear will just confuse the viewers. Chart and graph designers can make a number of mistakes that detract from the message they’re trying to convey. For instance, using the wrong color scheme could make it difficult for the audience to easily discern the scale of difference between various subjects. Creating effective data visualizations will help disseminate accurate information with the right message.

10) Summary:- Data can be a powerful ally in the battle against the coronavirus. While most of these visualizations show what’s happening on a large scale, providers in communities can also use the data they have on hand to view trends and see what’s happening at a local level. What’s important is that people have access to data and can easily understand what it’s telling them so they can make better, more informed decisions as a result.