Project 1 - COVID-19 Vaccinations Trend Analysis

Project 1 : COVID-19 Vaccinations Trend Analysis

AIM:

• To identify how different countries are progressing in their vaccination efforts and what factors may be influencing those trends.
• To identify the country which is fast contributing towards the reduction of spread of pandemic, further reducing the disease.

INTRODUCTION:

This case study is focused on analysing the trend of Covid-19 vaccinations over time for different countries. A Covid 19 vaccination dataset of different countries is available. The dataset consists of vaccination details pertaining to people who are fully vaccinated, daily vaccinations etc. of corresponding countries over different time periods.

Using the available dataset, data cleaning and data formatting, is done by power query editor. Moving on, built relationship between the tables so that identifying the patterns between the data will be simpler. Thus, using the visualization tools in Power BI desktop, identified the possible insights and drew a conclusion of the top countries that are contributing towards a fast-pace reduction in pandemic.

PROBLEM STATEMENT:

The main agenda of this case study is to find the countries that are fast contributing towards the reduction of covid-19 pandemic. So that, we will be able to understand the rate of daily vaccinations and reproduction of vaccines. From this analysis, future prediction of trend analysis can also be made.

The covid-19 vaccination dataset contains huge amount of data spread across unorderly and not in a sequence. It also consists of data regarding daily vaccination over a period of three years. The data is made available from different sources across the web. Hence, finding out the potential insights from the dataset is time consuming. In order to overcome this hurdle, data visualization using Power BI desktop is required.

METHODOLOGY:

• Firstly, the dataset was already made available for the case study.
• Data cleaning and formatting was done using power query editor where tasks such as keeping the first row as headers, checking the datatypes and deleting null rows. The cleaned data was then loaded into Power BI.
• Created a calendar table using the DAX function ‘Calendar’. In the same table, new columns of year, month and day was also created.
• Thoroughly, went through the columns and also the online platforms and created a rough idea of how to approach the data.
• Made a rough visualization and drew insights and started creating tables, graphs and charts.
• Thus, top countries were identified which are contributing towards the reduction of pandemic.

ANALYSIS:

In the analysis, few cards are created at the top. Below are the details of the cards present.

• Total no. of countries participated in the survey.
• Total count of people who are fully vaccinated.
• Total vaccinations taken place over three years.
• Average no. of people vaccinated.

A line graph is made to find out the people who are vaccinated by date. A bubble map of total vaccinations by country is been displayed where China emerged to be the largest country. Stacked bar chart is created to find out the top five countries with daily vaccinations. Another stacked column chart is displayed to find the top 10 countries where people are fully vaccinated. A line chart is created in the second page to find the country contributing towards the reduction of pandemic. In this graph, country versus sum of daily vaccinations and sum of daily vaccinations raw is been displayed. A slicer of three consecutive years in the form of list is also been added.

INSIGHTS:

In any case study, figuring out the potential pattern play a crucial role in data visualization. In this case study, keeping in mind the aim that is finding the top countries contributing towards the fast reduction of pandemic, and thus identifying the possible factors is very important.

Considering the dataset, daily vaccination was one of the insights which played a crucial role. With the report of sum of daily vaccination and vaccination raw data, it was easier to draw the conclusion for the case study.

RECOMMENDATIONS:

Further analysis and study can be done with the right dataset. Here, another dataset with factors such as average deaths caused by the pandemic, total no. of cured cases, population density, rate of mortality etc. using these information a better visualization and future trend analysis can be perfectly made.

Data scraping for the case study is also an important step in the analysis. So that the aim of the case study is met properly.

CONCLUSIONS:

From the data visualization and analysis, it was found that 223 countries were part of the covid-19 vaccination report. Different countries emerged to be in the top category for different questions. Hence, finding out the common from those is quite difficult. Following are the result found from this case study.

• In the total vaccinations by date, China is the largest country.
• India, United states and China are the top countries where people are fully vaccinated.
• Chile and Cuba emerged to be the countries in the top list where daily vaccinations are high.
• China and India are the countries where sum of daily vaccinations and daily vaccinations raw are the highest.

From the points above, it can be concluded that in all means and factors China, India and the United States are the countries which are on full drive in the reduction of the pandemic.