This data set that I had chosen is the data set of global air pollution. It contains information about air quality measurements from various countries around the world. The data is typically collected by air quality monitoring stations that measure concentrations of various air pollutants such as carbon monoxide (CO),ozone(O3), nitrogen dioxide (NO2), and particulate matter (PM2.5). This dataset has total of 12 columns. AQI in the data set stands for Air Quality Index,which is a measure of how polluted the air is in a specific location. It is a standardized measurement used by governments and organizations around the world to provide a simple and consistent way of communicating air quality levels to the public. The AQI takes into account measurements of various air pollutants such as PM2.5, ozone, carbon monoxide and nitrogen dioxide. Based on these measurements, the AQI is calculated and reported as a numerical value ranging from 0 to 500. The higher the AQI value, the greater the level of air pollution and the greater the health risks associated with exposure.
To know more about it view the pdf below:
Tableau is a powerful and popular data visualization and business intelligence tool that enables users to create interactive and insightful dashboards, reports, and charts. It was first introduced in 2003 by a company called Tableau Software, and has since become widely used across industries for its user-friendly interface, ability to connect to multiple data sources, and advanced analytics capabilities. Tableau allows users to connect to a variety of data sources such as spreadsheets, databases, and cloud-based platforms. Once connected, users can manipulate and analyze data using a drag-and-drop interface to create interactive visualizations. Tableau also offers a wide range of visualization options including heat maps, scatterplots, histograms, and maps, among others.
Tableau is a strong data visualization tool.Out of all the other tool I was more comfortable with Tableau.It is very easy to use and understand.One of the perks of this is that most of the visualization are already given in tableau such as pie char, bar graph, etc. You can simply include your dataset and it automatically recognizes all column. Now you just need to drag those columns name such that they fit the pre defined requirements if you are using any of the visualization given by the tableau. Out of all the other tools, tableau was incredibly good.
D3, short for Data-Driven Documents, is a JavaScript library used for creating interactive and dynamic visualizations for the web. It was created by Mike Bostock in 2011 and has since become a popular tool for data visualization and web development. D3 allows users to bind data to HTML or SVG elements, and manipulate them using a powerful set of data-driven methods. This makes it possible to create highly customized and interactive visualizations that can respond to user interactions and changes in data. D3 also provides a wide range of visualization options including bar charts, line charts, scatterplots, and more. These visualizations can be enhanced with animations, transitions, and user interactions to create dynamic and engaging data visualizations.
D3 is a really powerful tool, but it was very hard for me use since I was not familiar with it. I hardly new any syntax about it and found it really hard to work with. Out of all the other tool it was the most toughest tool I have worked with. It was very hard for me to even create a simple bar graph using this , which I could have done in less than 5 min using tableau. I know from my research on d3 that you can create some amazing and complicated things using this, but personally I found it hard to work with.
Python is a popular programming language that is widely used for data analysis and visualization. There are several libraries in Python that can be used for data visualization, including:
Matplotlib: Matplotlib is a 2D plotting library that provides a range of visualization options including line plots, scatter plots, histograms, and more. It is a widely used library for data visualization in Python.
Seaborn: Seaborn is a library that is built on top of Matplotlib and provides additional visualization options including heatmaps, violin plots, and categorical plots.
Plotly: Plotly is a library that provides interactive visualization options including scatter plots, line charts, and bar charts. It also allows users to create dashboards and other interactive visualizations.
Bokeh: Bokeh is a library that provides interactive visualization options for web browsers. It allows users to create interactive visualizations using Python code and provides a range of options for customization.
Python is language that I was already familiar with so obviously I found it easy to work with compared to d3.But I still prefer Tableau over Python. Though having quiet a bit experience on python , I had no idea this type of visuvalization was possible with it. It was a bit of new thing for me to work with the new libraries, but I got used to them pretty quick and it was not as difficult as d3.
Gephi is an open-source and free software application used for visualizing and exploring complex networks and graphs. It was first released in 2008 and is now widely used in various fields, including social sciences, biology, and computer science. Gephi provides a user-friendly and interactive interface that allows users to import, manipulate, and visualize data in the form of nodes and edges. Nodes represent the entities in the network, while edges represent the connections or relationships between them. The data can be imported from various sources such as CSV files, spreadsheets, and databases.
Gephi was something that was not so hard,but it wasn't easy either. First I had no idea about gephi and just started to run it by simply importing my dataset and I did got some pretty weird visuvalization. Then I got to know that basically it works on the concept of graphs that include nodes and edges. And one more thing I got to know was that we should be sending our dataset in two columns, source and target. Then I got a graph visuvalization that included both nodes and edges.The above graph shows graph of country and their AQI Category.