Data visualization is the representation of data or information in form of graphs, charts, diagrams, pictures, etc. Choosing the best visualization could be tricky, as choosing the wrong one may not fully explain your data. Visualizations make your work easy to understand in form of a dashboard or visual representation.
In this article, we listed 4 types of graphs and when to use them:
1. Bar chart: This is among the most used graph as far as data science is concerned. It can mostly be used for categorical data and is often used to compare variables. It uses the x-axis and y-axis; the x-axis to show the categories that are been compared, and the y-axis to show the measured value. Bar charts can be used to compare the sales made by employee A and employee B for 1 year.
2. Scatterplots: This is mostly used for showing the relationship between two numerical variables. It shows the relationship on a vertical and horizontal axis which is the y-axis and x-axis. The data on a scatter plot is dependent on each other, as one value changes, it affects the outcome of the other. E.g. Scatter plot can show the relationship between the sales of a company on the y-axis and the amount spent on advertising on the x-axis.
3. Pie chart: It shows proportions or percentages of different categories of variables. This is preferred when you want to show the percentage of one variable or category in comparison with another variable. E.g. Pie Chart can show the percentage of bills paid by a salary earner on different services. It helps to determine which bill or expense is taking the most money and which is taking less etc.
4. Pictographs: This represents data in form of symbols or images. It can also be in form of infographics (information embedded in graphics) which makes it very interesting for viewers to understand.
Data visualization can be done with a lot of graphs, play around with them and see which best fits the project or business problem you are working on.
