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The objective of this article is to give readers insight into what data visualization is, its techniques, its peculiarity, its tool for visualising and its Usage. Do have a good read!
What is data Visualization?
Simply graphical storytelling, to elaborate, is the process of envisioning facts from analysed data to give insight, give explanatory reports and help in decision making. Using specialized software, data visualization give direction on compatible and counteracting variables, it shows the relationship between these variable and helps reach a meaningful conclusion.
I will say without data visualization, an analytical modelling process isn’t complete. In other words, data visualization is the salt of an analytical modelling process, let’s dive in!
The field in the use of Data visualization:
All data-based fields make use of data visualization continuously as it takes a core part of their work process, to mention, we have:
● Data Science
● Data Analysis
● Business Intelligence
● Data Specialist
● Market Specialist
● Business Analyst
● Data Engineer
Visualization Techniques:
Before designing graphics some key factors are considered, these considerations are involved in data visualization as it has been defined as graphical storytelling. Below are the techniques to put in place to have a good and relevant visualization.
● Pen things down: This is you making a mind mapping of how you want your visualization to be considering the data at your disposal and the objective of your analysis.
● Create a rough Overview: This is essential, at this stage, you sketch a rough look of how you want the visualization to look like, pick your chart, fix the labels and repeat the process all over until you get a suitable visualization pattern to settle for.
● Interact with the variables: Explore your data to know which variables you are considering in your visualization.
● Choose the perfect chart: There are different types of charts for different purposes, which will later be discussed, study the chart types to know the one that perfectly fits your visualisation output.
● Choose your colour wisely: This is a crucial technique, you have to consider blending colours and your audience, and make sure the colour you choose answers the questions what if one of my audience is short- blinded? What if one of my audience has colour blindness?
● Perfect your visualization: After considering the above techniques, come up with the final graphics and ensure you’ve added the sauce(legends, labels, and title) to the subject to review.
● Give a report: Reporting your visualization helps you understand your choice of charts better.
Types of Visualization
There are finitely many types of charts for visualization, some are the common ones we are familiar with right from basic mathematics, but we have more to it and each of them can be explored for further discovery and usage, the chart is segmented based on their uses, let’s have the ride below!
Graphs for the purpose of balancing, differentiation, and comparison: These are charts that are used for comparing our variables with one another to see how that balance with each other, and answer the questions, which variable has the highest quantity, which variable is relevant? Which one isn’t relevant? Examples of this type of chart are Bar charts, histograms, and table charts.
Graphs for the purpose of showing relationships and interactions: These are charts that are used to show the interactions between variables, this helps us conclude our dependent variable and the independent ones, and also helps to identify the variable that isn’t needed in the analysis. Examples of this type of chart are the Venn diagram, Heat map, and tree diagram.
Graphs for the purpose of showing patterns and paths: These are charts that are used to show the path of variables in the analysis, this will help greatly in discovering outliers(Out of bound data points), and choosing the right model for a model fitting analysis. Examples of this type of chart are a Scatter plot, box plot, count plot, and line graph.
Graph for the purpose of showing proportions and percentages: These types of charts are used to show the portion of entries in a column or columns, it helps to give quantities to our data set. Examples of this type of chart are Pie chart, doughnut chart, bubble sort, and pictogram.
Graph for the purpose of showing range and rank: These charts are used for displaying the range of variables. Examples of these charts are the Gantt chart, Bullet chart, and Error chart.
There are other charts for visualization as they aren’t limited to the ones mentioned above.
Tools for Visualization
There are many tools for visualization, and to generalize it, all analytic tools are used for visualization, while some are specialized for visualization. Below is the partitioned list.
Analytic Tools
● Excel
● Spss
● Python
● R
● SAS
● Power BI
● Tableau
Specialized Visualization Tools
● ReDash
● Power BI
● Chart.js
● Tableau
● Google charts
● dygraphs
● Data Wrapper
These tools are open for exploitation for further learning.
Visualization Hacks
Do you know that the most used chart for visualization is a Pie chart?
Do you know that one of the most uncommon charts for visualization is the Sankey chart?
Do you know that the best colour combos for visualization are warm colours and blue?
Do you know that one of the best visualization tools is tableau?
Do you know that Tongston’s introduction to data science course has a section on practical data visualization? interested? Click here.
Conclusion.
I hope you enjoyed reading the article, do well to watch out for more.
