Azeez Olanrewaju Shoderu
Quantitative Data Analysis Module Task 6 Discussion University of East London, UK through UNICAF Scholarship
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Introduction
The knowledge of data analysis or science is not complete without the technical know-how of data visualization. It will not be out of place to state that a data analyst, scientist, AI/ML engineer, etc will not be fit to be called a professional if they lack the ability to communicate effectively through images and other visual aids their analyzed data and drawn insights.
Data Visualization
Data visualization is none but a method of result expression. In the words of Sanchez (2020), data visualization is “the graphical representation of information and data”. Thus, it applies various computer graphic effects to show the insights, correlation and connections embedded in the analyzed dataset.Moreso, data visualization is done through the use of visual analysis software that portray data illustratively while removing unwanted details and focusing on important data aspects in order to aid humans cognition to easily and quickly grasp the paper or computer information without necessarily employing crude data presentation methods like tables (Few, 2007). Examples of computer software and tools used to implement data visualization include programming languages like python, R, tableau, matlab, javascript among others.This result-driven field of data science emphasizes on the simplification of information sharing amongst analysts, business leaders and the general public. Hence, it makes use of layman techniques to present outcomes of data analysis. Some of these methods are the application of tables, maps and graphs which are mostly common and easy to understand by everyone. Though, the often utilized data visualization techniques include line graph, scatter plot, pie and bar chart (Sadiku et al, 2016).Table
From past knowledge from statistics or elementary data science, it can be deducted that a table is a traditional method of collecting or analyzing the information in a dataset through the use of rows and columns. The values represented in a table are always firstly put at the top and left hand side of it to denote the names or labels given to the variables that come after. This is the underlying representation of data portrayed in Excel sheets, Google sheets, etc.
Map
A map is a visualization tool used to show the data in form of hierarchy, geographical areas and related aesthetics. There are several types of maps in data visualization including administrative maps, bubble maps, heatmaps, statistical maps, trajectory maps and their categories among which are dynamic maps, 2D maps, 3D maps or static maps, interactive maps (Chou, 2019).
Graph
A graph can be any pictorial tool used to show the relationship amongst units in a dataset. It helps infer the noticeable difference in various parts or aspects of data that may have occurred over time. Instances of graphs in data visualization are bar graph, line graph, pyramid graph, and so on. These crude graphs along with other data visualization techniques are transformed to high quality visuals not by analysts but through softwares like Microsoft Excel, Microsoft Word, Spreadsheet, PowerPoint (Gandhi and Pruthi, 2020).
Conclusion
With so many techniques to choose from, knowing how well to use them and which are really important to the data analysis at hand is crucial. Not every one of them is useful to all projects. It is imperative to understand the current work and use only that which will make the results clearly understood. Since this is the communication stage, presenting ideas and insights properly is much more pertinent than just trying to impress the audience.
Reference List
Chou, L. (2019, August 1). Top 10 map types in data visualization. Towards data science. https://towardsdatascience.com/top-10-map-types-in-data-visualization-b3a80898ea70
Few, S. (2007, January 10). Data visualization past, present, and future. Perceptual Edge. https://www.perceptualedge.com/articles/Whitepapers/Data_Visualization.pdf
Gandhi, P., and Pruthi, J. (2020). Data Visualization Techniques: Traditional Data to Big Data. 53 S. M. Anouncia et al. (eds.), Data Visualization. Springer Nature Singapore Pte Ltd. https://doi.org/10.1007/978-981-15-2282-6_4
Sanchez, J. R. (2020). Data X: Data visualization. Berkeley: SCET.
Sadiku, M. N. O., Shadare, A. E., Musa, M. S., and Akujuobi, C. M. (2016). Data visualization. International Journal of Engineering Research and Advanced Technology (IJERAT). 12. 2454-6135.