Practical connection data visualization
Data visualization is a part of statistical analysis in which data is presented visually. In other words, it’s a graphical representation of raw facts and figures. Some of the visuals mostly applied to perform data visualization are maps, graphs, and charts. All these elements highly depend on the type of data one is working on, either continuous or categorical. For example, categorical data would be better visualized using bar charts, while continuous variables could be better represented by a scatter plot (Thornton, H. R., 2019).
This data visualization course will be important to me as it will help me reduce numerical analysis, which is a bit tiresome and require a lot of criterion in interpreting. Being a statistical analyst, I will pass information to staff assigned under me in a visualized manner. This will be important as there will be more understanding of directives to the staff, and the company will have improved. Being a senior analyst, I will be able to train my workers and build their knowledge. Workers’ knowledge plays a major role in every company’s success (Han, J., & Wang,2019).
I will also use this course to help me build a better profile in social academic networks like GitHub and LinkedIn. My projects in these networks will involve knowledge of data visualization. This would be important to me as it will be included in my resume if I wanted a job or an analysis tender with companies. Employers will have a look at my profile, and there will be evidence of sound knowledge of data visualization, which is practiced by almost all companies. Again, having a vision of starting my own statistical consultation company, I will gain the required knowledge to create a perfect profile on my website. I will post a few projects done that’s deals with data visualization on the website. This will attract more customers, as it is known that data visualization always creates the first image in any data analysis (Liu, S.,2019).
Generally, learning data visualization will always be of certain benefit. Statistical analysis can never be done numerically alone but will always need some visuals; however, data visualization can be used alone without any numerical analysis in some cases where time is a factor.
Reference:
Han, J., & Wang, C. (2019). TSR-TVD: Temporal super-resolution for time-varying data analysis and visualization. IEEE Transactions on Visualization and Computer Graphics, 26(1), 205-215.
Liu, S., Wang, D., Maljovec, D., Anirudh, R., Thiagarajan, J. J., Jacobs, S. A., … & Peterson, L. (2019). Scalable topological data analysis and visualization for evaluating data-driven models in scientific applications. IEEE Transactions on Visualization and Computer Graphics, 26(1), 291-300.
Thornton, H. R., Delaney, J. A., Duthie, G. M., & Dascombe, B. J. (2019). Developing athlete monitoring systems in team sports: Data analysis and visualization. International journal of sports physiology and performance, 14(6), 698-705.