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Campbellsville University What Is Your Definition of Data Visualization Questions


Analyzing and Visualizing Data!

Please follow the instructions and make sure no plagarisim and I need in APA format and dont forget to add references

after sending me the initial post I will share others for replies

This week our focus is on defining data visualization.  Therefore, after reviewing all of the content this week, answer all of the following questions in the initial prompt.

  1. What is your definition of data visualization?
  2. What are the key components of data visualization?
  3. What techniques do you hope to learn from this course?

Discussion 1

Week 1 Discussion

Kirk (2019) described data visualization as everything related to charts and the activities to choose the appropriate chart for demonstrating the characteristics of data that the user may believe to be relevant. Visualization enables the user to comprehend enormous data in no time and in an enhanced manner. It facilitates in understanding better the data for measuring its impact on businesses and interact visual insight with inward and outward users. Put simply, data visualization is nothing but a graphical representation of data and information. This can be illustrated as an image, diagram, or chart, etc. Utilizing these elements, possibly data visualization may offer an improved means to view and have an understanding of patterns and trends.

Visual representation, visual presentation, and the aim to facilitate understanding are the components involving in the data visualization. Here, representation includes decisions concerning the portrayal of data visually. The constructive blocks of a chart include marks for representing data items such are shapes, lines, or points or attributes that are explained as channels are mark’s visual variations for representing the values related to each. These involve properties like color, size, or position. The visual presentation concerns all the diverse design decisions that constitute the complete anatomy of specific visualization. This involves design preferences like probable use of interactivity, annotation features, etc. The ultimate component involves the objective in facilitating understanding that has various phases namely to perceive, to interpret, and to comprehend (Kirk, 2019).

In context with diverse techniques that I am hopeful to be aware of from this specific data visualization course, that may provide with a broader understanding of contemporary techniques applied for expressing the data visually (Kirk, 2019). Working alongside data, possibly we may come across few statistical techniques that may include univariate, bivariate along multi-variate techniques. During this process, we may come across histograms, charts, treemaps, network diagrams, pie charts, etc.


Kirk, A. (2019). Data Visualization: A Handbook for Data Driven Design. 2nd Edition. ISBN 978-1-5264-6893-2, ISBN 978-1-5264-6892-5 (pbk). Sage Publications.

Discussion 2

Data Visualization

Data visualization includes representing the data graphically and this can be done by using different charts and graphs so that the content can be better presented for the audiences. This can be better applied to understand the trends and patterns in the data are also discovered through this. Relationship with the data is also communicated and data is represented effectively with the use of images. In different ways, data can be delivered to the users and it is better to use and can deliver more change in the system (Gumna et al., 2020). Data can be made more natural with this and key information is provided in a variety of ways.

Some of the key components of data visualization include area charts that will identify the areas in which the charts can be applied. The other one is having clear headings and keys for the data and with this simple analysis can be performed. Better comparisons can be done and summarizing then key points is also a key component of this data visualization. Having consolidated information and using pie charts and bubble charts are useful in it. Lots of data and pieces of evidence are also there as a component in it and adding design elements is another key component of data visualization.

From this course, I hope to learn different techniques such as using area charts and bar charts based on the requirements different kinds of charts are used. This course has changed my view in visualizing the data and effective techniques can be developed with this and more enhancements in the business are seen. Using plots for mapping is also important in this and when large volumes of data are there bubble plots can be used and will maintain better changes in the data (Huestegge & Pötzsch, 2018). Data correlations can also be developed in a great way and a wide range of techniques can be developed with this.


Gumna, J., Zok, T., Figurski, K., Pachulska-Wieczorek, K., & Szachniuk, M. (2020). RNAthor – fast, accurate normalization, visualization, and statistical analysis of RNA probing data resolved by capillary electrophoresis. PLoS ONE, 15(10), 1–12.

Huestegge, L., & Pötzsch, T. H. (2018). Integration processes during frequency graph comprehension: Performance and eye movements while processing tree maps versus pie charts. Applied Cognitive Psychology, 32(2), 200–216.


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