Definition Of Data Visualization Study Essay

Definition Of Data Visualization Study Essay

Instructions/Descriptions

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Question

For this discussion, please use your weekly readings to compose a response to the following prompt/question(s):

What is your definition of Data Visualization? How has your past knowledge or experiences helped you to develop your definition?

And please write responses for below posts:

Misleading data can be defines as “The data provided to an user is essentially abused intentionally or unintentionally so that the actual representation of analyzed data is not real or accurate.

Data can be misled through many ways:

1) Representation of data: Due to many reasons, such as limitation with tools, the representation of the data might not be accurate.

2) Data Availability: There also another way that the necessary data is not available to provide accurate information.

3) Skills: Its is also important that properly analyzed data needs to be used. lack of necessary skills in analyzation is also a reason for misleading the data.

The picture above represents a company’s  Chips and Boards “Sales” in four regions.

the boards sales are more than chips sales except in south region.

The components in the graph are sales is represented in $ on Y axis and the 4 regions represented on X-axis with a legend of Chips and Boards. The title is properly mentioned in the char as sales by region.

One can understand from the graph that how the company sales is over the 4 regions. but the exact values of the sales over 4 regions is not represented in the graph.

How do you think the graph below can be considered to be misleading?

The picture above represents a ticket sales for a foot ball game where the maximum tickets 300 are sold in January.

The picture is not representing the year of the sale. So it is misleading the user in understanding the following points

1) Which year the tickets are sold

2) what is the exact number of tickets sold in different months.

Data May be misleading through Data May be misleading through being excessively complex or poorly constructed. being excessively complex or poorly constructed. The estimations are the maltreatment – intentional or not – of a numerical Information. The results give a beguiling of information to the beneficiary, who by then thinks something inaccurately in case the person in the inquiry doesn’t see the error or doesn’t have the full data picture. Given the essentials of data in the present a remarkable estimation are the maltreatment – conscious or not – of a numerical Information. The results give a beguiling of information to the beneficiary, who by then thinks something erroneously if the person in the inquiry doesn’t see the slip-up or doesn’t have the full data picture. Given the centrality of data in the present rapidly progressing mechanized world, it is basic to be alright with the fundamentals of beguiling bits of knowledge And oversight. As action in due productivity, we will review likely the most outstanding sorts of maltreatment of bits of knowledge, and diverse upsetting (and deplorably, standard) misleading estimation models from open energetic progressing electronic world, it is basic to be alright with the fundamentals of beguiling bits of knowledge And oversight. As movement in due innovation, we will review likely the most outstanding kinds of maltreatment of bits of knowledge, and distinctive upsetting (and grievously, standard) misleading estimation models from open life.

Data can be misleading in the following ways:

1. Non exposure of data that is important and vital
2. Presenting unnecessary data
3. Graphing any false information

Part 2:  Study the components of a traditional graph as pictured below.

The above graph represents sales of chips and boards value in dollars by each region.

The above Football Game Ticket The sales graph represents tickets sold in each month from September to January. The tickets sold value labels are not showed in proper increments i.e.

The value labels increments shown from 0 to 250 and 250 to 300 are not the same. This improper partition or increments representation of tickets sold value is misleading and forces us to the the conclusion that the sales in January are far better than December but, there is only a little different if we plot the Sales Value axis from 0 to 250 and 250 to 500.

References:

Andy Kirk. (2016). Data Visualization: A Handbook for Data Driven Design. SAGE Publications Ltd.

Mona Lebied. (2018). Misleading statistics and data. Retrieved from https://www.datapine.com/blog/misleading-statistics-and-data/

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