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Data presentation - types & its importance, what is data presentation.
Data Analysis and Data Presentation have a practical implementation in every possible field. It can range from academic studies, commercial, industrial and marketing activities to professional practices.
In its raw form, data can be extremely complicated to decipher and in order to extract meaningful insights from the data, data analysis is an important step towards breaking down data into understandable charts or graphs.
Data analysis tools used for analyzing the raw data which must be processed further to support N number of applications.
Therefore, the processes or analyzing data usually helps in the interpretation of raw data and extract the useful content out of it. The transformed raw data assists in obtaining useful information.
Once the required information is obtained from the data, the next step would be to present the data in a graphical presentation.
The presentation is the key to success. Once the information is obtained the user transforms the data into a pictorial Presentation so as to be able to acquire a better response and outcome.
Methods of Data Presentation in Statistics
1. pictorial presentation.
It is the simplest form of data Presentation often used in schools or universities to provide a clearer picture to students, who are better able to capture the concepts effectively through a pictorial Presentation of simple data.
2. Column chart
It is a simplified version of the pictorial Presentation which involves the management of a larger amount of data being shared during the presentations and providing suitable clarity to the insights of the data.
3. Pie Charts
Pie charts provide a very descriptive & a 2D depiction of the data pertaining to comparisons or resemblance of data in two separate fields.
4. Bar charts
A bar chart that shows the accumulation of data with cuboid bars with different dimensions & lengths which are directly proportionate to the values they represent. The bars can be placed either vertically or horizontally depending on the data being represented.
5. Histograms
It is a perfect Presentation of the spread of numerical data. The main differentiation that separates data graphs and histograms are the gaps in the data graphs.
6. Box plots
Box plot or Box-plot is a way of representing groups of numerical data through quartiles. Data Presentation is easier with this style of graph dealing with the extraction of data to the minutes of difference.
Map Data graphs help you with data Presentation over an area to display the areas of concern. Map graphs are useful to make an exact depiction of data over a vast case scenario.
All these visual presentations share a common goal of creating meaningful insights and a platform to understand and manage the data in relation to the growth and expansion of one’s in-depth understanding of data & details to plan or execute future decisions or actions.
Importance of Data Presentation
Data Presentation could be both can be a deal maker or deal breaker based on the delivery of the content in the context of visual depiction.
Data Presentation tools are powerful communication tools that can simplify the data by making it easily understandable & readable at the same time while attracting & keeping the interest of its readers and effectively showcase large amounts of complex data in a simplified manner.
If the user can create an insightful presentation of the data in hand with the same sets of facts and figures, then the results promise to be impressive.
There have been situations where the user has had a great amount of data and vision for expansion but the presentation drowned his/her vision.
To impress the higher management and top brass of a firm, effective presentation of data is needed.
Data Presentation helps the clients or the audience to not spend time grasping the concept and the future alternatives of the business and to convince them to invest in the company & turn it profitable both for the investors & the company.
Although data presentation has a lot to offer, the following are some of the major reason behind the essence of an effective presentation:-
- Many consumers or higher authorities are interested in the interpretation of data, not the raw data itself. Therefore, after the analysis of the data, users should represent the data with a visual aspect for better understanding and knowledge.
- The user should not overwhelm the audience with a number of slides of the presentation and inject an ample amount of texts as pictures that will speak for themselves.
- Data presentation often happens in a nutshell with each department showcasing their achievements towards company growth through a graph or a histogram.
- Providing a brief description would help the user to attain attention in a small amount of time while informing the audience about the context of the presentation
- The inclusion of pictures, charts, graphs and tables in the presentation help for better understanding the potential outcomes.
- An effective presentation would allow the organization to determine the difference with the fellow organization and acknowledge its flaws. Comparison of data would assist them in decision making.
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The Organization and Graphic Presentation of Data—23 A proportion is a relative frequency obtained by dividing the frequency in each category by the total number of cases. To find a proportion (p), divide the frequency (f) in each category by the total number of cases (N):p ¼ f N where f = frequency N = total number of cases Thus, the proportion of foreign born originally from Latin America is
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5. Histograms. It is a perfect Presentation of the spread of numerical data. The main differentiation that separates data graphs and histograms are the gaps in the data graphs. 6. Box plots. Box plot or Box-plot is a way of representing groups of numerical data through quartiles. Data Presentation is easier with this style of graph dealing with ...
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Choose the best metric for your analysis and your audience. Choose your chart to fit your question rather than your question to fit your chart. Let the data tell its story without excess clutter or distraction. Keep the focus of the visualization on the data. Make sure all use of font, color, shading, and text enhance rather than distract.
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