Graphical Representation of Data in IT
Graphical representation of data in data analysis is crucial because it enables IT professionals to make sense of vast amounts of data and reach insightful conclusions. In order to make data easier to interpret and evaluate, it is often necessary to present information in a graphical format (Peng et al., 2020). In the information technology sector, different types of graphs are frequently employed, including line graphs, bar graphs, pie charts, and scatter plots. Consistently, trends are displayed throughout time using line graphs. They are frequently employed to monitor performance indicators like website traffic, sales, and user engagement. Secondly, several data points are compared using bar graphs. They are frequently used to measure user engagement, sales numbers, and any other indicator that must be compared across various categories. Pie charts are used to show how the data is distributed. They are frequently used to show market share or the proportion of visitors who utilize various devices to access a website. Lastly, scatter plots are used to show how closely two variables are correlated (Shahapure & Nicholas, 2020). They are frequently employed to show connections between website traffic and earnings or between user engagement and time spent on a website.
The inferences made from graphs depend on the sector and the particular data being examined. Graphs are frequently used in the IT sector to spot patterns, trends, and abnormalities in data. A line graph, for instance, would depict a gradual reduction in website traffic, calling for adjustments to marketing tactics. If a bar graph reveals that one product is selling more than another, judgments about product development and marketing campaigns may be made. In conclusion, a graphical representation is a crucial tool in the IT sector for assisting professionals in making defensible judgments based on data analysis.
References
Peng, Z., Huang, W., Luo, M., Zheng, Q., Rong, Y., Xu, T., & Huang, J. (2020, April). Graph representation learning via graphical mutual information maximization. In Proceedings of The Web Conference 2020 (pp. 259-270). https://doi.org/10.1145/3366423.3380112
Shahapure, K. R., & Nicholas, C. (2020, October). Cluster quality analysis using silhouette score. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 747-748). IEEE. https://doi.org/10.1109/
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Question 
Respond to the following in a minimum of 175 words:
How will you graphically represent data in your future career?
Graphical Representation of Data in IT
What types of graphs are typically used in that industry?
What types of conclusions are drawn from the graphs?