Difference Between Data Science And Data Visualization

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Introduction Data science: An art of interpretation of the data and bringing insights from the data. It is also a study of observations and interpretation in any form. Data visualization: Representation of the data. Data scientists need tools to deal with the data. What best value can be brought out of it? How can it be broken down? How one parameter is correlated to other? All these questions are answered with one of the solutions - Data Visualization. Best example on our day today basis is amazon's recommendation for a user while shopping. Machine is learning about a user's web activity and interprets and manipulate it thus by giving best recommendation based on your interests and choice of shopping. To provide this recommendation, the …show more content…

They are bound to each other. Data visualization is a subset of data science. Data science is not a single process or a method or any workflow. It is a combined effect of small miniatures dealing with the data. Be it a process of data mining, the EDA, modeling, representation. Example: To portray any incident/story in our daily basis, it could be conveyed as a speech but when it is represented visually, the real value of it will be established and understood. Also, it is not only about representing the final outcome, but also applicable in understanding the raw data. It is always better to represent the data in order to get better insights and how to solve the problem or get a meaningful information out of it which influences the system. To get better understanding of data science and data visualization, Let's say we want to predict what will be iPhone sales for the year 2018 vs google pixel 2018: How exactly one can predict the sales in the future? What are the prerequisites, how confidence is your prediction, what’s the error rate? All these are answered and justified using data science. Prerequisites, 1. Historical data - iPhone sales from the year 2010 - 2017 2. Trend of each year's and the …show more content…

The initial phase of analytics (i.e., Represent the available data and conclude what attributes and parameters to be used in order to build a predictive machine). So here in our example, it is historical data representation, opinion analysis on each iPhone by the users. Two - Outcome phase. The prediction results for the year 2018 has to be represented in a way that it reaches the world. Comparison between phone and google pixel sales for the upcoming years. Back to the iPhone analysis, the historical data has to be analyzed and pick the best attributes that cause significant impact towards the prediction rate (like sales on location wise, season-wise, age ). Followed by picking up the best model (Algorithms like Linear regression, logistic regression, support vector machine – to mention few). Train the model using the historical data and get the prediction for the upcoming year. This is a high level picture on the processes involved in the data science. Once the prediction results for the upcoming year is settled, it can be represented and get some insights that influence other analytics based on the sales and marketing techniques for a

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