Critical Analysis on Data Science and Big Data Avenues | Dr. Mirza Baig

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Critical Analysis on Data Science and Big Data Avenues | Dr. Mirza Baig
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In the current scenario of digitalization of volumes, data is used to process and store in a bulky variety. These kinds of volumes of data can be operational and non-operational whereas regular transition for multiple operations is used to perform which to analyze the structuring of data in huge amounts. Regular updating is also a hazardous activity for operational data and regular deposition of past data is helpful for future prospective. Data specification may be extracted with the labeled data called supervised. Unsupervised learning is helpful for identifying equally likely entities for the future perspective. In this research paper, it is being tried to identify that the data is fuel for Data Science, Artificial Intelligence, Machine Learning, and Deep Learning with various equally likely items with the help of suggestive measures of supervised and unsupervised learning of data. The key attribute for handling errors such as environment and performance of error identification is also presented in the Machine Learning security, which shows the importance of the environment for better performance of error management.

Data Science, Big Data, Artificial Intelligence, Machine Learning, Deep Learning, Supervised Learning, Unsupervised Learning, and Reinforcement Learning.


It is the process of extracting useful knowledge and insight from data we collected by using scientific methods dealing with unstructured and structured data, Data Science is a field that comprises everything that related to data cleansing, preparation, and analysis. Data Science is the combination of mathematics, programming, statistics, analytic, data visualization & communication, capturing data in ingenious ways, the ability to look at things differently, and the activity of cleansing, preparing, and aligning the data.


It is one of the mainstreams of Data Science. American computer scientist John McCarthy first coins the term Artificial Intelligence in 1956 at Dartmouth Conference. He defined AI as the science and engineering of making intelligent machines. When the computer is able to perform action usually requires human intelligence, such as speech recognition, decision-making, languages translation, and visual perception [12]. The machine performed like human generating activities and behavior through Digital Intelligence. Types of Human Behavior are Thinking Rationally and Acting Rationally.


In Machines Learning, models are needed to train to behave like humans enabling them to mimic advanced psychological features or functions like decision-making, reasoning, and inferences. It is a subset of AI, which use statistical methods to enable the machine to improve with experiences. It can learn from static methods, which enable it to model the scenario in any mathematical form. ML is required for Navigation, Recognition, Prediction or Description. A good use case for machine learning that has to be performed in our day-to-day activity is spam filters, which characteristically determine whether a text or message is junk based on however closely it matches emails with the same tag.