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Data Science: Mindset, Methodologies, and Misconceptions by Zacharias Voulgaris PhD

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Chapter 3 Data Science Methodologies

As mentioned in the previous two chapters, data science is diverse in its applications, which is why the pipeline I described is bound to require some adaptation to the problem at hand. This is because data science lends itself to a variety of different situations. Plus the data itself is quite diverse too, making the potential applications different from one another. So using data science, we can engage in a variety of methodologies, such as predictive analytics, recommender systems, automated data exploration (e.g. data mining), graph analytics, natural language processing, and other methodologies.

Predictive Analytics

Predictive analytics is an umbrella of methodologies, all aiming at predicting the value ...

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