Download Intelligent Techniques for Data Science by Rajendra Akerkar, Priti Srinivas Sajja PDF

By Rajendra Akerkar, Priti Srinivas Sajja

This textbook presents readers with the instruments, concepts and situations required to excel with glossy synthetic intelligence equipment. those embody the family members of neural networks, fuzzy structures and evolutionary computing as well as different fields inside laptop studying, and may assist in picking out, visualizing, classifying and interpreting info to help enterprise decisions./p>

The authors, speak about benefits and disadvantages of other techniques, and current a valid starting place for the reader to layout and enforce info analytic options for real‐world purposes in an clever demeanour. Intelligent strategies for information Science additionally presents real-world situations of extracting worth from information in a number of domain names resembling retail, well-being, aviation, telecommunication and tourism.

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This textbook presents readers with the instruments, innovations and instances required to excel with smooth synthetic intelligence tools. those include the kin of neural networks, fuzzy structures and evolutionary computing as well as different fields inside computing device studying, and may assist in deciding upon, visualizing, classifying and interpreting information to aid enterprise judgements.

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Extra info for Intelligent Techniques for Data Science

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There are many other openings where data science can drive organizations into being smarter, secured and connected. 11 Tools and Techniques Helpful for Doing Data Science For different phases of data science activities, supporting tools and techniques are available. This section describes tools and techniques for various activities such as data acquisition, data cleaning, data munging, modelling, simulation and data visualization. Techniques such as structured interview, unstructured interview, open-ended questionnaire, closed-ended questionnaire, record reviews and observation are collectively known as fact-finding methods.

The most important aspect of success in data analytics is the presentation of analysed data in a user-friendly, re-usable and intelligible format. The complexity of data is adding to the complexity of its presentation as well. Occasionally, simple tabular representations may not be sufficient to represent data in certain cases, requiring further explanations, historical incidences, and so on. Moreover, predictive or statistical analysis from the data is also expected from the analytics tool to support decision making.

The challenge is that most of this data is personal in nature; one should have proper permission and access to the data set generated by such platforms. Further, these data are totally unstructured, redundant and lack standard format. Beyond these difficulties, such data are full of errors and emotions. Even if the challenge of having accessibility to such data is met, another big challenge is to make sense from such data and establish a generic data-based model to promote the intended task. Once these two challenges are solved, such data are useful for a number of applications, including promoting government schemes, getting feedback on product, service and employees (evaluation and promotion), establishing e-learning, mobile learning and personalized learning projects on such platforms, generating innovative ideas for development, and troubleshooting and online assistance.

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