Parikshit Narendra Mahalle - Foundations of Data Science for Engineering Problem Solving (Studies in Big Data, 94)

Senses (Sense-Organs) are purified by water; Mind is purified by truth; Soul is purified by learning and penance; while intelligence is purified by knowledge.

Sanskrit Shubhashitani

The book Foundations of Data Science for Engineering Problem Solving is envisioned to present the detailed and comprehensive overview of data science foundations including data science evolution, data collection, preparation, analysis of data using machine learning algorithm, data visualization and how data science can make better insights into various use cases in science and engineering. Since the last decade, there is much advancement in very large scale integration technology and the semiconductor industry making electronic wearable devices, and all Wi-Fi-enabled devices cheaper and tiny having functionalities of sensing, computing and communication. In addition to this, the Internet is also available at a more faster and affordable cost as compared to the past. Due to these reasons, the data generated by these devices and its posting on the cloud is increasing at a very faster rate. The data has become big in terms of volume, variety, velocity and complexity, and all information technology leaders are facing the problem of how to deal with this big data.

The book focuses on how data science can enrich the applications of the science and engineering domain for making it smarter. The main objective of this book is to help readers to understand how this evolving field of data science is going to be useful in forecasting, prediction, estimation and recommendation. The entire notion of the book is exploring foundations of data science from the basics to applications followed by case studies in science and engineering. The entire book is mainly divided into three parts. The first part of the book deals with Big data and its emergence in todays context, data science basics, its evolution and need for today, and various applications. Data collection and preparation is the main part of any data science application which includes data exploration, various types of datasets, their classification based on the sources and types, data preprocessing phases and different tasks involved; web scrapping tools like Beautiful Soap, Scrapy and URLLIB are presented and discussed in this part of the book.

The next part of the book covers the important topic of data visualization, its need, challenges, respective tools and modelling of data. Visualization tools like Tableau, Matplotlib, Looker, Seaborn, PowerBI, IBM Cognos Analytics and their functioning are also discussed in detail in this part of the book. The process of data modelling, impact of modelling on the outcomes, decision making process as well as the role of data science in engineering problem solving are also elucidated in this part of the book. The main objective of this part of the book is to focus on various emerging tools and techniques adapted in industry for data science applications to enhance business intelligence.