Название: Data Science Concepts and Techniques with Applications, 2nd EditionАвтор: Usman Qamar, Muhammad Summair Raza
Издательство: Springer
Год: 2023
Страниц: 492
Язык: английский
Формат: pdf
Размер: 13.3 MB
This textbook comprehensively covers both fundamental and advanced topics related to Data Science. Data Science is an umbrella term that encompasses data analytics, data mining, Machine Learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to Data Science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in Data Science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, Deep Learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main Data Science programming languages, and shows in a completely new chapter practical Data Science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different Machine Learning and data mining tasks. An appendix explaining the basic mathematical concepts of Data Science completes the book.
The textbook illustrates concepts with simple and intuitive examples, accompanied by a step-by-step explanation. It provides numerous examples using real-world data, supports understanding through hands-on experience of solving Data Science problems using Python and R programming language, reviews a range of applications of Data Science, as well as provides supplementary code resources and data.
TensorFlow is a Python library for fast numerical computing developed and maintained by Google and released under an open-source license. Irrespective of the developer’s level of expertise, Theano includes the tools and libraries which help to develop the ML and DL algorithms effectively. It is also very flexible and can easily be deployed over a range of systems including but not limited to single-core processors, multicore processor systems on chips (MPSoCs), Graphical Processing Units (GPU), Tensor Processing Unit (TPU), mobile systems, and even over the distributed systems. With this library, it is also very easy to acquire data, model, train, and serve predictions. For user convenience, it can be used with Python or jаvascript-based application programming interface (API) for application development. For resource-constrained systems such as mobile computing and edge devices, a library that requires limited resources was also developed and referred to as TensorFlow light.
The book is suitable for both undergraduate and postgraduate students as well as those involved in research in Data Science. It can be used as a textbook for undergraduate students in computer science, engineering, and mathematics. It can also be accessible to undergraduate students from other disciplines. The more advanced chapters can be used by researchers intending to gain a solid theoretical understanding. The book can also be used to develop new courses in Data Science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines. Although this book primarily serves as a textbook, it will also appeal to practitioners and researchers.
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