Description
Book Synopsis: 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. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics. "This book delivers a systematic, carefully thoughtful material on Data Science." from the Foreword by Witold Pedrycz, U Alberta, Canada.
Details
Are you looking to expand your knowledge in data science? Look no further! Our Data Science Concepts and Techniques with Applications book is the ultimate comprehensive resource for both beginners and advanced learners. With a wide range of topics covered, including data analytics, data mining, and machine learning, this textbook is your one-stop shop for all things data science.
With its organized and easy-to-follow structure, this book takes you on a journey through the fundamental concepts of data science and then delves into advanced techniques and tools. From data pre-processing to deep learning, our book covers it all. Plus, we introduce you to the two main data science programming languages, Python and R, and provide practical examples using the open-source tool WEKA.
Whether you're an advanced undergraduate student, a graduate student, or an industrial practitioner, this textbook is suitable for all levels of expertise. Not only will you benefit from the comprehensive presentation of important topics, but you'll also gain valuable insights from the numerous application examples and the extensive list of further readings. Don't miss out on this opportunity to enhance your skills and stay ahead in the evolving field of data science.
Get your copy of Data Science Concepts and Techniques with Applications today and embark on a journey of knowledge and expertise in the world of data science. Don't wait, start exploring the endless possibilities.
Click here to order your copy now!
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