Data Science Concepts and Techniques with Applications

This book comprehensively covers the topic of data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three sections:

• The first section is an 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 discussion on wide range of applications of data science and widely used techniques in data science.
• The second section is devoted to the tools and techniques of data science. It consists of data pre-processing, feature selection, classification and clustering concepts as well as an introduction to text mining and opining mining.

• And finally, the third section of the bookfocuses on two programming languages commonly used for data science projects i.e. Python and R programming language. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. The book is suitable for both undergraduate and postgraduate students as well as those carrying out 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 areas with the adequate background. The more advanced chapters can be used by postgraduate researchers intending to gather a deeper theoretical understanding.

  Provides details about the fundamental tools and techniques used for data analysis Covers state-of-the-art techniques for data analytics, future research directions and guidance on data analytics Illustrates concepts with simple and intuitive examples, along with step-wise explanations Includes Python and R programming language tutorials for data science Useful advance undergraduate and postgraduate textbook for data science researchers

  Autorentext

  Dr Usman Qamar has over 15 years of experience in data engineering and decision sciences both in academia and industry. He has a Masters in Computer Systems Design from University of Manchester Institute of Science and Technology (UMIST), UK. His MPhil in Computer Systems was a joint degree between UMIST and University of Manchester which focused on feature selection in big data. In 2008 he was awarded PhD from University of Manchester, UK. His Post PhD work at University of Manchester, involved various research projects including hybrid mechanisms for statistical disclosure (feature selection merged with outlier analysis) for Office of National Statistics (ONS), London, UK, churn prediction for Vodafone UK and customer profile analysis for shopping with the University of Ghent, Belgium. He is currently Associate Professor of Data Engineering at National University of Sciences and Technology (NUST), Pakistan. He has authored over 200 peer reviewed publications which includes 3 books published by Springer & Co. He is on the Editorial Board of many journals including Applied Soft Computing, Neural Computing and Applications, Computers in Biology and Medicine, Array. He has successfully supervised 5 PhD students and over 100 master students.
  Dr. Muhammad Summair Raza has been affiliated with the Virtual University of Pakistan for more than 8 years and has taught a number of subjects to graduate-level students. He has authored several articles in quality journals and is currently working in the field of data analysis, big data with a focus on rough sets.

  Inhalt
  Introduction.- Applications of Data Science.- Widely used techniques in Data Science Applications.- Data Pre-processing.- Classification, Basic Concepts.- Clustering.- Text Mining.- Data Science Programming Language.