دانلود کتاب مبانی علم داده

Data science is an evolving area of study that is extensively used in solving real-life problems that are empirical in nature. It is not just about machine learning, statistics, or databases. Instead, it is a comprehensive study of topics that help extract novel knowledge from data, starting with preparing the data, applying suitable intelligent learning models, and interpreting the outcome. Themodels applied are not “one-size-fits-all” and vary with the nature of the data and the applications under consideration. This book presents basic as well as advanced concepts in Data Science, along with real-life applications.We believe this book will provide students, researchers, and professionals at multiple levels with a good understanding of Data Science,Machine Learning, Big DataMining, and analytics. The overwhelming and growing need for data scientists in the industry has created the demand for extensive, easy-to-understand study materials that cover basic concepts to state-of-the-art applications of the concepts of Data Science in different domains. This book includes discussions of theoretical and practical approaches in Data Science, aiming to produce a solid understanding of the field with the ultimate aim of novel knowledge discovery. It contains an indepth discussion on some essential topics for Data Science projects. It starts with an introduction to data types, sources, and generation pipelines. It presents a detailed discussion of various data-preparation steps and techniques. Prepared data are then analyzed through predictive and descriptive data-analysis techniques. The book includes a systematic presentation of many predictive and descriptive learning algorithms, including recent developments that have successfully handled large datasets with high accuracy. It presents a detailed discussion ofMachine-Learning techniques and evaluation methods. Several chapters cover regression analysis, classification techniques, artificial neural networks, and deep-learning models. The cluster-analysis chapter starts with various proximity measures, followed by an introduction to several clustering techniques. The chapter also covers biclustering techniques with cluster-validation measures. The chapter on association-rulemining introduces the basic idea of frequent itemset mining, and a new topic on qualitative and correlation association mining. A unique feature of this book is a chapter on Big Data analysis and Ensemble Learning as emphasized topics. A dedicated chapter on how Data Sciencemethods can be implemented using popular Python libraries is also included in the penultimate chapter. This book will not only be useful for beginners, it will also become a handbook for those already working in Data Science, DataMining, and related areas. The book is organized into 13 chapters. Chapter 1 introduces the basic concepts of Data Science. Chapter 2 deals with data types, storage formats, data sources, and datageneration techniques. A detailed discussion of data preparation or preprocessing techniques is presented in Chapter 3. The basic concept of Machine Learning is presented in Chapter 4. Chapter 5 is dedicated to Regression Analysis. Chapter 6 is on the topic of Classification. Artificial Neural Networks (ANN) and Deep Learning are currently popular and effective techniques, and are discussed in Chapter 7. Classification requires a “best set” of features to work optimally. Feature-Selection techniques presented in Chapter 8 discuss howto find the “optimal” feature set. Chapter 9 deals with Cluster Analysis. Ensemble Learning is a unique topic introduced in Chapter 10. Association-rulemining, another data-analytics technique, is introduced in Chapter 11. Big Data analysis is another unique topic that is presented in Chapter 12. Finally, the book ends with a significant chapter on applications of Data Science in real life and a brief hands-on tutorial on Data Science using Python.