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Hands-On Introduction to Data Science with Python
Students will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool Python, a new chapter on using Python for statistical analysis, and a new chapter that demonstrates how to use Python within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.
R 2,320.00
Hands-On Introduction to Data Science with Python
Students will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool Python, a new chapter on using Python for statistical analysis, and a new chapter that demonstrates how to use Python within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.
R 6,415.54
Data Science Using Python and R
Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.
R 3,743.00
Hands-On Introduction to Data Science
This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.
R 2,139.00
R 493.00
R 428.00
Hands-On Introduction to Data Science with R
Students will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool R, a new chapter on using R for statistical analysis, and a new chapter that demonstrates how to use R within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.
R 6,358.10
Hands-On Introduction to Data Science with R
Students will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool R, a new chapter on using R for statistical analysis, and a new chapter that demonstrates how to use R within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.
R 2,320.00
Introduction to Clustering Large and High-Dimensional Data
There is a growing need for a more automated system of partitioning data sets into groups, or clusters. For example, digital libraries and the World Wide Web continue to grow exponentially, the ability to find useful information increasingly depends on the indexing infrastructure or search engine. Clustering techniques can be used to discover natural groups in data sets and to identify abstract structures that might reside there, without having any background knowledge of the characteristics of the data. Clustering has been used in a variety of areas, including computer vision, VLSI design, data mining, bio-informatics (gene expression analysis), and information retrieval, to name just a few. This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and show recent research for more advanced audiences.
R 2,001.85
Beginning Microsoft SQL Server 2012 Programming
Get up to speed on the extensive changes to the newest release of Microsoft SQL Server The 2012 release of Microsoft SQL Server changes how you develop applications for SQL Server. With this comprehensive resource, SQL Server authority Robert Vieira presents the fundamentals of database design and SQL concepts, and then shows you how to apply these concepts using the updated SQL Server. Publishing time and date with the 2012 release, Beginning Microsoft SQL Server 2012 Programming begins with a quick overview of database design basics and the SQL query language and then quickly proceeds to show you how to implement the fundamental concepts of Microsoft SQL Server 2012. You'll explore the key additions and changes to this newest version, including conditional action constructs, enhanced controls for results paging, application integration with SharePoint and Excel, and development of BI applications. Covers new features such as SQL Azure for cloud computing, client-connectivity enhancements, security and compliance, data replication, and data warehouse performance improvements Addresses essential topics including managing keys, writing scripts, and working with store procedures Shares helpful techniques for creating and changing tables, programming with XML, and using SQL Server Reporting and Integration Services Beginning Microsoft SQL Server 2012 Programming demystifies even the most difficult challenges you may face with the new version of Microsoft SQL Server.
R 912.00
Wiley Pathways Introduction to Database Management
You can get there Where do you want to go? You might already be working in the information technology field and may be looking to expand your skills. You might be setting out on a new career path. Or, you might want to learn more about exciting opportunities in database management. Wherever you want to go, Introduction to Databases will help you get there. Easy-to-read, practical, and up-to-date, this text not only helps you learn fundamental database design and management concepts, it also helps you master the core competencies and skills you need to succeed in the classroom and in the real world. The book's brief, modular format and variety of built-in learning resources enable you to learn at your own pace and focus your studies. With this book, you will be able to: * Appreciate the key role of data in daily business operations and strategic decisions. * Understand databases, database management systems, and SQL, the software on which they are based, from the ground up. * Know how to gather and organize critical business information, design a database based on this information, and retrieve and modify that information in a useful manner. * Use accepted data modeling procedures to design a relational database. * Master the concept of data normalization and the use of standard normalization rules. * Explore critical real-world issues including application integration and securing data against disclosure and loss. Wiley Pathways helps you achieve your goals Not every student is on the same path, but every student wants to succeed. The Information Technology series in the new Wiley Pathways imprint helps you achieve your goals. The books in this series--Introduction to Databases, Introduction to Programming Using Visual Basic, Introduction to Operating Systems, Networking Basics, Windows Network Administration, Network Security Fundamentals, and PC Hardware Essentials--offer a coordinated information technology curriculum. Learn more at www.wiley.com/go/pathways
R 2,912.00
