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As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to the basic concepts of managing data using a variety of computer languages and applications. It is intended to be a fast-paced introduction to some basic features of data management and covers statistical concepts, data-related techniques, features of Pandas, RDBMS, SQL, NLP topics, Matplotlib, and data visualization. Companion files with source code and color figures are available. FEATURES: Covers Pandas, RDBMS, NLP, data cleaning, SQL, and data visualization Introduces probability and statistical concepts Features numerous code samples throughout Includes companion files with source code and figures
As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to the basic concepts of data science using Python 3 and other computer applications. It is intended to be a fast-paced introduction to some basic features of data analytics and also covers statistics, data visualization, linear algebra, and regular expressions. The book includes numerous code samples using Python, NumPy, R, SQL, NoSQL, and Pandas. Companion files with source code and color figures are available. FEATURES: Includes a concise introduction to Python 3 and linear algebra Provides a thorough introduction to data visualization and regular expressions Covers NumPy, Pandas, R, and SQL Introduces probability and statistical concepts Features numerous code samples throughout Companion files with source code and figures
This book takes a minimalist approach to the traditional data structures course. It covers only those topics that are absolutely essential; the more esoteric structures and algorithms are left for later study. Suitable for an introductory data structures course or self-study, this book is written from the ground up in C++ (not translated from a Java-based text), and uses features of the C++ Standard Template Library to illustrate important concepts. A unique feature of the text is its use of literate programming techniques (originally developed by Donald Knuth) to present the sample code in a way that keeps the code from overwhelming the accompanying explanations. This book is suitable for an undergraduate data structures course using C++ or for developers needing review. Features • Takes a “minimalist” approach to the material that presents only essential concepts. This enables readers to focus on (and remember) just what they’ll need. • Uses select features of the C++11 standard to simplify the sample code and make it easier to understand. • Connects the concepts directly to the classes provided the Standard Template Library (STL), and shows how these classes can be implemented in C++. • Uses “literate programming” techniques that allow the presentation of the sample code to more clearly show the details of the code as well as how the pieces fit together.
As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to the basic concepts of data analytics using Python 3. It is intended to be a fast-paced introduction to some basic features of data analytics and also covers statistics, data visualization, and data cleaning. The book includes numerous code samples using NumPy, Pandas, Matplotlib, Seaborn, and features an appendix on regular expressions. Companion files with source code and color figures are available online by emailing the publisher with proof of purchase at [email protected]. FEATURES: Includes a concise introduction to Python 3 Provides a thorough introduction to data and data cleaning Covers NumPy and Pandas Introduces statistical concepts and data visualization (Matplotlib/Seaborn) Features an appendix on regular expressions Includes companion files with source code and figures
As part of the best selling Pocket Primer series, this book is an effort to give programmers sufficient knowledge of data cleaning to be able to work on their own projects. It is designed as a practical introduction to using flexible, powerful (and free) Unix / Linux shell commands to perform common data cleaning tasks. The book is packed with realistic examples and numerous commands that illustrate both the syntax and how the commands work together. Companion files with source code are available for downloading from the publisher. Features: - A practical introduction to using flexible, powerful (and free) Unix / Linux shell commands to perform common data cleaning tasks - Includes the concept of piping data between commands, regular expression substitution, and the sed and awk commands - Packed with realistic examples and numerous commands that illustrate both the syntax and how the commands work together - Assumes the reader has no prior experience, but the topic is covered comprehensively enough to teach a pro some new tricks - Includes companion files with all of the source code examples (download from the publisher).
As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to basic deep learning concepts and incorporate that knowledge into Angular 10 applications. It is intended to be a fast-paced introduction to some basic features of deep learning and an overview of several popular deep learning classifiers. The book includes code samples and numerous figures and covers topics such as Angular 10 functionality, basic deep learning concepts, classification algorithms, TensorFlow, and Keras. Companion files with code and color figures are included. FEATURES: Introduces basic deep learning concepts and Angular 10 applications Covers MLPs (MultiLayer Perceptrons) and CNNs (Convolutional Neural Networks), RNNs (Recurrent Neural Networks), LSTMs (Long Short-Term Memory), GRUs (Gated Recurrent Units), autoencoders, and GANs (Generative Adversarial Networks) Introduces TensorFlow 2 and Keras Includes companion files with source code and 4-color figures. The companion files are also available online by emailing the publisher with proof of purchase at [email protected].
As part of the new Pocket Primer series, this book provides an overview of the major aspects and the source code to use Python 2. It covers the latest Python developments, built-in functions and custom classes, data visualization, graphics, databases, and more. It includes a companion disc with appendices, source code, and figures. This Pocket Primer is primarily for self-directed learners who want to learn Python 2 and it serves as a starting point for deeper exploration of Python programming. Features: +Includes a companion disc with appendices, source code, and figures +Contains material devoted to Raspberry Pi, Roomba, JSON, and Jython +Includes latest Python 2 developments, built-in functions and custom classes, data visualization, graphics, databases, and more +Provides a solid introduction to Python 2 via complete code samples On the CD-ROM: +Appendices (HTML5 and JavaScript Toolkits, Jython, SPA) +Source code samples +All images from the text (including 4-color) +Solutions to Odd-Numbered Exercises
As part of the best-selling Pocket Primer series, this book is designed to provide a thorough introduction to numerous Python tools for data scientists. The book covers features of NumPy and Pandas, how to write regular expressions, and how to perform data cleaning tasks. It includes separate chapters on data visualization and working with Sklearn and SciPy. Companion files with source code are available. FEATURES: Introduces Python, NumPy, Sklearn, SciPy, and awk Covers data cleaning tasks and data visualization Features numerous code samples throughout Includes companion files with source code
As part of the Pocket Primer series, this book provides an overview of the major aspects and the source code to use D3. This Pocket Primer is primarily for self-directed learners who want to learn D3 and serves as a starting point for deeper exploration of its programming. Features: • Includes a companion disc with appendices, source code, and figures • Contains material devoted to D3 on mobile devices, using D3 with Ajax, HTML5 Web Sockets, NodeJS, and covers D3 application programming interfaces and other toolkits • Provides a solid introduction to D3 via complete code samples eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at [email protected].
As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to basic machine learning concepts and incorporate that knowledge into Angular applications. The book is intended to be a fast-paced introduction to some basic features of machine learning and an overview of several popular machine learning classifiers. It includes code samples and numerous figures and covers topics such as Angular functionality, basic machine learning concepts, classification algorithms, TensorFlow and Keras. The files with code and color figures are on the companion disc with the book or available from the publisher. Features: Introduces the basic machine learning concepts and Angular applications Includes source code and full color figures