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e-book
I never seemed to find the perfect data-oriented Python book for my course, so I set out to write just such a book. Luckily at a faculty meeting three weeks before I was about to start my new book from scratch over the holiday break, Dr. Atul Prakash showed me the Think Python book which he had used to teach his Python course that semester. It is a well-written Computer Science text with a focus on short, direct explanations and ease of learning.The overall book structure has been changed to get to doing data analysis problems as quickly as possible and have a series of running examples and exercises about data analysis from the very beginning. Chapters 2–10 are similar to the Think Python book, but there have been major changes. Number-oriented examples and exercises have been replaced with data- oriented exercises. Topics are presented in the order needed to build increasingly sophisticated data analysis solutions. Some topics like try and except are pulled forward and presented as part of the chapter on conditionals. Functions are given very light treatment until they are needed to handle program complexity rather than introduced as an early lesson in abstraction. Nearly all user-defined functions have been removed from the example code and exercises outside of Chapter 4. The word “recursion”1 does not appear in the book at all. In chapters 1 and 11–16, all of the material is brand new, focusing on real-world uses and simple examples of Python for data analysis including regular expressions for searching and parsing, automating tasks on your computer, retrieving data across the network, scraping web pages for data, object-oriented programming, using web services, parsing XML and JSON data, creating and using databases using Structured Query Language, and visualizing data. The ultimate goal of all of these changes is a shift from a Computer Science to an Informatics focus is to only include topics into a first technology class that can be useful even if one chooses not to become a professional programmer.
- Subjects:
- Computing
- Keywords:
- Computer programming Programming languages (Electronic computers) Textbooks Python (Computer program language)
- Resource Type:
- e-book
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e-book
In this book, you will learn how digital signals are captured, represented, processed, communicated, and stored in computers. The specific topics we will cover include: physical properties of the source information (such as sound or images), devices for information cap- ture (microphones, cameras), digitization, compression, digital signal representation (JPEG, MPEG), digital signal processing (DSP), and network communication. By the end of this book, you should understand the problems and solutions facing signal computing systems development in the areas of user interfaces, information retrieval, data structures and algo- rithms, and communications.
- Subjects:
- Computing
- Keywords:
- Signal processing -- Digital techniques Textbooks
- Resource Type:
- e-book
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e-book
This book introduces concepts in mobile, autonomous robotics to 3rd-4th year students in Computer Science or a related discipline. The book covers principles of robot motion, forward and inverse kinematics of robotic arms and simple wheeled platforms, perception, error propagation, localization and simultaneous localization and mapping. The cover picture shows a wind-up toy that is smart enough to not fall off a table just using intelligent mechanism design and illustrate the importance of the mechanism in designing intelligent, autonomous systems. This book is open source, open to contributions, and released under a creative common license.
- Subjects:
- Computing
- Keywords:
- Autonomous robots Robots -- Programming Textbooks
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- e-book
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e-book
Pharo is a clean, innovative, open-source, live-programming environment. Deep into Pharois the second volume of a series of books covering Pharo. Whereas the first volume is intended for newcomers, this second volume covers deeper topics.You will learn about Pharo frameworks and libraries such as Glamour, PetitParser, Roassal, FileSystem, Regex, and Socket. You will explore the language with chapters on exceptions, blocks, small integers, and floats. You will discover tools such as profilers, Metacello and Gofer.
- Subjects:
- Computing
- Keywords:
- Textbooks Smalltalk-80 (Computer program language)
- Resource Type:
- e-book
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e-book
Pharo is an open-source, elegant and pure object-oriented language that supports truly immersive and life programming experience. Pharo offers excellent tools such as hot-debuggers and on the fly code update that change the programming experience. More at http://www.pharo.org.Pharo is a powerful language and IDE that companies use to deliver complex business-effective applications. More at: http://www.pharo.org/success In Pharo everything is an object, and anything can change at run-time under your fingers. Pharo is written in itself you can explore a complete world. You can feel and talk to objects. But Pharo does not stop there, with Pharo you can improve your object-oriented skills by rediscovering the essence of object-oriented programming. Pharo by Example 50, intended for both students and developers, will guide you gently through the Pharo language and environment by means of a series of examples and exercises. This book is available under the Creative Commons Attribution-ShareAlike 3.0 license.
- Subjects:
- Computing
- Keywords:
- Textbooks Smalltalk-80 (Computer program language)
- Resource Type:
- e-book
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e-book
Squeak is a modern open-source development environment for the classic Smalltalk-80 programming language. Despite being the first purely object-oriented language and environment, Smalltalk is in many ways still far ahead of its successors in promoting a vision of an environment where everything is an object, and anything can change at run-time. Squeak by Example, intended for both students and developers, will guide you gently through the Squeak language and environment by means of a series of examples and exercises. The book helps you get started with A Quick Tour of Squeak and guides you through A First Application. The Smalltalk language is introduced in three chapters on Syntax in a Nutshell, Understanding Message Syntax and The Smalltalk Object Model. Development with Squeak is covered in The Squeak Programming Environment and SUnit. Several of the key classes are presented in chapters on Basic Classes, Collections, Streams and Morphic. The first edition of the book concludes with chapters on Classes and Metaclasses and Frequently Asked Questions.
- Subjects:
- Computing
- Keywords:
- Multimedia systems Squeak Textbooks Smalltalk-80 (Computer program language)
- Resource Type:
- e-book
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e-book
Think DSP is an introduction to Digital Signal Processing in Python. The premise of this book (and the other books in the Think X series) is that if you know how to program, you can use that skill to learn other things. The author is writing this book because he thinks the conventional approach to digital signal processing is backward: most books (and the classes that use them) present the material bottom-up, starting with mathematical abstractions like phasors.
- Subjects:
- Electrical Engineering and Computing
- Keywords:
- Signal processing -- Digital techniques -- Data processing Python (Computer program language) Textbooks
- Resource Type:
- e-book
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e-book
This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science: Data structures and algorithms: A data structure is a collection that contains data elements organized in a way that supports particular operations. For example, a dictionary organizes key-value pairs in a way that provides fast mapping from keys to values, but mapping from values to keys is generally slower. An algorithm is a mechanical process for performing a computation. Designing efficient programs often involves the co-evolution of data structures and the algorithms that use them. For example, the first few chapters are about graphs, a data structure that is a good implementation of a graph---nested dictionaries---and several graph algorithms that use this data structure. Python programming: This book picks up where Think Python leaves off. I assume that you have read that book or have equivalent knowledge of Python. As always, I will try to emphasize fundmental ideas that apply to programming in many languages, but along the way you will learn some useful features that are specific to Python. Computational modeling: A model is a simplified description of a system that is useful for simulation or analysis. Computational models are designed to take advantage of cheap, fast computation. Philosophy of science: The models and results in this book raise a number of questions relevant to the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, holism and reductionism, and Bayesian epistemology. This book focuses on discrete models, which include graphs, cellular automata, and agent-based models. They are often characterized by structure, rules and transitions rather than by equations. They tend to be more abstract than continuous models; in some cases there is no direct correspondence between the model and a physical system. Complexity science is an interdisciplinary field---at the intersection of mathematics, computer science and physics---that focuses on these kinds of models. That's what this book is about.
- Subjects:
- Computing
- Keywords:
- Computational complexity Python (Computer program language) Textbooks
- Resource Type:
- e-book
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e-book
Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops. I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems.
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Bayesian statistical decision theory Python (Computer program language) Textbooks
- Resource Type:
- e-book
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e-book
Think Stats is an introduction to Probability and Statistics for Python programmers. Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets. If you have basic skills in Python, you can use them to learn concepts in probability and statistics. Think Stats is based on a Python library for probability distributions (PMFs and CDFs). Many of the exercises use short programs to run experiments and help readers develop understanding.
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Textbooks Statistics -- Computer programs
- Resource Type:
- e-book