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HT THINK LIKE A COMPUTER SCIEN
The goal of this book is to teach you to think like a computer scientist. This way of thinking combines some of the best features of mathematics, engineering, and natural science. Like mathematicians, computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating tradeoffs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions. The single most important skill for a computer scientist is problem solving. Problem solving means the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem-solving skills. That's why this chapter is called, The way of the program. On one level, you will be learning to program, a useful skill by itself. On another level, you will use programming as a means to an end. As we go along, that end will become clearer.
Python for Data Analysis
Computer programing is the vital field for the electronics, information and computer students. Programming with Python is trending topics nowadays. Its application has been increasing day by day. This book includes easy and readable theories with more examples. It also focusses on python projects. Computer Programming is the core subject for undergraduate students. With python, computer programming is not a big deal. This book is for beginners and intermediate students who wants to learn basics of Python Programming as well as Data Analysis and Visualization. In each Chapter, students will find necessary theories with relevant and practical examples. The concepts and examples used in this book are the inspiration from the different sources and authors. The whole text has been divided into seven chapters: 1. Introduction to Python 2. Data Structure and Conditional Statements 3. Loops and Functions 4. Object Oriented Programming in Python 5. Plotting graphs and charts in Python 6. Data analysis using NumPy and pandas 7. Mini Projects in Python
The Microeconomics of Complex Economies
The Microeconomics of Complex Economies uses game theory, modeling approaches, formal techniques, and computer simulations to teach useful, accessible approaches to real modern economies. It covers topics of information and innovation, including national and regional systems of innovation; clustered and networked firms; and open-source/open-innovation production and use. Its final chapter on policy perspectives and decisions confirms the value of the toolset. Written so chapters can be used independently, the book includes an introduction to computer simulation and pedagogical supplements. Its formal, accessible treatment of complexity goes beyond the scopes of neoclassical and mainstream economics. The highly interdependent economy of the 21st century demands a reconsideration of economic theories. - Describes the usefulness of complex heterodox economics - Emphasizes divergences and convergences with neoclassical economic theories and perspectives - Fits easily into courses on intermediate microeconomics, industrial organization, and games through self-contained chapters
Python in a Nutshell
This volume offers Python programmers a straightforward guide to the important tools and modules of this open source language. It deals with the most frequently used parts of the standard library as well as the most popular and important third party extensions.
Python
Named after the Monty Python comedy troupe, Python is an interpreted, open-source, object-oriented programming language. It's also free and runs portably on Windows, Mac OS, Unix, and other operating systems. Python can be used for all manner of programming tasks, from CGI scripts to full-fledged applications. It is gaining popularity among programmers in part because it is easier to read (and hence, debug) than most other programming languages, and it's generally simpler to install, learn, and use. Its line structure forces consistent indentation. Its syntax and semantics make it suitable for simple scripts and large programs. Its flexible data structures and dynamic typing allow you to get...
Python for Software Design
Python for Software Design is a concise introduction to software design using the Python programming language. The focus is on the programming process, with special emphasis on debugging. The book includes a wide range of exercises, from short examples to substantial projects, so that students have ample opportunity to practice each new concept.
Think Java
Currently used at many colleges, universities, and high schools, this hands-on introduction to computer science is ideal for people with little or no programming experience. The goal of this concise book is not just to teach you Java, but to help you think like a computer scientist. You’ll learn how to program—a useful skill by itself—but you’ll also discover how to use programming as a means to an end. Authors Allen Downey and Chris Mayfield start with the most basic concepts and gradually move into topics that are more complex, such as recursion and object-oriented programming. Each brief chapter covers the material for one week of a college course and includes exercises to help yo...
CATBox
Graph algorithms are easy to visualize and indeed there already exists a variety of packages to animate the dynamics when solving problems from graph theory. Still it can be difficult to understand the ideas behind the algorithm from the dynamic display alone. CATBox consists of a software system for animating graph algorithms and a course book which we developed simultaneously. The software system presents both the algorithm and the graph and puts the user always in control of the actual code that is executed. In the course book, intended for readers at advanced undergraduate or graduate level, computer exercises and examples replace the usual static pictures of algorithm dynamics. For this volume we have chosen solely algorithms for classical problems from combinatorial optimization, such as minimum spanning trees, shortest paths, maximum flows, minimum cost flows, weighted and unweighted matchings both for bipartite and non-bipartite graphs. Find more information at http://schliep.org/CATBox/.
