Introduction to Computers and Application Software provides a complete survey of the computer technologies necessary for achieving basic technology literacy and sets you on the path to career success. This helpful resource covers: - Computer Hardware - The Microsoft Windows 7 Operating System - The Internet - Microsoft Word 2010 - Microsoft Excel 2010 - Microsoft PowerPoint 2010

Introduction to Mechanism Design: with Computer Applications provides an updated approach to undergraduate Mechanism Design and Kinematics courses/modules for engineering students. The use of web-based simulations, solid modeling, and software such as MATLAB and Excel is employed to link the design process with the latest software tools for the design and analysis of mechanisms and machines. While a mechanical engineer might brainstorm with a pencil and sketch pad, the final result is developed and communicated through CAD and computational visualizations. This modern approach to mechanical design processes has not been fully integrated in most books, as it is in this new text.

Introduction to Computing is a comprehensive text designed for the CS0 (Intro to CS) course at the college level. It may also be used as a primary text for the Advanced Placement Computer Science course at the high school level.

This book’s primary objective is to educate aspiring finance professionals about mathematics and computation in the context of financial derivatives. The authors offer a balance of traditional coverage and technology to fill the void between highly mathematical books and broad finance books. The focus of this book is twofold: To partner mathematics with corresponding intuition rather than diving so deeply into the mathematics that the material is inaccessible to many readers. To build reader intuition, understanding and confidence through three types of computer applications that help the reader understand the mathematics of the models. Unlike many books on financial derivatives requiring stochastic calculus, this book presents the fundamental theories based on only undergraduate probability knowledge. A key feature of this book is its focus on applying models in three programming languages –R, Mathematica and EXCEL. Each of the three approaches offers unique advantages. The computer applications are carefully introduced and require little prior programming background. The financial derivative models that are included in this book are virtually identical to those covered in the top financial professional certificate programs in finance. The overlap of financial models between these programs and this book is broad and deep.

Perkovic's Introduction to Programming Using Python is more than just an introduction to programming. It is an inclusive introduction to Computer Science that takes the pedagogical approach of "the right tool for the job at the right moment," and focuses on application development. The approach is hands-on and problem-oriented, with practice problems and solutions appearing throughout the text. The text is imperative-first, but does not shy away from discussing objects early where appropriate. Discussions of user-defined classes and Object-Oriented Programming appear later in the text, when students have more background and concepts can be motivated. Chapters include an introduction to problem solving techniques and classical algorithms, problem-solving and programming and ways to apply core skills to application development.

Through the material, readers are given the ability to analyze systematically a potential area of need - such as a function in a department - to determine whether a computer application might help to assess the costs and benefits of alternative proposals and to proceed from that analysis to a clear specification for what the proposed solution should accomplish. There is also coverage of how computer professionals evaluate hardware and design the software and systems that will implement a given application. Furthermore, readers will learn how to manage the people, resources, time, and money required in the development process for creating a given application program or for adapting a package application to the needs specified in the design.

The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization. This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters. Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics.