Based on the tenet that good habits are formed early, the authors consistently emphasize the principles of structured programming and software engineering. Every complete programme uses a consistent style and as programmes are analyze, styles and standards are further explained.

Much of current programming practice is basically empirical and ad hoc in approach. Each problem is tackled without relation to those that have gone before; experiences are made and stored as a series of fragments. Now, under the pressure of events, this unsatisfactory state of affairs is coming to an end. Programming is becoming a technology, a theory known as structured programming is developing. The purpose of a theory is to categorise and explain existing practice, thus enabling it to be improved through the development of new and sharper techniques. The resulting experiences have then to be fed back into the theory so that the process of enrichment may continue. This dialectical relationship between theory and practice is essential to a healthy programming technology. The lack of such a relationship in the 1950s and 60s and the accompanying software crisis certainly confirm the converse of this proposition. My aim in writing this book has been to explain the current state of the theory of structured programming, so that it may be used to improve the reader's practice. The book deals with two facets of programming - how to design a program in terms of abstract data structures and how to represent the data structures on real and bounded computers. The separation between program design and data structure representation leads to more reliable and flexible programs.

Programming Fundamentals - A Modular Structured Approach using C++ is written by Kenneth Leroy Busbee, a faculty member at Houston Community College in Houston, Texas. The materials used in this textbook/collection were developed by the author and others as independent modules for publication within the Connexions environment. Programming fundamentals are often divided into three college courses: Modular/Structured, Object Oriented and Data Structures. This textbook/collection covers the rest of those three courses.

This well-organized book, now in its second edition, discusses the fundamentals of various data structures using C as the programming language. Beginning with the basics of C, the discussion moves on to describe Pointers, Arrays, Linked lists, Stacks, Queues, Trees, Heaps, Graphs, Files, Hashing, and so on that form the base of data structure. It builds up the concept of Pointers in a lucid manner with suitable examples, which forms the crux of Data Structures. Besides updated text and additional multiple choice questions, the new edition deals with various classical problems such as 8-queens problem, towers of Hanoi, minesweeper, lift problem, tic-tac-toe and Knapsack problem, which will help students understand how the real-life problems can be solved by using data structures. The book exhaustively covers all important topics prescribed in the syllabi of Indian universities/institutes, including all the Technical Universities and NITs. Primarily intended as a text for the undergraduate students of Engineering (Computer Science/Information Technology) and postgraduate students of Computer Application (MCA) and Computer Science (M.Sc.), the book will also be of immense use to professionals engaged in the field of computer science and information technology. Key Features • Provides more than 160 complete programs for better understanding. • Includes over 470 MCQs to cater to the syllabus needs of GATE and other competitive exams. • Contains over 500 figures to explain various algorithms and concepts. • Contains solved examples and programs for practice. • Provides companion CD containing additional programs for students’ use.

A Practical Approach to Data Structure and Algorithm with Programming in C discusses about how data structure and algorithm plays out with programming in C. This book comprises topics such as algorithm writing and array. This book sheds light on topics such as searching algorithms, searching algorithms and heap & heap sort in terms of data structure. Readers have also provided insights about basic as well as advanced level information about types of arrays, space complexity of recursive algorithm and primitive operations on array. There is also a discussion about the applications and implementation of the above-mentioned factors in this book.

Programming is now parallel programming. Much as structured programming revolutionized traditional serial programming decades ago, a new kind of structured programming, based on patterns, is relevant to parallel programming today. Parallel computing experts and industry insiders Michael McCool, Arch Robison, and James Reinders describe how to design and implement maintainable and efficient parallel algorithms using a pattern-based approach. They present both theory and practice, and give detailed concrete examples using multiple programming models. Examples are primarily given using two of the most popular and cutting edge programming models for parallel programming: Threading Building Blocks, and Cilk Plus. These architecture-independent models enable easy integration into existing applications, preserve investments in existing code, and speed the development of parallel applications. Examples from realistic contexts illustrate patterns and themes in parallel algorithm design that are widely applicable regardless of implementation technology. The patterns-based approach offers structure and insight that developers can apply to a variety of parallel programming models Develops a composable, structured, scalable, and machine-independent approach to parallel computing Includes detailed examples in both Cilk Plus and the latest Threading Building Blocks, which support a wide variety of computers

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results