The Scientific Programmer's Toolkit: Turbo Pascal Edition presents a complete software environment for anyone writing programs in mathematical, engineering, or science areas. This toolkit package is designed for use with Turbo Pascal, the de facto standard Pascal system for PC and compatible machines. The book and its software provides an integrated software library of programming tools. The programs and routines fall into three categories: graphical, mathematical, and utilities. Routines are further subdivided into three levels that reflect the experience of the user. For graphics and text handling routines there is also a Level 0, which provides an interface to the machine operating system. By using hierarchically structured routines, the clearly written text, and a wide range of example programs, software users can construct a user-friendly interface with minimal effort. The levels structure makes it easy for newcomers to use the Toolkit, and with growing experience, users can achieve more elaborate effects. The Scientific Programmer's Toolkit will be useful to consultants, researchers, and students in any quantitative profession or science, in private or public sector research establishments, or in secondary and higher education.

The Scientific Programmer's Toolkit: Turbo Pascal Edition presents a complete software environment for anyone writing programs in mathematical, engineering, or science areas. This toolkit package is designed for use with Turbo Pascal, the de facto standard Pascal system for PC and compatible machines. The book and its software provides an integrated software library of programming tools. The programs and routines fall into three categories: graphical, mathematical, and utilities. Routines are further subdivided into three levels that reflect the experience of the user. For graphics and text handling routines there is also a Level 0, which provides an interface to the machine operating system. By using hierarchically structured routines, the clearly written text, and a wide range of example programs, software users can construct a user-friendly interface with minimal effort. The levels structure makes it easy for newcomers to use the Toolkit, and with growing experience, users can achieve more elaborate effects. The Scientific Programmer's Toolkit will be useful to consultants, researchers, and students in any quantitative profession or science, in private or public sector research establishments, or in secondary and higher education.

This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.

The book teaches students to model a scientific problem and write a computer program in C language to solve that problem. It introduces the basics of C language, and then describes and discusses algorithms commonly used in scientific applications (e.g. searching, graphs, statistics, equation solving, Monte Carlo methods etc.).

The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches "Matlab-style" and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science. From the reviews: Langtangen ... does an excellent job of introducing programming as a set of skills in problem solving. He guides the reader into thinking properly about producing program logic and data structures for modeling real-world problems using objects and functions and embracing the object-oriented paradigm. ... Summing Up: Highly recommended. F. H. Wild III, Choice, Vol. 47 (8), April 2010 Those of us who have learned scientific programming in Python ‘on the streets’ could be a little jealous of students who have the opportunity to take a course out of Langtangen’s Primer.” John D. Cook, The Mathematical Association of America, September 2011 This book goes through Python in particular, and programming in general, via tasks that scientists will likely perform. It contains valuable information for students new to scientific computing and would be the perfect bridge between an introduction to programming and an advanced course on numerical methods or computational science. Alex Small, IEEE, CiSE Vol. 14 (2), March /April 2012 “This fourth edition is a wonderful, inclusive textbook that covers pretty much everything one needs to know to go from zero to fairly sophisticated scientific programming in Python...” Joan Horvath, Computing Reviews, March 2015

This concise guide covers the fundamental aspects of the numerical analysis, basing upon it the construction of its routines for solving nonlinear equations, linear and nonlinear systems of equations, and eigenvalue problems. Focusing on software development, this book emphasizes software tools, OOP techniques for handling vectors, polynomials, and matrices. Using actual examples to demonstrate reusable tools, the book enables readers to solve broad classes of software development and programming challenges. It adopts a balanced approach between OOP techniques and quick and dirty number crunching, and emphasizes the use of OOP features in implementing vector, polynomial and matrix algebra. As a practical reference, it will help developers and consultants setting up applications programs for electrical, electronic engineering and physical sciences who need to develop clean, efficient C++ programs in minimal time.

Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications. Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you. Use graphics to describe data with one, two, or dozens of variables Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments Mine data with computationally intensive methods such as simulation and clustering Make your conclusions understandable through reports, dashboards, and other metrics programs Understand financial calculations, including the time-value of money Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations Become familiar with different open source programming environments for data analysis "Finally, a concise reference for understanding how to conquer piles of data."--Austin King, Senior Web Developer, Mozilla "An indispensable text for aspiring data scientists."--Michael E. Driscoll, CEO/Founder, Dataspora