Given the improved analytical capabilities of Excel, scientists and engineers everywhere are using it--instead of FORTRAN--to solve problems. And why not? Excel is installed on millions of computers, features a rich set of built-in analyses tools, and includes an integrated Visual Basic for Applications (VBA) programming language. No wonder it's today's computing tool of choice. Chances are you already use Excel to perform some fairly routine calculations. Now the Excel Scientific and Engineering Cookbook shows you how to leverage Excel to perform more complex calculations, too, calculations that once fell in the domain of specialized tools. It does so by putting a smorgasbord of data analysis techniques right at your fingertips. The book shows how to perform these useful tasks and others: Use Excel and VBA in general Import data from a variety of sources Analyze data Perform calculations Visualize the results for interpretation and presentation Use Excel to solve specific science and engineering problems Wherever possible, the Excel Scientific and Engineering Cookbook draws on real-world examples from a range of scientific disciplines such as biology, chemistry, and physics. This way, you'll be better prepared to solve the problems you face in your everyday scientific or engineering tasks. High on practicality and low on theory, this quick, look-up reference provides instant solutions, or "recipes," to problems both basic and advanced. And like other books in O'Reilly's popular Cookbook format, each recipe also includes a discussion on how and why it works. As a result, you can take comfort in knowing that complete, practical answers are a mere page-flip away.

Learn to fully harness the power of Microsoft Excel(r) to perform scientific and engineering calculations With this text as your guide, you can significantly enhance Microsoft Excel's(r) capabilities to execute the calculations needed to solve a variety of chemical, biochemical, physical, engineering, biological, and medicinal problems. The text begins with two chapters that introduce you to Excel's Visual Basic for Applications (VBA) programming language, which allows you to expand Excel's(r) capabilities, although you can still use the text without learning VBA. Following the author's step-by-step instructions, here are just a few of the calculations you learn to perform: * Use worksheet functions to work with matrices * Find roots of equations and solve systems of simultaneous equations * Solve ordinary differential equations and partial differential equations * Perform linear and non-linear regression * Use random numbers and the Monte Carlo method This text is loaded with examples ranging from very basic to highly sophisticated solutions. More than 100 end-of-chapter problems help you test and put your knowledge to practice solving real-world problems. Answers and explanatory notes for most of the problems are provided in an appendix. The CD-ROM that accompanies this text provides several useful features: * All the spreadsheets, charts, and VBA code needed to perform the examples from the text * Solutions to most of the end-of-chapter problems * An add-in workbook with more than twenty custom functions This text does not require any background in programming, so it is suitable for both undergraduate and graduate courses. Moreover, practitioners in science and engineering will find that this guide saves hours of time by enabling them to perform most of their calculations with one familiar spreadsheet package.

In its most advanced form, Integrated Computational Materials Engineering (ICME) holistically integrates manufacturing simulation, advanced materials models and component performance analysis. This volume contains thirty-five papers presented at the 1st World Congress on Integrated Computational Materials Engineering. Modeling processing-microstructure relationships, modeling microstructure-property relationships, and the role of ICME in graduate and undergraduate education are discussed. Ideal as a primary text for engineering students, this book motivates a wider understanding of the advantages and limitations offered by the various computational (and coordinated experimental) tools of this field.

Millions of users create and share Excel spreadsheets every day, but few go deeply enough to learn the techniques that will make their work much easier. There are many ways to take advantage of Excel's advanced capabilities without spending hours on advanced study. Excel Hacks provides more than 130 hacks -- clever tools, tips and techniques -- that will leapfrog your work beyond the ordinary. Now expanded to include Excel 2007, this resourceful, roll-up-your-sleeves guide gives you little known "backdoor" tricks for several Excel versions using different platforms and external applications. Think of this book as a toolbox. When a need arises or a problem occurs, you can simply use the right tool for the job. Hacks are grouped into chapters so you can find what you need quickly, including ways to: Reduce workbook and worksheet frustration -- manage how users interact with worksheets, find and highlight information, and deal with debris and corruption. Analyze and manage data -- extend and automate these features, moving beyond the limited tasks they were designed to perform. Hack names -- learn not only how to name cells and ranges, but also how to create names that adapt to the data in your spreadsheet. Get the most out of PivotTables -- avoid the problems that make them frustrating and learn how to extend them. Create customized charts -- tweak and combine Excel's built-in charting capabilities. Hack formulas and functions -- subjects range from moving formulas around to dealing with datatype issues to improving recalculation time. Make the most of macros -- including ways to manage them and use them to extend other features. Use the enhanced capabilities of Microsoft Office 2007 to combine Excel with Word, Access, and Outlook. You can either browse through the book or read it from cover to cover, studying the procedures and scripts to learn more about Excel. However you use it, Excel Hacks will help you increase productivity and give you hours of "hacking" enjoyment along the way.

A brain friendly guide to Excel.

Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries Key FeaturesDiscover solutions for feature generation, feature extraction, and feature selectionUncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasetsImplement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy librariesBook Description Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems. What you will learnSimplify your feature engineering pipelines with powerful Python packagesGet to grips with imputing missing valuesEncode categorical variables with a wide set of techniquesExtract insights from text quickly and effortlesslyDevelop features from transactional data and time series dataDerive new features by combining existing variablesUnderstand how to transform, discretize, and scale your variablesCreate informative variables from date and timeWho this book is for This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book.

In this third edition of Vehicle Accident Analysis & Reconstruction Methods, Raymond M. Brach and R. Matthew Brach have expanded and updated their essential work for professionals in the field of accident reconstruction. Most accidents can be reconstructed effectively using of calculations and investigative and experimental data: the authors present the latest scientific, engineering, and mathematical reconstruction methods, providing a firm scientific foundation for practitioners. Accidents that cannot be reconstructed using the methods in this book are rare. In recent decades, the field of crash reconstruction has been transformed through the use of technology. The advent of event data records (EDRs) on vehicles signaled the era of modern crash reconstruction, which utilizes the same physical evidence that was previously available as well as electronic data that are measured/captured before, during, and after the collision. There is increased demand for more professional and accurate reconstruction as more crash data is available from vehicle sensors. The third edition of this essential work includes a new chapter on the use of EDRs as well as examples using EDR data in accident reconstruction. Early chapters feature foundational material that is necessary for the understanding of vehicle collisions and vehicle motion; later chapters present applications of the methods and include example reconstructions. As a result, Vehicle Accident Analysis & Reconstruction Methods remains the definitive resource in accident reconstruction.

Today, interpreting data is a critical decision-making factor for businesses and organizations. If your job requires you to manage and analyze all kinds of data, turn to Head First Data Analysis, where you'll quickly learn how to collect and organize data, sort the distractions from the truth, find meaningful patterns, draw conclusions, predict the future, and present your findings to others. Whether you're a product developer researching the market viability of a new product or service, a marketing manager gauging or predicting the effectiveness of a campaign, a salesperson who needs data to support product presentations, or a lone entrepreneur responsible for all of these data-intensive functions and more, the unique approach in Head First Data Analysis is by far the most efficient way to learn what you need to know to convert raw data into a vital business tool. You'll learn how to: Determine which data sources to use for collecting information Assess data quality and distinguish signal from noise Build basic data models to illuminate patterns, and assimilate new information into the models Cope with ambiguous information Design experiments to test hypotheses and draw conclusions Use segmentation to organize your data within discrete market groups Visualize data distributions to reveal new relationships and persuade others Predict the future with sampling and probability models Clean your data to make it useful Communicate the results of your analysis to your audience Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First Data Analysis uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.

The utilization of sensors, communications, and computer technologies to create greater efficiency in the generation, transmission, distribution, and consumption of electricity will enable better management of the electric power system. As the use of smart grid technologies grows, utilities will be able to automate meter reading and billing and consumers will be more aware of their energy usage and the associated costs. The results will require utilities and their suppliers to develop new business models, strategies, and processes. With an emphasis on reducing costs and improving return on investment (ROI) for utilities, Smart Grids: Clouds, Communications, Open Source, and Automation explores the design and implementation of smart grid technologies, considering the benefits to consumers as well as businesses. Focusing on industrial applications, the text: Provides a state-of-the-art account of the smart grid Explains how smart grid technologies are currently being used Includes detailed examples and test cases for real-life implementation Discusses trade-offs associated with the utilization of smart grid technologies Describes smart grid simulation software and offers insight into the future of the smart grid The electric power grid is in the early stages of a sea of change. Nobody knows which business models will survive, but companies heeding the lessons found in Smart Grids: Clouds, Communications, Open Source, and Automation might just increase their chances for success.

Written by someone who has experienced both teaching and working as a research chemist, this textbook will provide the theoretical chemistry associated with volumetric analysis supported by a selection of practicals for undergraduate students taking modules in introductory and analytical chemistry as well as for non-specialists teaching chemistry.