Dive into the world of scientific computing with 'SciPy Recipes', a practical guide tailored for anyone seeking hands-on experience with the SciPy stack. With over 110 detailed recipes, you'll gain expertise in handling real-world data challenges, from statistical computations to crafting intricate visualizations and beyond. What this Book will help me do Learn to use the SciPy Stack libraries like NumPy, pandas, and matplotlib effectively for scientific computing tasks. Master data wrangling techniques using pandas for efficient data manipulation. Understand the process of creating informative visualizations using matplotlib. Perform advanced statistical and numerical computations with simplicity. Solve real-world problems like numerical analysis and linear algebra using SciPy components. Author(s) None Martins, Ruben Oliva Ramos, and V Kishore Ayyadevara bring years of experience in scientific computing and Python programming to this book. Individually, they have contributed extensively to the implementation of computational tools and systems. Together, they've crafted this book to be both accessible to learners and insightful for practitioners, blending instruction with real-world practical applications. Who is it for? This book is designed for Python developers, data scientists, and analysts eager to venture into scientific computing. If you have a basic understanding of Python and aspire to effectively manipulate and visualize data using the SciPy stack, this book is perfect for you. It's equally beneficial for those who seek practical solutions to complex computational challenges. Begin your journey into scientific computing with this essential guide.

This book covers the fundamentals of adaptive filtering, with a focus on the least mean square (LMS) adaptive filter. It discusses random variables, stochastic processes, vectors, matrices, determinants, discrete random signals, and probability distributions, while delivering a concise introduction to MATLAB®—complete with problems, computer experiments, and over 110 functions and script files. The text not only addresses the basics of the LMS adaptive filter algorithm but also explores the Wiener filter and its applications, details the steepest descent method, and develops the Newton’s algorithm.

Edited by a pioneer in the area of advanced semiconductor materials, this book contains contributions from experts who explore the development and use of sensors in biological and medical applications. It covers advanced sensing and communications, modeling of DNA-derivative architecture, and the use of enzyme and quartz crystal microbalance-based biosensors. The book also addresses biosensors in human behavior measurement, sweat rate wearable sensors, and the future of medical imaging, including developments in spatial and spectral resolution of semiconductor detectors. Contributors discuss application of high-resolution CdTe detectors in gamma ray imaging and recent advances in positron emission tomography technology.

Electronically scanned arrays (ESAs) have become a key technology for sensor electronic systems. MATLAB® provides an excellent framework for ESA design and analysis, and this book is an invaluable resource for those who require simulation analysis tools that provide insight and understanding for ESA design. In addition to covering ESA fundamentals such as pattern synthesis, grating lobes, and instantaneous bandwidth, the text also provides insight into pattern optimization, subarray beamforming, space-based application of ESAs, and ESA reliability modeling. The book provides MATLAB code, giving readers an opportunity to model ESAs and develop an in-depth understanding that other books do not offer.

Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded second edition of Fundamentals of Predictive Analytics with JMP(R) bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining and predictive analytics. Going beyond the theoretical foundation, this book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis. First, this book teaches you to recognize when it is appropriate to use a tool, what variables and data are required, and what the results might be. Second, it teaches you how to interpret the results and then, step-by-step, how and where to perform and evaluate the analysis in JMP . Using JMP 13 and JMP 13 Pro, this book offers the following new and enhanced features in an example-driven format: an add-in for Microsoft Excel Graph Builder dirty data visualization regression ANOVA logistic regression principal component analysis LASSO elastic net cluster analysis decision trees k-nearest neighbors neural networks bootstrap forests boosted trees text mining association rules model comparison With today’s emphasis on business intelligence, business analytics, and predictive analytics, this second edition is invaluable to anyone who needs to expand his or her knowledge of statistics and to apply real-world, problem-solving analysis. This book is part of the SAS Press program.

The only how-to guide offering a unified, systemic approach to acquiring, cleaning, and managing data in R Every experienced practitioner knows that preparing data for modeling is a painstaking, time-consuming process. Adding to the difficulty is that most modelers learn the steps involved in cleaning and managing data piecemeal, often on the fly, or they develop their own ad hoc methods. This book helps simplify their task by providing a unified, systematic approach to acquiring, modeling, manipulating, cleaning, and maintaining data in R. Starting with the very basics, data scientists Samuel E. Buttrey and Lyn R. Whitaker walk readers through the entire process. From what data looks like and what it should look like, they progress through all the steps involved in getting data ready for modeling. They describe best practices for acquiring data from numerous sources; explore key issues in data handling, including text/regular expressions, big data, parallel processing, merging, matching, and checking for duplicates; and outline highly efficient and reliable techniques for documenting data and recordkeeping, including audit trails, getting data back out of R, and more. The only single-source guide to R data and its preparation, it describes best practices for acquiring, manipulating, cleaning, and maintaining data Begins with the basics and walks readers through all the steps necessary to get data ready for the modeling process Provides expert guidance on how to document the processes described so that they are reproducible Written by seasoned professionals, it provides both introductory and advanced techniques Features case studies with supporting data and R code, hosted on a companion website A Data Scientist's Guide to Acquiring, Cleaning and Managing Data in R is a valuable working resource/bench manual for practitioners who collect and analyze data, lab scientists and research associates of all levels of experience, and graduate-level data mining students.

Use machine learning and Oracle Business Intelligence Enterprise Edition (OBIEE) as a comprehensive BI solution. This book follows a when-to, why-to, and how-to approach to explain the key steps involved in utilizing the artificial intelligence components now available for a successful OBIEE implementation. Oracle Business Intelligence with Machine Learning covers various technologies including using Oracle OBIEE, R Enterprise, Spatial Maps, and machine learning for advanced visualization and analytics. The machine learning material focuses on learning representations of input data suitable for a given prediction problem. This book focuses on the practical aspects of implementing machine learning solutions using the rich Oracle BI ecosystem. The primary objective of this book is to bridge the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to machine learning with OBIEE. What You Will Learn See machine learning in OBIEE Master the fundamentals of machine learning and how it pertains to BI and advanced analytics Gain an introduction to Oracle R Enterprise Discover the practical considerations of implementing machine learning with OBIEE Who This Book Is For Analytics managers, BI architects and developers, and data scientists.

Discover hidden relationships among the variables in your data, and learn how to exploit these relationships. This book presents a collection of data-mining algorithms that are effective in a wide variety of prediction and classification applications. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the focus is on practical applicability, with all code written in such a way that it can easily be included into any program. The Windows-based DATAMINE program lets you experiment with the techniques before incorporating them into your own work. What You'll Learn Use Monte-Carlo permutation tests to provide statistically sound assessments of relationships present in your data Discover how combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the data Work with feature weighting as regularized energy-based learning to rank variables according to their predictive power when there is too little data for traditional methods See how the eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the data Plot regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is high Who This Book Is For Anyone interested in discovering and exploiting relationships among variables. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.

The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Pandas for Everyone Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning Register your product at informit.com/register for convenient access to downloads, updates, and/or corrections as they become available.

A simple communication framework to begin practising today We all carry around the technology to stay connected 24/7, yet many of us are disengaged and challenged with our lack of communication skills. The Power of Connection provides you with practical, real-world solutions for improving your professional performance, your personal relationships and your outlook — one conversation at a time. Becoming a confident and compelling communicator might be the most important skill for leaders in the modern business landscape, parents in the modern home and individuals who use ‘self-talk' to help shape their world. By adopting the simple strategies revealed in every chapter, you can become an unshakeable success at what you set out to do. This book is designed to help you start communicating better today, so start reading and start practicing with your very next conversation! Understand your communication strengths and weaknesses Become a better listener to build a deeper connection Learn how communication sits at the heart of all relationships Develop the skills to connect, inspire, engage and empower We are surrounded by noise, yet no one is actually saying anything we can connect with — or are we just not listening? Communication is a two-way street, and involves so much more than just speaking. The Power of Connection offers a quick and easy road map for your personal journey of growth and development that will make you a better parent, friend, spouse and employee. It's the right message for this time considering there's never a wrong time to level up your skills and become more effective at work, at home and in life.

Deliver eye-catching Business Intelligence with Microsoft Power BI Desktop. This new edition has been updated to cover all the latest features, including combo charts, Cartesian charts, trend lines, use of gauges, and more. Also covered are Top-N features, the ability to bin data into groupings and chart the groupings, and new techniques for detecting and handling outlier data points. You can take data from virtually any source and use it to produce stunning dashboards and compelling reports that will seize your audience’s attention. Slice and dice the data with remarkable ease and then add metrics and KPIs to project the insights that create your competitive advantage. Make raw data into clear, accurate, and interactive information with Microsoft’s free self-service business intelligence tool. Pro Power BI Desktop shows you how to choose from a wide range of built-in and third-party visualization types so that your message is always enhanced. You’ll be able to deliver those results on the PC, tablets, and smartphones, as well as share results via the cloud. This book helps you save time by preparing the underlying data correctly without needing an IT department to prepare it for you. What You'll Learn Deliver attention-grabbing information, turning data into insight Mash up data from multiple sources into a cleansed and coherent data model Create dashboards that help in monitoring key performance indicators of your business Build interdependent charts, maps, and tables to deliver visually stunning information Share business intelligence in the cloud without involving IT Deliver visually stunning and interactive charts, maps, and tables Find new insights as you chop and tweak your data as never before Adapt delivery to mobile devices such as phones and tablets Who This Book Is For Everyone from CEOs and Business Intelligence developers to power users and IT managers

D3.js in Action, Second Edition is completely revised and updated for D3 v4 and ES6. It's a practical tutorial for creating interactive graphics and data-driven applications using D3. About the Technology Visualizing complex data is hard. Visualizing complex data on the web is darn near impossible without D3.js. D3 is a JavaScript library that provides a simple but powerful data visualization API over HTML, CSS, and SVG. Start with a structure, dataset, or algorithm; mix in D3; and you can programmatically generate static, animated, or interactive images that scale to any screen or browser. It's easy, and after a little practice, you'll be blown away by how beautiful your results can be! About the Book D3.js in Action, Second Edition is a completely updated revision of Manning's bestselling guide to data visualization with D3. You'll explore dozens of real-world examples in full-color, including force and network diagrams, workflow illustrations, geospatial constructions, and more! Along the way, you'll pick up best practices for building interactive graphics, animations, and live data representations. You'll also step through a fully interactive application created with D3 and React. What's Inside Rich full-color diagrams and illustrations Updated for D3 v4 and ES6 Reusable layouts and components Geospatial data visualizations Mixed-mode rendering About the Reader Suitable for web developers with HTML, CSS, and JavaScript skills. No specialized data science skills required. About the Author Elijah Meeks is a senior data visualization engineer at Netflix. Quotes From basic to complex, this book gives you the tools to create beautiful data visualizations. - Claudio Rodriguez, Cox Media Group The best reference for one of the most useful DataViz tools. - Jonathan Rioux, TD Insurance From toy examples to techniques for real projects. Shows how all the pieces fit together. - Scott McKissock, USAID A clever way to immerse yourself in the D3.js world. - Felipe Vildoso Castillo, University of Chile

Measurement and Data Analysis for Engineering and Science, Fourth Edition, provides up-to-date coverage of experimentation methods in science and engineering.

Business Research Reporting addresses the essential activities of locating, collecting, evaluating, analyzing, interpreting, and reporting business data. It highlights the value of primary and secondary research to making business decisions and solving business problems. It aims to help business managers, MBA candidates, and upper-level college students boost their research skills and report research with confidence. This book discusses primary data collection, sampling concepts, and the use of measurement and scales in preparing instruments. Also, this book explores statistical and non-statistical analysis of qualitative and quantitative data and data interpretation (findings, conclusions, and recommendations). The author shows how to locate, evaluate, and extract secondary data found on the web and in brick-and-mortar libraries, including optimized searching, evaluating, and recording. Plus, the book demonstrates how to avoid copyright infringement and plagiarism, use online citation software, and cite sources when writing and presenting. Two glossaries—one each for primary and secondary research—round out the content. Business Research Reporting can be your go-to guidebook for years to come. Reading through it in a couple of hours, you can pick up ample information to apply instantly. Then keep it handy and refer to it in your ongoing research activities.

"Learning Pentaho Data Integration 8 CE" is your comprehensive guide to mastering data manipulation and integration using Pentaho Data Integration (PDI) 8 Community Edition. Through step-by-step instructions and practical examples, you'll learn to explore, transform, validate, and integrate data from multiple sources, equipping you to handle real-world data challenges efficiently. What this Book will help me do Effectively install and understand the foundational concepts of Pentaho Data Integration 8 Community Edition. Efficiently organize, clean, and transform raw data from various sources into useful formats. Perform advanced data operations like metadata injection, managing relational databases, and implementing ETL solutions. Design, create, and deploy comprehensive data warehouse solutions using modern best practices. Streamline daily data processing tasks with flexibility and accuracy while handling errors gracefully. Author(s) The author, Carina Roldán, is an experienced professional in the field of data science and ETL (Extract, Transform, Load) development. Her expertise in leveraging tools like Pentaho Data Integration has allowed her to contribute significantly to BI and data management projects. Her approach in writing this book reflects her commitment to simplifying complex topics for aspiring professionals. Who is it for? This book is ideal for software developers, data analysts, business intelligence professionals, and IT students aiming to enhance their skills in ETL processes using Pentaho Data Integration. Beginners who wish to learn PDI comprehensively and professionals looking to deepen their expertise will both find value in this resource. It's also suitable for individuals involved in data warehouse design and implementation. This book will equip you with the skills to handle diverse data transformation tasks effectively.

This book, "Learning D3.js 5 Mapping", guides developers through the process of creating dynamic and interactive data visualizations. With a focus on D3.js, you'll learn to harness the power of JavaScript to create maps and graphical objects that inform and engage. What this Book will help me do Gain expertise in working with SVG geometric shapes to design compelling graphics. Learn techniques to manage, process, and use geographic data effectively. Master adding interactivity to visual maps to provide an immersive user experience. Understand how to optimize and manipulate geoJSON files using topoJSON. Learn to create varied map types, such as hexbins and globes, using D3.js and Canvas. Author(s) None Newton and Oscar Villarreal, among others, collaborated to author this guide. They are experienced in front-end development and data visualization, bringing a practical and hands-on approach to learning through this book. Their backgrounds ensure the book addresses common challenges faced during implementation, offering thoughtful solutions. Who is it for? "Learning D3.js 5 Mapping" is perfect for web developers familiar with HTML, CSS, and JavaScript who want to expand their expertise in data visualization and mapping. If you're looking to incorporate interactive charts or maps into your web applications, this book will provide practical guidance and solid fundamentals. No prior experience with D3.js is necessary.

Dive into the world of data mining with 'R Data Mining' and discover how to utilize R's vast tools for uncovering insights in data. This hands-on guide immerses you in real-world cases, teaching both foundational concepts and advanced techniques like regression models and text mining. You'll emerge with a sharp understanding of how to transform raw data into actionable information. What this Book will help me do Gain proficiency in R packages such as dplyr and ggplot2 for data manipulation and visualization. Master the CRISP-DM methodology to systematically approach data mining projects. Develop skillsets in data cleaning and validation to ensure quality data analysis. Understand and implement multiple regression and classification techniques effectively. Learn to use ensemble learning methods and produce reporting with R Markdown. Author(s) Andrea Cirillo brings extensive expertise in data science and R programming as the author of 'R Data Mining.' Their practical approach, drawing from professional experiences in various industries, makes complex techniques accessible and engaging. Their passion for teaching translates into a meticulously crafted learning journey for aspiring data miners. Who is it for? This book is ideal for beginner to intermediate-level data analysts or aspiring data scientists eager to delve into the field of data mining using R. If you're familiar with the basics of programming in R and want to expand into practical applications of data mining methodologies, this is the resource for you. Gain hands-on experience by engaging with real-world datasets and scenarios.

Familiarize yourself with MATLAB using this concise, practical tutorial that is focused on writing code to learn concepts. Starting from the basics, this book covers array-based computing, plotting and working with files, numerical computation formalism, and the primary concepts of approximations. Introduction to MATLAB is useful for industry engineers, researchers, and students who are looking for open-source solutions for numerical computation. In this book you will learn by doing, avoiding technical jargon, which makes the concepts easy to learn. First you’ll see how to run basic calculations, absorbing technical complexities incrementally as you progress toward advanced topics. Throughout, the language is kept simple to ensure that readers at all levels can grasp the concepts. What You'll Learn Apply sample code to your engineering or science problems Work with MATLAB arrays, functions, and loops Use MATLAB’s plotting functions for data visualization Solve numerical computing and computational engineering problems with a MATLAB case study Who This Book Is For Engineers, scientists, researchers, and students who are new to MATLAB. Some prior programming experience would be helpful but not required.

Discover how to leverage the power of SAS for big data analytics in 'Big Data Analytics with SAS.' This book helps you unlock key techniques for preparing, analyzing, and reporting on big data effectively using SAS. Whether you're exploring integration with Hadoop and Python or mastering SAS Studio, you'll advance your analytics capabilities. What this Book will help me do Set up a SAS environment for performing hands-on data analytics tasks efficiently. Master the fundamentals of SAS programming for data manipulation and analysis. Use SAS Studio and Jupyter Notebook to interface with SAS efficiently and effectively. Perform preparatory data workflows and advanced analytics, including predictive modeling and reporting. Integrate SAS with platforms like Hadoop, SAP HANA, and Cloud Foundry for scaling analytics processes. Author(s) None Pope is a seasoned data analytics expert with extensive experience in SAS and big data platforms. With a passion for demystifying complex data workflows, None teaches SAS techniques in an approachable way. Their expert insights and practical examples empower readers to confidently analyze and report on data. Who is it for? If you're a SAS professional or a data analyst looking to expand your skills in big data analysis, this book is for you. It suits readers aiming to integrate SAS into diverse tech ecosystems or seeking to learn predictive modeling and reporting with SAS. Both beginners and those familiar with SAS can benefit.

"R Data Visualization Recipes" is a valuable resource for data professionals who want to create clear and effective data visualizations using R. Through a series of practical recipes, the book walks you through various techniques, from mastering the basics to creating advanced, interactive dashboards. By following these recipes, you'll be equipped to use R's visualization packages to their full potential. What this Book will help me do Understand and effectively use R's diverse data visualization libraries. Create polished and informative graphics with ggplot2, ggvis, and plotly. Enhance plots with interactive and animated elements to tell a compelling story. Develop expertise in creating three-dimensional and multivariate visualizations. Design custom interactive dashboards using the power of Shiny. Author(s) None Bianchi Lanzetta is an expert in data visualization and programming, bringing years of experience in using R for applications in data analysis and graphics. With a background in software development, data science, and teaching, the author shares practical insights and clear instructions. Lanzetta's approachable and methodical writing style makes even complex topics accessible. Who is it for? This book is perfect for data professionals, analysts, and scientists who know the basics of R and want to enhance their ability to communicate findings visually. Even if you are a beginner with some exposure to R's ggplot2 package or similar, you'll find the recipes approachable and methodical. The book is ideal for readers who want practical, directly applicable techniques. Whether you're looking to augment your reporting abilities or explore advanced data visualization, you'll gain valuable skills.