Employ essential tools and functions of the MATLAB and Simulink packages, which are explained and demonstrated via interactive examples and case studies. This revised edition covers features from the latest MATLAB 2022b release, as well as other features that have been released since the first edition published. This book contains dozens of simulation models and solved problems via m-files/scripts and Simulink models which will help you to learn programming and modelling essentials. You’ll become efficient with many of the built-in tools and functions of MATLAB/Simulink while solving engineering and scientific computing problems. Beginning MATLAB and Simulink, Second Edition explains various practical issues of programming and modelling in parallel by comparing MATLAB and Simulink. After studying and using this book, you'll be proficient at using MATLAB and Simulink and applying the source code and models from the book's examples as templates for your own projects in data science or engineering. What You Will Learn Master the programming and modelling essentials of MATLAB and Simulink Carry out data visualization with MATLAB Build a GUI and develop App with MATLAB Work with integration and numerical root finding methods Apply MATLAB to differential equations-based models and simulations Use MATLAB and Simulink for data science projects Who This Book Is For Engineers, programmers, data scientists, and students majoring in engineering and scientific computing who are new to MATLAB and Simulink.

The scientific field of data analysis is constantly expanding due to the rapid growth of the computer industry and the wide applicability of computational and algorithmic techniques, in conjunction with new advances in statistical, stochastic and analytic tools. There is a constant need for new, high-quality publications to cover the recent advances in all fields of science and engineering. This book is a collective work by a number of leading scientists, computer experts, analysts, engineers, mathematicians, probabilists and statisticians who have been working at the forefront of data analysis and related applications. The chapters of this collaborative work represent a cross-section of current concerns, developments and research interests in the above scientific areas. The collected material has been divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with related applications.

The scientific field of data analysis is constantly expanding due to the rapid growth of the computer industry and the wide applicability of computational and algorithmic techniques, in conjunction with new advances in statistical, stochastic and analytic tools. There is a constant need for new, high-quality publications to cover the recent advances in all fields of science and engineering. This book is a collective work by a number of leading scientists, computer experts, analysts, engineers, mathematicians, probabilists and statisticians who have been working at the forefront of data analysis and related applications. The chapters of this collaborative work represent a cross-section of current concerns, developments and research interests in the above scientific areas. The collected material has been divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with related applications.

Face analysis is essential for a large number of applications such as human-computer interaction or multimedia (e.g. content indexing and retrieval). Although many approaches are under investigation, performance under uncontrolled conditions is still not satisfactory. The variations that impact facial appearance (e.g. pose, expression, illumination, occlusion, motion blur) make it a difficult problem to solve. This book describes the progress towards this goal, from a core building block – landmark detection – to the higher level of micro and macro expression recognition. Specifically, the book addresses the modeling of temporal information to coincide with the dynamic nature of the face. It also includes a benchmark of recent solutions along with details about the acquisition of a dataset for such tasks.

In this handy, quick reference book you'll be introduced to several R data science packages, with examples of how to use each of them. All concepts will be covered concisely, with many illustrative examples using the following APIs: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more. With R 4 Data Science Quick Reference, you'll have the code, APIs, and insights to write data science-based applications in the R programming language. You'll also be able to carry out data analysis. All source code used in the book is freely available on GitHub.. What You'll Learn Implement applicable R 4 programming language specification features Import data with readr Work with categories using forcats, time and dates with lubridate, and strings with stringr Format data using tidyr and then transform that data using magrittr and dplyr Write functions with R for data science, data mining, and analytics-based applications Visualize data with ggplot2 and fit data to models using modelr Who This Book Is For Programmers new to R's data science, data mining, and analytics packages. Some prior coding experience with R in general is recommended.

Data Storytelling with Google Looker Studio is your definitive guide to creating compelling dashboards using Looker Studio. In this book, you'll journey through the principles of effective data visualization and learn how to harness Looker Studio to convey impactful data stories. Step by step, you'll acquire the skills to design, build, and refine dashboards using real-world data. What this Book will help me do Understand and apply data visualization principles to enhance data analysis and storytelling. Master the features and capabilities of Google Looker Studio for dashboard building. Learn to use a structured 3D approach - determine, design, and develop - for creating dashboards. Explore practical examples to apply your knowledge effectively in real projects. Gain insights into monitoring and measuring the impact of Looker Studio dashboards. Author(s) Sireesha Pulipati is an accomplished data analytics professional with extensive experience in business intelligence tools and data visualization. Leveraging her years of expertise, she has crafted this book to empower readers to effectively use Looker Studio. Sireesha's approachable teaching style and practical insights make complex concepts accessible to learners. Who is it for? This book is perfect for aspiring data analysts eager to master data visualization and dashboard design. It caters to beginners and requires no prior experience, making it a great starting point. Intermediate and seasoned professionals in analytics and business intelligence who are keen on using Looker Studio effectively will find immense value as well. If you aim to create insightful dashboards and refine your data storytelling skills, this book is for you.

A swift and practical introduction to building interactive data visualization apps in Python, known as dashboards. Youâ??ve seen dashboards before; think election result visualizations you can update in real time, or population maps you can filter by demographic. With the Python Dash library youâ??ll create analytic dashboards that present data in effective, usable, elegant ways in just a few lines of code. The book is fast-paced and caters to those entirely new to dashboards. It will talk you through the necessary software, then get straight into building the dashboards themselves. Youâ??ll learn the basic format of a Dash app by building a twitter analysis dashboard that maps the number of likes certain accounts gained over time. Youâ??ll build up skills through three more sophisticated projects. The first is a global analysis app that compares country data in three areas: the percentage of a population using the internet, percentage of parliament seats held by women, and CO2 emissions. Youâ??ll then build an investment portfolio dashboard, and an app that allows you to visualize and explore machine learning algorithms. In this book you will: â?¢Create and run your first Dash apps â?¢Use the pandas library to manipulate and analyze social media data â?¢Use Git to download and build on existing apps written by the pros â?¢Visualize machine learning models in your apps â?¢Create and manipulate statistical and scientific charts and maps using Plotly Dash combines several technologies to get you building dashboards quickly and efficiently. This book will do the same.

The aim of the book is to help students become data scientists. Since this requires a series of courses over a considerable period of time, the book intends to accompany students from the beginning to an advanced understanding of the knowledge and skills that define a modern data scientist. The book presents a comprehensive overview of the mathematical foundations of the programming language R and of its applications to data science.

NONPARAMETRIC STATISTICS WITH APPLICATIONS TO SCIENCE AND ENGINEERING WITH R Introduction to the methods and techniques of traditional and modern nonparametric statistics, incorporating R code Nonparametric Statistics with Applications to Science and Engineering with R presents modern nonparametric statistics from a practical point of view, with the newly revised edition including custom R functions implementing nonparametric methods to explain how to compute them and make them more comprehensible. Relevant built-in functions and packages on CRAN are also provided with a sample code. R codes in the new edition not only enable readers to perform nonparametric analysis easily, but also to visualize and explore data using R’s powerful graphic systems, such as ggplot2 package and R base graphic system. The new edition includes useful tables at the end of each chapter that help the reader find data sets, files, functions, and packages that are used and relevant to the respective chapter. New examples and exercises that enable readers to gain a deeper insight into nonparametric statistics and increase their comprehension are also included. Some of the sample topics discussed in Nonparametric Statistics with Applications to Science and Engineering with R include: Basics of probability, statistics, Bayesian statistics, order statistics, Kolmogorov–Smirnov test statistics, rank tests, and designed experiments Categorical data, estimating distribution functions, density estimation, least squares regression, curve fitting techniques, wavelets, and bootstrap sampling EM algorithms, statistical learning, nonparametric Bayes, WinBUGS, properties of ranks, and Spearman coefficient of rank correlation Chi-square and goodness-of-fit, contingency tables, Fisher exact test, MC Nemar test, Cochran’s test, Mantel–Haenszel test, and Empirical Likelihood Nonparametric Statistics with Applications to Science and Engineering with R is a highly valuable resource for graduate students in engineering and the physical and mathematical sciences, as well as researchers who need a more comprehensive, but succinct understanding of modern nonparametric statistical methods.

Use this practical guide to understand what talent intelligence is and how to use it to make evidence-based organizational decisions and add business value.

How Charts Work brings the secrets of effective data visualisation in a way that will help you bring data alive. Charts, graphs and tables are essential devices in business, but all too often they present information poorly. This book will help you: Feel confident understanding different types of charts, graphs and tables – and how to read them Recognise the true story behind the data presented and what the information really shows Know the principles and rules of how best to represent information so you can create your own information-driven (and beautiful) visuals Design visuals that people engage with, understand and act upon Don’t value design over information – present data persuasively. Find the FT Chart Doctor’s columns here - https://www.ft.com/chart-doctor

Master the tricks and techniques of business analytics consulting, specifically applicable to small-to-medium businesses (SMEs). Written to help you hone your business analytics skills, this book applies data science techniques to help solve problems and improve upon many aspects of a business' operations. SMEs are looking for ways to use data science and analytics, and this need is becoming increasingly pressing with the ongoing digital revolution. The topics covered in the books will help to provide the knowledge leverage needed for implementing data science in small business. The demand of small business for data analytics are in conjunction with the growing number of freelance data science consulting opportunities; hence this book will provide insight on how to navigate this new terrain. This book uses a do-it-yourself approach to analytics and introduces tools that are easily available online and are non-programming based. Data science will allow SMEs to understand their customer loyalty, market segmentation, sales and revenue increase etc. more clearly. Data Science and Analytics for SMEs is particularly focused on small businesses and explores the analytics and data that can help them succeed further in their business. What You'll Learn Create and measure the success of their analytics project Start your business analytics consulting career Use solutions taught in the book in practical uses cases and problems Who This Book Is For Business analytics enthusiasts who are not particularly programming inclined, small business owners and data science consultants, data science and business students, and SME (small-to-medium enterprise) analysts

Matrix-analytic methods (MAM) were introduced by Professor Marcel Neuts and have been applied to a variety of stochastic models since. In order to provide a clear and deep understanding of MAM while showing their power, this book presents MAM concepts and explains the results using a number of worked-out examples. This book’s approach will inform and kindle the interest of researchers attracted to this fertile field. To allow readers to practice and gain experience in the algorithmic and computational procedures of MAM, Introduction to Matrix Analytic Methods in Queues 1 provides a number of computational exercises. It also incorporates simulation as another tool for studying complex stochastic models, especially when the state space of the underlying stochastic models under analytic study grows exponentially. The book’s detailed approach will make it more accessible for readers interested in learning about MAM in stochastic models.

As platforms for sharing, re-using and storing data, research data repositories are integral to open science policy. This book provides a comprehensive approach to these data repositories, their functionalities, uses, issues and prospects. Taking France as an example, the current landscape of data repositories is considered, including discussion of the idea of a national repository and a comparative study of several national systems. The international re3data directory is outlined and a collection of six case studies of model repositories, both public and private, are detailed (CDS, Data INRAE, SEANOE, Nakala, Figshare and Data Mendeley). Research Data Sharing and Valorization also includes appendices containing a number of websites and reference texts from the French Ministry of Higher Education, Research and Innovation, and the CNRS. To the authors’ knowledge, it is the first book to be entirely devoted to these new platforms and is aimed at researchers, teachers, students and professionals working with scientific and technical data and information.

While simulation has a vast area of application, this textbook focuses on the use of simulation to analyse business processes.

Microsoft Power BI is a data analytics and visualization tool powerful enough for the most demanding data scientists, but accessible enough for everyday use for anyone who needs to get more from data. The market has many books designed to train and equip professional data analysts to use Power BI, but few of them make this tool accessible to anyone who wants to get up to speed on their own. This streamlined intro to Power BI covers all the foundational aspects and features you need to go from "zero to hero" with data and visualizations. Whether you work with large, complex datasets or work in Microsoft Excel, author Jeremey Arnold shows you how to teach yourself Power BI and use it confidently as a regular data analysis and reporting tool. You'll learn how to: Import, manipulate, visualize, and investigate data in Power BI Approach solutions for both self-service and enterprise BI Use Power BI in your organization's business intelligence strategy Produce effective reports and dashboards Create environments for sharing reports and managing data access with your team Determine the right solution for using Power BI offerings based on size, security, and computational needs

A start-to-finish guide to one of the most useful programming languages for researchers in a variety of fields In the newly revised Third Edition of The R Book, a team of distinguished teachers and researchers delivers a user-friendly and comprehensive discussion of foundational and advanced topics in the R software language, which is used widely in science, engineering, medicine, economics, and other fields. The book is designed to be used as both a complete text—readable from cover to cover—and as a reference manual for practitioners seeking authoritative guidance on particular topics. This latest edition offers instruction on the use of the RStudio GUI, an easy-to-use environment for those new to R. It provides readers with a complete walkthrough of the R language, beginning at a point that assumes no prior knowledge of R and very little previous knowledge of statistics. Readers will also find: A thorough introduction to fundamental concepts in statistics and step-by-step roadmaps to their implementation in R; Comprehensive explorations of worked examples in R; A complementary companion website with downloadable datasets that are used in the book; In-depth examination of essential R packages. Perfect for undergraduate and postgraduate students of science, engineering, medicine economics, and geography, The R Book will also earn a place in the libraries of social sciences professionals.

SOFR Futures and Options is the practical guide through the maze of the transition from LIBOR. In the first section, it provides an in-depth explanation of the concepts involved: The repo market and the construction of SOFR SOFR-based lending markets and the term rate The secured-unsecured basis SOFR futures and options and their spread contracts Margin and convexity Applying these insights, the second section offers detailed worked-through examples of hedging loans, swaps, bonds, and floors with SOFR futures and options, supported by interactive spreadsheets accessible on the web. The gold standard resource for professionals working at financial institutions, SOFR Futures and Options also belongs in the libraries of students of finance and business, as well as those preparing for the Chartered Financial Analyst exam.

Harness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. In this book, you’ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. This edition includes new and expanded projects, and covers generative deep learning and reinforcement learning. Over the course of the book, you'll learn to model complex systems and apply deep learning to problems in those areas. Applications include: Aircraft navigation An aircraft that lands on Titan, the moon of Saturn, using reinforcement learning Stock market prediction Natural language processing Music creation usng generative deep learning Plasma control Earth sensor processing for spacecraft MATLAB Bluetooth data acquisition applied to dance physics What You Will Learn Explore deep learning using MATLAB and compare it to algorithms Write a deep learning function in MATLAB and train it with examples Use MATLAB toolboxes related to deep learning Implement tokamak disruption prediction Now includes reinforcement learning Who This Book Is For Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.

Use this practical guide to take a data-driven approach to workforce planning and continuously analyse the organization to allow the business to meet its strategic objectives.

Microsoft Power Apps Cookbook, Second Edition, is your ultimate guide to unlocking the full potential of Microsoft's low-code platform for building custom business applications. From practical recipes that solve real-world challenges to advanced techniques, this book empowers you with tools to streamline processes and elevate your organization's productivity. What this Book will help me do Master the skills to design and implement canvas and model-driven apps to fit your business requirements. Utilize Microsoft Dataverse effectively as the data backbone for your applications. Automate business workflows dynamically using Power Automate and robotic process automation techniques. Expand your application's capabilities with AI Builder and mixed reality integrations for innovative solutions. Harness the Power Apps Component Framework to build powerful, customized extensions to meet enterprise-grade needs. Author(s) Eickhel Mendoza is a seasoned professional in low-code application development, specializing in the Microsoft Power Platform. With extensive experience in helping organizations implement effective solutions and improve productivity, Eickhel brings a pragmatic, hands-on approach to technical writing. His deep understanding of Power Apps and user-centric teaching style make this book an invaluable resource for developers and citizen creators alike. Who is it for? This book is designed for information workers, IT professionals, and citizen developers aiming to build custom applications tailored to their organizational needs. It is equally beneficial for traditional application developers interested in leveraging a rapid application development platform. A basic understanding of the Microsoft Power Platform ecosystem is recommended to make the most out of this book.

Discover how to manage and optimize the life cycle of your data science projects with Comet! By the end of this book, you will master preparing, analyzing, building, and deploying models, as well as integrating Comet into your workflow. What this Book will help me do Master managing data science workflows with Comet. Confidently prepare and analyze your data for effective modeling. Deploy and monitor machine learning models using Copet tools. Integrate Comet with DevOps and GitLab workflows for production readiness. Apply Comet to advanced topics like NLP, deep learning, and time series analysis. Author(s) Angelica Lo Duca is an experienced author and data scientist with years of expertise in data science workflows and tools. She brings practical insights into integrating platforms like Comet into modern data science tasks. Who is it for? If you are a data science practitioner or programmer looking to understand and implement efficient project lifecycles using Comet, this book is tailored for you. A basic backdrop in data science and programming is highly recommended, but prior expertise in Comet is unnecessary.

Learning Tableau 2022 is your comprehensive guide to mastering Tableau, one of the most popular tools for data visualization and analysis. Through this book, you will understand how to build impactful visualizations, create interactive dashboards, and tell compelling stories with data. With updated coverage of Tableau 2022's latest features, this book will take your data storytelling skills to the next level. What this Book will help me do Develop effective visualizations and dashboards to present complex data intuitively. Enhance data analysis with Tableau's advanced features like clustering, AI extensions, and Explain Data. Utilize calculations and parameters for tailoring and enriching analytics. Optimize workflows for data cleaning and preparation using Tableau Prep Builder. Confidently leverage Tableau for interlinking datasets and performing geospatial analysis. Author(s) Joshua N. Milligan, the author of Learning Tableau 2022, is a seasoned Tableau Zen Master. He has years of experience helping individuals and businesses transform their data into actionable insights through visualization and analysis. With a focus on clarity and practical applications, Joshua explains complex concepts in an approachable manner and equips readers with the skills to bring their ideas to life in Tableau. Who is it for? This book is ideal for business intelligence developers, data analysts, or any professional eager to improve their data visualization skills. Both beginners looking to understand Tableau from the ground up and intermediate users aiming to explore advanced Tableau techniques will find it valuable. A Tableau license and a thirst for learning are all you'll need to embark on this data visualization journey.

This book provides all you need to find data from external sources and load and transform that data into Power BI where you can mine it for business insights and a competitive edge. This ranges from connecting to corporate databases such as Azure SQL and SQL Server to file-based data sources, and cloud- and web-based data sources. The book also explains the use of Direct Query and Live Connect to establish instant connections to databases and data warehouses and avoid loading data. The book provides detailed guidance on techniques for transforming inbound data into normalized data sets that are easy to query and analyze. This covers data cleansing, data modification, and standardization as well as merging source data into robust data structures that can feed into your data model. You will learn how to pivot and transpose data and extrapolate missing values as well as harness external programs such as R and Python into a Power Query data flow. You also will see how to handle errors in source data and extend basic data ingestion to create robust and parameterized data load and transformation processes. Everything in this book is aimed at helping you deliver compelling and interactive insight with remarkable ease using Power BI’s built-in data load and transformation tools. What You Will Learn Connect Power BI to a range of external data sources Prepare data from external sources for easy analysis in Power BI Cleanse data from duplicates, outliers, and other bad values Make live connections from which to refresh data quickly and easily Apply advanced techniques to interpolate missing data Who This Book Is For All Power BI users from beginners to super users. Any user of the world’s leading dashboarding toolcan leverage the techniques explained in this book to turbo-charge their data preparation skills and learn how a wide range of external data sources can be harnessed and loaded into Power BI to drive their analytics. No previous knowledge of working with data, databases, or external data sources is required—merely the need to find, transform, and load data into Power BI..

This book, "Codeless Time Series Analysis with KNIME," serves as your practical guide to mastering time series analysis using the KNIME Analytics Platform. By diving into this book, you'll explore a variety of statistical and machine learning techniques applied explicitly to real-world time series scenarios, helping you build predictive and analysis models effectively. What this Book will help me do Leverage KNIME's powerful tools to preprocess and prepare time series data for analysis. Visualize and dissect time series data into its components like trends and seasonality. Apply statistical models like ARIMA to analyze and forecast continuous data. Train and utilize neural networks including LSTM models for predictive analytics. Integrate external tools like Spark and H2O to enhance your forecasting workflows. Author(s) The authors, including experts from KNIME AG, Corey Weisinger, Maarit Widmann, and Daniele Tonini, collectively bring extensive experience in data analytics and time series modeling. Their expertise with KNIME's tools and real-world time series analysis applications ensures readers gain insights into practical, hands-on techniques. Who is it for? This book is ideally suited for data analysts and scientists eager to explore time series analysis through codeless methodologies. Beginners will benefit from the introductory explanations, while seasoned professionals will find value in the advanced topics and real-world examples. A basic understanding of the KNIME platform is recommended to get the most from this book.