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.

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.

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.

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

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.

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, "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.

Begin working with the Grafana data visualization platform. This book is a “how-to manual” for deploying and administering Grafana, creating real-time dashboards and alerts, exploring the data you have, and even synthesizing new data by combining and manipulating data from multiple different sources. You’ll be able to see and manage data on any scale, from your laptop or a Raspberry Pi to a production datacenter or even a multi-region cloud environment! Getting Started with Grafana takes a hands-on approach. You’ll learn by doing with easy-to-follow examples along with pointers to more resources to help you go deeper. The skills you’ll learn will help you provide business value by monitoring your operations in real time and reacting to changing circumstances as they occur. You’ll be able to derive new insights from your existing data through Grafana’s powerful and beautiful graphing capabilities, and you’ll be able to share your dashboards with colleagues soeveryone in your organization can benefit. What You Will Learn Connect to data "where it lives” and work with multiple sources of data Build beautiful and informative dashboards that show real-time status Deploy Grafana at any scale and manage it efficiently Integrate with other enterprise systems such as LDAP or Active Directory Automate creation and deployment of Grafana, dashboards, and alerts Understand what is available in the Enterprise version of Grafana Who This Book Is For Anyone who has data that they want to understand visually, IT professionals who work with multiple sources of data on a regular basis and need to make sense of the confusion that this data sprawl causes, and people who learn best by doing and want to get hands-on experience quickly with a project and then grow their knowledge

A unified and systematic theoretical framework for solving problems related to finite impulse response (FIR) estimate Optimal and Robust State Estimation: Finite Impulse Response (FIR) and Kalman Approaches is a comprehensive investigation into batch state estimators and recursive forms. The work begins by introducing the reader to the state estimation approach and provides a brief historical overview. Next, the work discusses the specific properties of finite impulse response (FIR) state estimators. Further chapters give the basics of probability and stochastic processes, discuss the available linear and nonlinear state estimators, deal with optimal FIR filtering, and consider a limited memory batch and recursive algorithms. Other topics covered include solving the q-lag FIR smoothing problem, introducing the receding horizon (RH) FIR state estimation approach, and developing the theory of FIR state estimation under disturbances. The book closes by discussing the theory of FIR state estimation for uncertain systems and providing several applications where the FIR state estimators are used effectively. Key concepts covered in the work include: A holistic overview of the state estimation approach, which arose from the need to know the internal state of a real system, given that the input and output are both known Optimal, optimal unbiased, maximum likelihood, and unbiased and robust finite impulse response (FIR) structures FIR state estimation approach along with the infinite impulse response (IIR) and Kalman approaches Cost functions and the most critical properties of FIR and IIR state estimates Optimal and Robust State Estimation: Finite Impulse Response (FIR) and Kalman Approaches was written for professionals in the fields of microwave engineering, system engineering, and robotics who wish to move towards solving finite impulse response (FIR) estimate issues in both theoretical and practical applications. Graduate and senior undergraduate students with coursework dealing with state estimation will also be able to use the book to gain a valuable foundation of knowledge and become more adept in their chosen fields of study.

Doing Statistical Analysis looks at three kinds of statistical research questions – descriptive, associational and inferential – and shows students how to conduct statistical analyses and interpret the results.

THE GUIDE FOR ANYONE AFRAID TO LEARN STATISTICS & ANALYTICS UPDATED WITH NEW EXAMPLES & EXERCISES This book discusses statistics and analytics using plain language and avoiding mathematical jargon. If you thought you couldn't learn these data analysis subjects because they were too technical or too mathematical, this book is for you! This edition delivers more everyday examples and end-of-chapter exercises and contains updated instructions for using Microsoft Excel. You'll use downloadable data sets and spreadsheet solutions, template-based solutions you can put right to work. Using this book, you will understand the important concepts of statistics and analytics, including learning the basic vocabulary of these subjects. Create tabular and visual summaries and learn to avoid common charting errors Gain experience working with common descriptive statistics measures including the mean, median, and mode; and standard deviation and variance, among others Understand the probability concepts that underlie inferential statistics Learn how to apply hypothesis tests, using Z, t, chi-square, ANOVA, and other techniques Develop skills using regression analysis, the most commonly-used Inferential statistical method Explore results produced by predictive analytics software Choose the right statistical or analytic techniques for any data analysis task Optionally, read the Equation Blackboards, designed for readers who want to learn about the mathematical foundations of selected methods ...

Examine select retail business scenarios to learn basic mathematics, as well as probability and statistics required to analyze big data. This book focuses on useful and imperative applied analytics needed to build a retail business and explains mathematical concepts essential for decision making and communication in retail business environments. Everyone is a buyer or seller of products these days whether through a physical department store, Amazon, or their own business website. This book is a step-by-step guide to understanding and managing the mechanics of markups, markdowns, and basic statistics, math and computers that will help in your retail business. You'll tackle what to do with data once it is has accumulated and see how to arrange the data using descriptive statistics, primarily means, median, and mode, and then how to read the corresponding charts and graphs. Analytics for Retail is your path to creating visualrepresentations that powerfully communicate information and drive decisions. What You'll Learn Review standard statistical concepts to enhance your understanding of retail data Understand the concepts of markups, markdowns and profit margins, and probability Conduct an A/B testing email campaign with all the relevant analytics calculated and explained Who This Book Is For This is a primer book for anyone in the field of retail that needs to learn or refresh their skills or for a reader who wants to move in their company to a more analytical position.

A thought-provoking and startlingly insightful reworking of the science of prediction In Prediction Revisited: The Importance of Observation, a team of renowned experts in the field of data-driven investing delivers a ground-breaking reassessment of the delicate science of prediction for anyone who relies on data to contemplate the future. The book reveals why standard approaches to prediction based on classical statistics fail to address the complexities of social dynamics, and it provides an alternative method based on the intuitive notion of relevance. The authors describe, both conceptually and with mathematical precision, how relevance plays a central role in forming predictions from observed experience. Moreover, they propose a new and more nuanced measure of a prediction’s reliability. Prediction Revisited also offers: Clarifications of commonly accepted but less commonly understood notions of statistics Insight into the efficacy of traditional prediction models in a variety of fields Colorful biographical sketches of some of the key prediction scientists throughout history Mutually supporting conceptual and mathematical descriptions of the key insights and methods discussed within With its strikingly fresh perspective grounded in scientific rigor, Prediction Revisited is sure to earn its place as an indispensable resource for data scientists, researchers, investors, and anyone else who aspires to predict the future from the data-driven lessons of the past.

The Tableau Workshop offers a comprehensive, hands-on guide to mastering data visualization with Tableau. Through practical exercises and engaging examples, you will learn how to prepare, analyze, and visualize data to uncover valuable business insights. By completing this book, you will confidently understand the key concepts and tools needed to create impactful data-driven visual stories. What this Book will help me do Master the use of Tableau Desktop and Tableau Prep for data visualization tasks. Gain the ability to prepare and process data for effective analysis. Learn to choose and utilize the most appropriate chart types for different scenarios. Develop the skills to create interactive dashboards that engage stakeholders. Understand how to perform calculations to extract deeper insights from data. Author(s) Sumit Gupta, None Pinto, Shweta Savale, JC Gillet None, and None Cherven are experts in the field of data analytics and visualization. With diverse backgrounds in business intelligence and hands-on experience with industry tools like Tableau, they bring valuable insights to this book. Their collaborative effort offers practical, real-world knowledge tailored to help learners excel in Tableau and data visualization. With their passion for making technical concepts accessible, they guide readers step by step through their learning journey. Who is it for? This book is ideal for professionals, analysts, or students looking to delve into the world of data visualization with Tableau. Whether you're a complete beginner seeking foundational knowledge, or an intermediate user aiming to refine your skills, this book offers the practical insights you need. It's designed for those who want to master Tableau tools, explore meaningful data insights, and effectively communicate them through engaging dashboards and stories.

It’s time for some truly “Excel-lent” spreadsheet reporting Beneath the seemingly endless rows and columns of cells, the latest version of Microsoft Excel boasts an astonishing variety of features and capabilities. But how do you go about tapping into some of that power without spending all of your days becoming a spreadsheet guru? It’s easy. You grab a copy of the newest edition of Excel Dashboards & Reports For Dummies and get ready to blow the pants off your next presentation audience! With this book, you’ll learn how to transform those rows and columns of data into dynamic reports, dashboards, and visualizations. You’ll draw powerful new insights from your company’s numbers to share with your colleagues – and seem like the smartest person in the room while you’re doing it. Excel Dashboards & Reports For Dummies offers: Complete coverage of the latest version of Microsoft Excel provided in the Microsoft 365 subscription Strategies to automate your reporting so you don’t have to manually crunch the numbers every week, month, quarter, or year Ways to get new perspectives on old data, visualizing it so you can find solutions no one else has seen before If you’re ready to make your company’s numbers and spreadsheets dance, it’s time to get the book that’ll have them moving to your tune in no time. Get Excel Dashboards & Reports For Dummies today.

The essential, intermediate and advanced topics of Simulink are covered in the book. The concept of multi-domain physical modeling concept and tools in Simulink are illustrated with examples for engineering systems and multimedia information. The combination of Simulink and numerical optimization methods provides new approaches for solving problems, where solutions are not known otherwise.

Time Series Analysis on AWS is your guide to building and deploying powerful forecasting models and identifying anomalies in your time series data. With this book, you will explore effective strategies for modern time series analysis using Amazon Web Services' powerful AI/ML tools. What this Book will help me do Master the fundamental concepts of time series and its applications using industry-relevant examples. Understand time series forecasting with Amazon Forecast and how to deliver actionable business insights. Build and deploy anomaly detection systems using Amazon Lookout for Equipment for predictive maintenance. Learn to utilize Amazon Lookout for Metrics to identify business operational anomalies effectively. Gain practical experience applying AWS ML tools to real-world time series data challenges. Author(s) None Hoarau is a data scientist with extensive experience in utilizing machine learning to solve real-world problems. Combining strong programming skills with domain expertise, they focus on developing applications leveraging AWS AI services. This book reflects their passion for making technical topics accessible and actionable for professionals. Who is it for? This book is ideal for data analysts, business analysts, and data scientists eager to enhance their skills in time series analysis. It suits readers familiar with statistical concepts but new to machine learning. If you're aiming to solve business problems using data and AWS tools, this resource is tailored for you.

Causal inference lies at the heart of our ability to understand why things happen by helping us predict the results of our actions. This process is vital for businesses that aspire to turn data and information into valuable knowledge. With this report, data scientists and analysts will learn a principled way of thinking about causality, using a suite of causal inference techniques now available. Authors Hugo Bowne-Anderson, a data science consultant, and Mike Loukides, vice president of content strategy at O'Reilly Media, introduce causality and discuss randomized control trials (RCTs), key aspects of causal graph theory, and much-needed techniques from econometrics. You'll explore: Techniques from econometrics, including randomized control trials, the causality gold standard used in A/B-testing The constant-effects model for dealing with all things not being equal across the groups you're comparing Regression for dealing with confounding variables and selection bias Instrumental variables to estimate causal relationships in situations where regression won't work Techniques from causal graph theory including forks and colliders, the graphical tools for representing common causal patterns Backdoor and front-door adjustments for making causal inferences in the presence of confounders

Become a stats superstar by using Excel to reveal the powerful secrets of statistics Microsoft Excel offers numerous possibilities for statistical analysis—and you don’t have to be a math wizard to unlock them. In Statistical Analysis with Excel For Dummies, fully updated for the 2021 version of Excel, you’ll hit the ground running with straightforward techniques and practical guidance to unlock the power of statistics in Excel. Bypass unnecessary jargon and skip right to mastering formulas, functions, charts, probabilities, distributions, and correlations. Written for professionals and students without a background in statistics or math, you’ll learn to create, interpret, and translate statistics—and have fun doing it! In this book you’ll find out how to: Understand, describe, and summarize any kind of data, from sports stats to sales figures Confidently draw conclusions from your analyses, make accurate predictions, and calculate correlations Model the probabilities of future outcomes based on past data Perform statistical analysis on any platform: Windows, Mac, or iPad Access additional resources and practice templates through Dummies.com For anyone who’s ever wanted to unleash the full potential of statistical analysis in Excel—and impress your colleagues or classmates along the way—Statistical Analysis with Excel For Dummies walks you through the foundational concepts of analyzing statistics and the step-by-step methods you use to apply them.

In recent years, scientific research and translation medicine have placed increased emphasis on computational methodology and data curation across many disciplines, both to advance underlying science and to instantiate precision-medicine protocols in the lab and in clinical practice. The nexus of concerns related to oncology, cardiology, and virology (SARS-CoV-2) presents a fortuitous context within which to examine the theory and practice of biomedical data curation. Innovative Data Integration and Conceptual Space Modeling for COVID, Cancer, and Cardiac Care argues that a well-rounded approach to data modeling should optimally embrace multiple perspectives inasmuch as data-modeling is neither a purely formal nor a purely conceptual discipline, but rather a hybrid of both. On the one hand, data models are designed for use by computer software components, and are, consequently, constrained by the mechanistic demands of software environments; data modeling strategies must accept the formal rigors imposed by unambiguous data-sharing and query-evaluation logic. In particular, data models are not well-suited for software-level deployment if such models do not translate seamlessly to clear strategies for querying data and ensuring data integrity as information is moved across multiple points. On the other hand, data modeling is, likewise, constrained by human conceptual tendencies, because the information which is managed by databases and data networks is ultimately intended to be visualized/utilized by humans as the end-user. Thus, at the intersection of both formal and humanistic methodology, data modeling takes on elements of both logico-mathematical frameworks (e.g., type systems and graph theory) and conceptual/philosophical paradigms (e.g., linguistics and cognitive science). The authors embrace this two-sided aspect of data models by seeking non-reductionistic points of convergence between formal and humanistic/conceptual viewpoints, and by leveraging biomedical contexts (viz., COVID, Cancer, and Cardiac Care) so as to provide motivating examples and case-studies in this volume. Provides an analysis of how conceptual spaces and related cognitive linguistic approaches can inspire programming and query-processing models Outlines the vital role that data modeling/curation has played in significant medical breakthroughs Presents readers with an overview of how information-management approaches intersect with precision medicine, providing case studies of data-modeling in concrete scientific practice Explores applications of image analysis and computer vision in the context of precision medicine Examines the role of technology in scientific publishing, replication studies, and dataset curation

Change Detection and Image Time Series Analysis 1 presents a wide range of unsupervised methods for temporal evolution analysis through the use of image time series associated with optical and/or synthetic aperture radar acquisition modalities. Chapter 1 introduces two unsupervised approaches to multiple-change detection in bi-temporal multivariate images, with Chapters 2 and 3 addressing change detection in image time series in the context of the statistical analysis of covariance matrices. Chapter 4 focuses on wavelets and convolutional-neural filters for feature extraction and entropy-based anomaly detection, and Chapter 5 deals with a number of metrics such as cross correlation ratios and the Hausdorff distance for variational analysis of the state of snow. Chapter 6 presents a fractional dynamic stochastic field model for spatio temporal forecasting and for monitoring fast-moving meteorological events such as cyclones. Chapter 7 proposes an analysis based on characteristic points for texture modeling, in the context of graph theory, and Chapter 8 focuses on detecting new land cover types by classification-based change detection or feature/pixel based change detection. Chapter 9 focuses on the modeling of classes in the difference image and derives a multiclass model for this difference image in the context of change vector analysis.

INTRODUCTION TO PROBABILITY Discover practical models and real-world applications of multivariate models useful in engineering, business, and related disciplines In Introduction to Probability: Multivariate Models and Applications, a team of distinguished researchers delivers a comprehensive exploration of the concepts, methods, and results in multivariate distributions and models. Intended for use in a second course in probability, the material is largely self-contained, with some knowledge of basic probability theory and univariate distributions as the only prerequisite. This textbook is intended as the sequel to Introduction to Probability: Models and Applications. Each chapter begins with a brief historical account of some of the pioneers in probability who made significant contributions to the field. It goes on to describe and explain a critical concept or method in multivariate models and closes with two collections of exercises designed to test basic and advanced understanding of the theory. A wide range of topics are covered, including joint distributions for two or more random variables, independence of two or more variables, transformations of variables, covariance and correlation, a presentation of the most important multivariate distributions, generating functions and limit theorems. This important text: Includes classroom-tested problems and solutions to probability exercises Highlights real-world exercises designed to make clear the concepts presented Uses Mathematica software to illustrate the text’s computer exercises Features applications representing worldwide situations and processes Offers two types of self-assessment exercises at the end of each chapter, so that students may review the material in that chapter and monitor their progress Perfect for students majoring in statistics, engineering, business, psychology, operations research and mathematics taking a second course in probability, Introduction to Probability: Multivariate Models and Applications is also an indispensable resource for anyone who is required to use multivariate distributions to model the uncertainty associated with random phenomena.

Learn the core aspects of NumPy, Matplotlib, and Pandas, and use them to write programs with Python 3. This book focuses heavily on various data visualization techniques and will help you acquire expert-level knowledge of working with Matplotlib, a MATLAB-style plotting library for Python programming language that provides an object-oriented API for embedding plots into applications. You'll begin with an introduction to Python 3 and the scientific Python ecosystem. Next, you'll explore NumPy and ndarray data structures, creation routines, and data visualization. You'll examine useful concepts related to style sheets, legends, and layouts, followed by line, bar, and scatter plots. Chapters then cover recipes of histograms, contours, streamplots, and heatmaps, and how to visualize images and audio with pie and polar charts. Moving forward, you'll learn how to visualize with pcolor, pcolormesh, and colorbar, and how to visualize in 3D in Matplotlib, create simple animations, and embed Matplotlib with different frameworks. The concluding chapters cover how to visualize data with Pandas and Matplotlib, Seaborn, and how to work with the real-life data and visualize it. After reading Hands-on Matplotlib you'll be proficient with Matplotlib and able to comfortably work with ndarrays in NumPy and data frames in Pandas. What You'll Learn Understand Data Visualization and Python using Matplotlib Review the fundamental data structures in NumPy and Pandas Work with 3D plotting, visualizations, and animations Visualize images and audio data Who This Book Is For Data scientists, machine learning engineers and software professionals with basic programming skills.