Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone's career as a data analyst.
Load, wrangle, and analyze your data using the world's most powerful statistical programming language About This Book Load, manipulate and analyze data from different sources Gain a deeper understanding of fundamentals of applied statistics A practical guide to performing data analysis in practice Who This Book Is For Whether you are learning data analysis for the first time, or you want to deepen the understanding you already have, this book will prove to an invaluable resource. If you are looking for a book to bring you all the way through the fundamentals to the application of advanced and effective analytics methodologies, and have some prior programming experience and a mathematical background, then this is for you. What You Will Learn Navigate the R environment Describe and visualize the behavior of data and relationships between data Gain a thorough understanding of statistical reasoning and sampling Employ hypothesis tests to draw inferences from your data Learn Bayesian methods for estimating parameters Perform regression to predict continuous variables Apply powerful classification methods to predict categorical data Handle missing data gracefully using multiple imputation Identify and manage problematic data points Employ parallelization and Rcpp to scale your analyses to larger data Put best practices into effect to make your job easier and facilitate reproducibility In Detail Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. With over 7,000 user contributed packages, it's easy to find support for the latest and greatest algorithms and techniques. Starting with the basics of R and statistical reasoning, Data Analysis with R dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with “messy data”, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone's career as a data analyst. Style and approach Learn data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach.
Master the art of predictive modeling About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Familiarize yourself with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, Naive Bayes, decision trees, text mining and so on. We emphasize important concepts, such as the bias-variance trade-off and over-fitting, which are pervasive in predictive modeling Who This Book Is For If you work with data and want to become an expert in predictive analysis and modeling, then this Learning Path will serve you well. It is intended for budding and seasoned practitioners of predictive modeling alike. You should have basic knowledge of the use of R, although it's not necessary to put this Learning Path to great use. What You Will Learn Get to know the basics of R's syntax and major data structures Write functions, load data, and install packages Use different data sources in R and know how to interface with databases, and request and load JSON and XML Identify the challenges and apply your knowledge about data analysis in R to imperfect real-world data Predict the future with reasonably simple algorithms Understand key data visualization and predictive analytic skills using R Understand the language of models and the predictive modeling process In Detail Predictive analytics is a field that uses data to build models that predict a future outcome of interest. It can be applied to a range of business strategies and has been a key player in search advertising and recommendation engines. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. This Learning Path will provide you with all the steps you need to master the art of predictive modeling with R. We start with an introduction to data analysis with R, and then gradually you'll get your feet wet with predictive modeling. You will get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. You will be able to solve the difficulties relating to performing data analysis in practice and find solutions to working with “messy data”, large data, communicating results, and facilitating reproducibility. You will then perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. By the end of this Learning Path, you will have explored and tested the most popular modeling techniques in use on real-world data sets and mastered a diverse range of techniques in predictive analytics. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Data Analysis with R, Tony Fischetti Learning Predictive Analytics with R, Eric Mayor Mastering Predictive Analytics with R, Rui Miguel Forte Style and approach Learn data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach. This is a practical course, which analyzes compelling data about life, health, and death with the help of tutorials. It offers you a useful way of interpreting the data that's specific to this course, but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of predictive modeling.
Master the art of building analytical models using R About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Build and customize publication-quality visualizations of powerful and stunning R graphs Develop key skills and techniques with R to create and customize data mining algorithms Use R to optimize your trading strategy and build up your own risk management system Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R Who This Book Is For This course is for data scientist or quantitative analyst who are looking at learning R and take advantage of its powerful analytical design framework. It's a seamless journey in becoming a full-stack R developer. What You Will Learn Describe and visualize the behavior of data and relationships between data Gain a thorough understanding of statistical reasoning and sampling Handle missing data gracefully using multiple imputation Create diverse types of bar charts using the default R functions Familiarize yourself with algorithms written in R for spatial data mining, text mining, and so on Understand relationships between market factors and their impact on your portfolio Harness the power of R to build machine learning algorithms with real-world data science applications Learn specialized machine learning techniques for text mining, big data, and more In Detail The R learning path created for you has five connected modules, which are a mini-course in their own right. As you complete each one, you'll have gained key skills and be ready for the material in the next module! This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best practices into effect to make your job easier and facilitate reproducibility. The second place to explore is R Graphs, which will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. You'll learn how to produce, customize, and publish advanced visualizations using this popular and powerful framework. With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well-versed with various financial techniques using R and will be able to place good bets while making financial decisions. Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. You'll also learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, and so on. Style and approach Learn data analysis, data visualization techniques, data mining, and machine learning all using R and also learn to build models in quantitative finance using this powerful language.
Load, wrangle, and analyze your data using the world's most powerful statistical programming language About This Book Load, manipulate and analyze data from different sources Gain a deeper understanding of fundamentals of applied statistics A practical guide to performing data analysis in practice Who This Book Is For Whether you are learning data analysis for the first time, or you want to deepen the understanding you already have, this book will prove to an invaluable resource. If you are looking for a book to bring you all the way through the fundamentals to the application of advanced and effective analytics methodologies, and have some prior programming experience and a mathematical background, then this is for you. What You Will Learn Navigate the R environment Describe and visualize the behavior of data and relationships between data Gain a thorough understanding of statistical reasoning and sampling Employ hypothesis tests to draw inferences from your data Learn Bayesian methods for estimating parameters Perform regression to predict continuous variables Apply powerful classification methods to predict categorical data Handle missing data gracefully using multiple imputation Identify and manage problematic data points Employ parallelization and Rcpp to scale your analyses to larger data Put best practices into effect to make your job easier and facilitate reproducibility In Detail Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. With over 7,000 user contributed packages, it's easy to find support for the latest and greatest algorithms and techniques. Starting with the basics of R and statistical reasoning, Data Analysis with R dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with “messy data”, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone's career as a data analyst. Style and approach Learn data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach.
Master the art of predictive modeling About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Familiarize yourself with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, Naive Bayes, decision trees, text mining and so on. We emphasize important concepts, such as the bias-variance trade-off and over-fitting, which are pervasive in predictive modeling Who This Book Is For If you work with data and want to become an expert in predictive analysis and modeling, then this Learning Path will serve you well. It is intended for budding and seasoned practitioners of predictive modeling alike. You should have basic knowledge of the use of R, although it's not necessary to put this Learning Path to great use. What You Will Learn Get to know the basics of R's syntax and major data structures Write functions, load data, and install packages Use different data sources in R and know how to interface with databases, and request and load JSON and XML Identify the challenges and apply your knowledge about data analysis in R to imperfect real-world data Predict the future with reasonably simple algorithms Understand key data visualization and predictive analytic skills using R Understand the language of models and the predictive modeling process In Detail Predictive analytics is a field that uses data to build models that predict a future outcome of interest. It can be applied to a range of business strategies and has been a key player in search advertising and recommendation engines. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. This Learning Path will provide you with all the steps you need to master the art of predictive modeling with R. We start with an introduction to data analysis with R, and then gradually you'll get your feet wet with predictive modeling. You will get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. You will be able to solve the difficulties relating to performing data analysis in practice and find solutions to working with “messy data”, large data, communicating results, and facilitating reproducibility. You will then perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. By the end of this Learning Path, you will have explored and tested the most popular modeling techniques in use on real-world data sets and mastered a diverse range of techniques in predictive analytics. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Data Analysis with R, Tony Fischetti Learning Predictive Analytics with R, Eric Mayor Mastering Predictive Analytics with R, Rui Miguel Forte Style and approach Learn data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach. This is a practical course, which analyzes compelling data about life, health, and death with the help of tutorials. It offers you a useful way of interpreting the data that's specific to this course, but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of predictive modeling.
Master the art of building analytical models using R About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Build and customize publication-quality visualizations of powerful and stunning R graphs Develop key skills and techniques with R to create and customize data mining algorithms Use R to optimize your trading strategy and build up your own risk management system Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R Who This Book Is For This course is for data scientist or quantitative analyst who are looking at learning R and take advantage of its powerful analytical design framework. It's a seamless journey in becoming a full-stack R developer. What You Will Learn Describe and visualize the behavior of data and relationships between data Gain a thorough understanding of statistical reasoning and sampling Handle missing data gracefully using multiple imputation Create diverse types of bar charts using the default R functions Familiarize yourself with algorithms written in R for spatial data mining, text mining, and so on Understand relationships between market factors and their impact on your portfolio Harness the power of R to build machine learning algorithms with real-world data science applications Learn specialized machine learning techniques for text mining, big data, and more In Detail The R learning path created for you has five connected modules, which are a mini-course in their own right. As you complete each one, you'll have gained key skills and be ready for the material in the next module! This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best practices into effect to make your job easier and facilitate reproducibility. The second place to explore is R Graphs, which will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. You'll learn how to produce, customize, and publish advanced visualizations using this popular and powerful framework. With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well-versed with various financial techniques using R and will be able to place good bets while making financial decisions. Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. You'll also learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, and so on. Style and approach Learn data analysis, data visualization techniques, data mining, and machine learning all using R and also learn to build models in quantitative finance using this powerful language.
An intimate, revealing portrait of Frank Sinatra-from the man closest to the famous singer during the last decade of his life. More than a hundred books have been written about legendary crooner and actor Frank Sinatra. Every detail of his life seems to captivate: his career, his romantic relationships, his personality, his businesses, his style. But a hard-to-pin-down quality has always clung to him-a certain elusiveness that emerges again and again in retrospective depictions. Until now. From Sinatra's closest confidant and an eventual member of his management team, Tony Oppedisano, comes an extraordinarily intimate look at the singing idol. Deep into the night, for more than two thousand nights, Frank and Tony would converse-about music, family, friends, great loves, achievements and successes, failures and disappointments, the lives they'd led, the lives they wished they'd led. In these full-disclosure conversations, Sinatra spoke of his close yet complex relationship with his father, his conflicts with record companies, his carousing in Vegas, his love affairs with some of the most beautiful women of his era, his triumphs on some of the world's biggest stages, his complicated relationships with his talented children, and, most important, his dedication to his craft. Toward the end, no one was closer to the singer than Oppedisano, who kept his own rooms at the Sinatra residences for many years, often brokered difficult conversations between family members, and held the superstar entertainer's hand when he drew his last breath. Featuring never-before-seen photos and offering startlingly fresh anecdotes and new revelations that center on some of the most famous people of the past fifty years-including Jackie Kennedy, Marilyn Monroe, Sam Giancana, Madonna, and Bono-Sinatra and Me pulls back the curtain to reveal a man whom history has, in many ways, gotten wrong"--
Not many people were allowed inside Frank Sinatra's inner circle. But Tony Consiglio was a boyhood friend of Sinatra's who remained his friend and confidant for over sixty years. One reason Sinatra valued Tony’s friendship is that he could be trusted: Sinatra nicknamed him "the Clam" because Tony never spoke to reporters or biographers about the singer. From the early days when Sinatra was trying to establish himself as a singer to the mid-1960s, Tony worked with Sinatra and was there to share in the highs and lows of Sinatra's life and career. Tony was with Sinatra during his "bobby-soxer" megastar days in the 1940s, and he remained loyal to Sinatra during the lean years of the early 1950s, when "the Voice" was struggling with a crumbling singing and acting career—as well as his tumultuous marriage to Ava Gardner. Tony also had a front row seat to Sinatra's comeback in the 1950s, starting with his Academy Award–winning role in From Here to Eternity and a string of now-classic hit recordings. Tony's friendship with Sinatra allowed him to rub elbows with the Hollywood elite, including Dean Martin, Jerry Lewis, Sammy Davis, Jr., Peter Lawford, Kim Novak, Ava Gardner, and many others. It also brought him close to the political world of the early 1960s, when Sinatra campaigned for John F. Kennedy and then helped plan the Kennedy inauguration. Tony was even at the Kennedy compound in Hyannis, Massachusetts, when the election results came in. Sinatra and Me will shed new light on the real Frank Sinatra—from the man who knew him better than anyone.
The industrial age of energy and transportation will be over by 2030. Maybe before. Exponentially improving technologies such as solar, electric vehicles, and autonomous (self-driving) cars will disrupt and sweep away the energy and transportation industries as we know it. The same Silicon Valley ecosystem that created bit-based technologies that have disrupted atom-based industries is now creating bit- and electron-based technologies that will disrupt atom-based energy industries. Clean Disruption projections (based on technology cost curves, business model innovation as well as product innovation) show that by 2030: - All new energy will be provided by solar or wind. - All new mass-market vehicles will be electric. - All of these vehicles will be autonomous (self-driving) or semi-autonomous. - The new car market will shrink by 80%. - Even assuming that EVs don't kill the gasoline car by 2030, the self-driving car will shrink the new car market by 80%. - Gasoline will be obsolete. Nuclear is already obsolete. - Up to 80% of highways will be redundant. - Up to 80% of parking spaces will be redundant. - The concept of individual car ownership will be obsolete. - The Car Insurance industry will be disrupted. The Stone Age did not end because we ran out of rocks. It ended because a disruptive technology ushered in the Bronze Age. The era of centralized, command-and-control, extraction-resource-based energy sources (oil, gas, coal and nuclear) will not end because we run out of petroleum, natural gas, coal, or uranium. It will end because these energy sources, the business models they employ, and the products that sustain them will be disrupted by superior technologies, product architectures, and business models. This is a technology-based disruption reminiscent of how the cell phone, Internet, and personal computer swept away industries such as landline telephony, publishing, and mainframe computers. Just like those technology disruptions flipped the architecture of information and brought abundant, cheap and participatory information, the clean disruption will flip the architecture of energy and bring abundant, cheap and participatory energy. Just like those previous technology disruptions, the Clean Disruption is inevitable and it will be swift.
Frank Sinatra was an entertainer of mesmerizing talent, charisma and style. As well as being one of the bestselling musical artists of all time, he was an Academy Award-winning actor who starred in over sixty movies, and a cultural icon of seismic influence. Created in close collaboration with the Sinatra family and Frank Sinatra Enterprises, this momentous book captures the man in public and private, with exclusive unseen photographs and memorabilia from the family archives, as well as the most iconic images, outtakes and contact sheets from celebrated photo shoots. In candid accounts from his closest friends and associates, a portrait emerges of a man who was intensely loyal to his friends, enthusiastically devoted to charity and who demonstrated exceptional stamina and resilience in a career that spanned an incredible six decades. The result is a captivating tribute to one of the best-loved performers the world has ever known, in a large-format presentation every bit as swanky and swoonsome as the man himself.
Broadband is one of the most transformative technologies of the 21st century, yet our understanding of its regional impacts remains somewhat rudimentary. Not only are issues of broadband pricing and speed relevant in this context, but the overall quality of service for broadband can often dictate its impacts on regional development. This book illuminates the regional impacts of this pervasive and important technology. The principle aim of this book is to deepen our understanding of broadband and its connections to regional development. First, it uses a geospatial lens to explore how the relationship between broadband and regional development influences access to technology platforms, dictates provision patterns, and facilitates the shrinkage of space and time in non-uniform and sometimes unexpected ways. Second, it book provides a comprehensive guide that details the strengths and weaknesses of publically available broadband data and their associated uncertainties, allowing regional development professionals and researchers to make more informed decisions regarding data use, analytical models and policy recommendations. Finally, this book is the first to detail the growing importance of broadband to digital innovation and entrepreneurship in regions. This book will be of interest to regional development professionals and researchers in economics, public policy, geography, regional science and planning.
God is good and powerful and wants the best for your life. He has big plans for you. You believe these things are true. But what is your own responsibility as a man when it comes to becoming all God created you to be? How can you walk in victory and faith and make an impact on others for God? Kingdom Men Rising challenges men to foster personal discipleship and apply discipleship skills and a leadership mindset to all areas of life. Dr. Tony Evans brings his insights, stories, and wise counsel from God's Word to clear all obstacles in your path, leading you to the abundant life you've been called to live. And along the way, you'll find your heart stirred to reach for more, no longer settling for a faith that just goes through the motions. The life of King David is used as the book's foundation, and topics include overcoming temptation, restoration from sin, how to disciple others, and finally how to leave a legacy of faith and godly influence. Replace helplessness, boredom, and regret with vibrancy, power, and joy. Let Kingdom Men Rising help you take the next step in your faith to become the powerful man of God you were made to be.
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.