The authorship of the Pastoral letters has been a matter of intense scholarly debate for almost two hundred years. The letters clearly purport to be written by Paul, but perceived differences in the literary style, vocabulary and theology of the Pastorals when compared with that of the genuine Pauline letters suggests that this was not so. The arguments have centred primarily on the question of whether Paul or a disciple of Paul - a gifted pseudonymist - composed these letters. It is the 'either/or' nature of the debate that is brought into serious question in this book. Dr Miller argues that the Pastorals reflect a compositional history that was commonplace throughout the ancient Near East. He takes the reader on a wide-ranging tour of biblical and extra-biblical sources, examining their literary histories, and arguing that the Pastorals are composite documents, not unlike many Jewish and early Christian works.

The authorship of the Pastoral letters has been a matter of intense scholarly debate for almost two hundred years. The letters clearly purport to be written by Paul, but perceived differences in the literary style, vocabulary and theology of the Pastorals when compared with that of the genuine Pauline letters suggests that this was not so. The arguments have centred primarily on the question of whether Paul or a disciple of Paul - a gifted pseudonymist - composed these letters. It is the 'either/or' nature of the debate that is brought into serious question in this book. Dr Miller argues that the Pastorals reflect a compositional history that was commonplace throughout the ancient Near East. He takes the reader on a wide-ranging tour of biblical and extra-biblical sources, examining their literary histories, and arguing that the Pastorals are composite documents, not unlike many Jewish and early Christian works.

Master the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential concepts About This Book * Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding * Leveraging the flexibility and modularity of R to experiment with a range of different techniques and data types * Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easily Who This Book Is For Although budding data scientists, predictive modelers, or quantitative analysts with only basic exposure to R and statistics will find this book to be useful, the experienced data scientist professional wishing to attain master level status , will also find this book extremely valuable.. This book assumes familiarity with the fundamentals of R, such as the main data types, simple functions, and how to move data around. Although no prior experience with machine learning or predictive modeling is required, there are some advanced topics provided that will require more than novice exposure. What You Will Learn * Master the steps involved in the predictive modeling process * Grow your expertise in using R and its diverse range of packages * Learn how to classify predictive models and distinguish which models are suitable for a particular problem * Understand steps for tidying data and improving the performing metrics * Recognize the assumptions, strengths, and weaknesses of a predictive model * Understand how and why each predictive model works in R * Select appropriate metrics to assess the performance of different types of predictive model * Explore word embedding and recurrent neural networks in R * Train models in R that can work on very large datasets In Detail R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets. How do you train models that can handle really large datasets? This book will also show you just that. Finally, you will tackle the really important topic of deep learning by implementing applications on word embedding and recurrent neural networks. By the end of this book, you will have explored and tested the most popular modeling techniques in use on real- world datasets and mastered a diverse range of techniques in predictive analytics using R. Style and approach This book takes a step-by-step approach in explaining the intermediate to advanced concepts in predictive analytics. Every concept is explained in depth, supplemented with practical examples applicable in a real-world setting.

Get your statistics basics right before diving into the world of data science About This Book * No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; * Implement statistics in data science tasks such as data cleaning, mining, and analysis * Learn all about probability, statistics, numerical computations, and more with the help of R programs Who This Book Is For This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful. What You Will Learn * Analyze the transition from a data developer to a data scientist mindset * Get acquainted with the R programs and the logic used for statistical computations * Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more * Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis * Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks * Get comfortable with performing various statistical computations for data science programmatically In Detail Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically. Style and approach Step by step comprehensive guide with real world examples

Learn effective tools and techniques to separate big data into manageable and logical components for efficient data visualization About This Book * This unique guide teaches you how to visualize your cluttered, huge amounts of big data with ease * It is rich with ample options and solid use cases for big data visualization, and is a must-have book for your shelf * Improve your decision-making by visualizing your big data the right way Who This Book Is For This book is for data analysts or those with a basic knowledge of big data analysis who want to learn big data visualization in order to make their analysis more useful. You need sufficient knowledge of big data platform tools such as Hadoop and also some experience with programming languages such as R. This book will be great for those who are familiar with conventional data visualizations and now want to widen their horizon by exploring big data visualizations. What You Will Learn * Understand how basic analytics is affected by big data * Deep dive into effective and efficient ways of visualizing big data * Get to know various approaches (using various technologies) to address the challenges of visualizing big data * Comprehend the concepts and models used to visualize big data * Know how to visualize big data in real time and for different use cases * Understand how to integrate popular dashboard visualization tools such as Splunk and Tableau * Get to know the value and process of integrating visual big data with BI tools such as Tableau * Make sense of the visualization options for big data, based upon the best suited visualization techniques for big data In Detail When it comes to big data, regular data visualization tools with basic features become insufficient. This book covers the concepts and models used to visualize big data, with a focus on efficient visualizations. This book works around big data visualizations and the challenges around visualizing big data and address characteristic challenges of visualizing like speed in accessing, understanding/adding context to, improving the quality of the data, displaying results, outliers, and so on. We focus on the most popular libraries to execute the tasks of big data visualization and explore "big data oriented" tools such as Hadoop and Tableau. We will show you how data changes with different variables and for different use cases with step-through topics such as: importing data to something like Hadoop, basic analytics. The choice of visualizations depends on the most suited techniques for big data, and we will show you the various options for big data visualizations based upon industry-proven techniques. You will then learn how to integrate popular visualization tools with graphing databases to see how huge amounts of certain data. Finally, you will find out how to display the integration of visual big data with BI using Cognos BI. Style and approach With the help of insightful real-world use cases, we'll tackle data in the world of big data. The scalability and hugeness of the data makes big data visualizations different from normal data visualizations, and this book addresses all the difficulties encountered by professionals while visualizing their big data.

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This book is packed with real world examples. Each major certification topic is covered in a separate chapter, which helps to make understanding of concepts easier. At the end of each chapter, you will find a variety of practice questions to strengthen and test your learning. If you are a beginner to intermediate level Cognos TM1 developer looking to add an important IBM certification to your resume but don't know where to start, this book is for you!