BRIDGES THE GAP BETWEEN SAS AND R, ALLOWING USERS TRAINED IN ONE LANGUAGE TO EASILY LEARN THE OTHER SAS and R are widely-used, very different software environments. Prized for its statistical and graphical tools, R is an open-source programming language that is popular with statisticians and data miners who develop statistical software and analyze data. SAS (Statistical Analysis System) is the leading corporate software in analytics thanks to its faster data handling and smaller learning curve. SAS for R Users enables entry-level data scientists to take advantage of the best aspects of both tools by providing a cross-functional framework for users who already know R but may need to work with SAS. Those with knowledge of both R and SAS are of far greater value to employers, particularly in corporate settings. Using a clear, step-by-step approach, this book presents an analytics workflow that mirrors that of the everyday data scientist. This up-to-date guide is compatible with the latest R packages as well as SAS University Edition. Useful for anyone seeking employment in data science, this book: Instructs both practitioners and students fluent in one language seeking to learn the other Provides command-by-command translations of R to SAS and SAS to R Offers examples and applications in both R and SAS Presents step-by-step guidance on workflows, color illustrations, sample code, chapter quizzes, and more Includes sections on advanced methods and applications Designed for professionals, researchers, and students, SAS for R Users is a valuable resource for those with some knowledge of coding and basic statistics who wish to enter the realm of data science and business analytics. AJAY OHRI is the founder of analytics startup Decisionstats.com. His research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces to cloud computing, investigating climate change, and knowledge flows. He currently advises startups in analytics off shoring, analytics services, and analytics. He is the author of Python for R Users: A Data Science Approach (Wiley), R for Business Analytics, and R for Cloud Computing.

Having appropriate storage for hosting business-critical data and advanced Security Information and Event Management software for deep inspection, detection, and prioritization of threats has become a necessity of any business. This IBM® Redpaper publication explains how the storage features of IBM Spectrum® Scale, combined with the log analysis, deep inspection, and detection of threats provided by IBM QRadar®, helps reduce the impact of incidents on business data. Such integration provides an excellent platform for hosting unstructured business data that is subject to regulatory compliance requirements. This paper describes how IBM Spectrum Scale file audit logging can be integrated with IBM QRadar. Using QRadar, an administrator can monitor, inspect, detect, and derive insights for identifying potential threats to the data stored on IBM Spectrum Scale. When the threats are identified, you can quickly act on them to mitigate or reduce the impact of incidents. This paper is intended for chief technology officers, solution engineers, security architects, and systems administrators. NOTE: This paper assumes a basic understanding of IBM Spectrum Scale, IBM QRadar, and their administration.

Learn how to fuse today's data science tools and techniques with your SAP enterprise resource planning (ERP) system. With this practical guide, SAP veterans Greg Foss and Paul Modderman demonstrate how to use several data analysis tools to solve interesting problems with your SAP data. Data engineers and scientists will explore ways to add SAP data to their analysis processes, while SAP business analysts will learn practical methods for answering questions about the business. By focusing on grounded explanations of both SAP processes and data science tools, this book gives data scientists and business analysts powerful methods for discovering deep data truths. You'll explore: Examples of how data analysis can help you solve several SAP challenges Natural language processing for unlocking the secrets in text Data science techniques for data clustering and segmentation Methods for detecting anomalies in your SAP data Data visualization techniques for making your data come to life

This document is intended to facilitate the deployment of the Cyber Resiliency solution for IBM® Spectrum Scale. This solution is designed to protect the data on IBM Spectrum™ Scale from external cyberattacks or insider attacks using its integration with IBM Spectrum Protect™ and IBM Tape Storage. To complete the tasks that it describes, you must understand IBM Spectrum Scale™, IBM Spectrum Protect, and IBM Tape Storage architecture, concepts, and configuration. The information in this document is distributed on an as-is basis without any warranty that is either expressed or implied. Support assistance for the use of this material is limited to situations where IBM Spectrum Scale or IBM Spectrum Protect are supported and entitled, and where the issues are specific to a blueprint implementation.

This IBM® Redbooks® publication helps strengthen the position of the IBM PowerHA® SystemMirror® for Linux solution with well-defined and documented deployment models within an IBM Power Systems™ environment, which provides customers a planned foundation for business resilience and disaster recovery (DR) for their IBM Power Systems infrastructure solutions. This book addresses topics to help answer customers' complex high availability (HA) and DR requirements for IBM AIX® and Linux on IBM Power Systems servers to help maximize system availability and resources and provide technical documentation to transfer the how-to-skills to users and support teams. This publication is targeted at technical professionals (consultants, technical support staff, IT architects, and IT specialists) who are responsible for providing HA and DR solutions and support for IBM PowerHA SystemMirror for AIX and Linux Standard and Enterprise Editions on IBM Power Systems servers.

Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics. This book is aimed at both researchers and practitioners who are interested in model-based development and the analytics of large-scale models, ranging from big data management and analytics, to enterprise domains. The book could also be used in graduate courses on model development, data analytics and data management. Identifies key problems and offers solution approaches and tools that have been developed or are necessary for model management and analytics Explores basic theory and background, current research topics, related challenges and the research directions for model management and analytics Provides a complete overview of model management and analytics frameworks, the different types of analytics (descriptive, diagnostics, predictive and prescriptive), the required modelling and method steps, and important future directions

Gain a better understanding of implementing SAP S/4HANA-based digital transformations. This book helps you understand the various components involved in the planning and execution of successful SAP S/4HANA projects. Learn how to ensure success by building a solid business case for SAP S/4HANA up front and track business value generated throughout the implementation. Implementing SAP S/4HANA provides a framework for planning and executing SAP S/4HANA projects by articulating the implementation approach used by different components in SAP S/4HANA implementations. Whether you are mid-way through the SAP S/4HANA program or about to embark on it, this book will help you throughout the journey. If you are looking for answers on why SAP S/4HANA requires special considerations as compared to a traditional SAP implementation, this book is for you. What You Will Learn Understand various components of your SAP S/4HANA project Forecast and track your success throughout the SAP S/4HANA implementation Build a solid business case for your SAP S/4HANA program Discover how the implementation approach varies across these components Who This Book Is For SAP S/4HANA clients (line managers and consultants).

Master the art of business intelligence with "Learn Power BI". This beginner-friendly guide introduces you to building interactive business reports and dashboards using Microsoft Power BI. By covering data modeling, visualization, and analysis techniques, you will be equipped with the tools to derive actionable insights and decision-making support from your organization's data. What this Book will help me do Create interactive dashboards using Power BI features to enhance business reporting. Import and transform raw data using Power BI's Query Editor for cleaner analysis. Perform dynamic and complex calculations with DAX to enrich your data insights. Develop storytelling reports that convey impactful business insights effectively. Publish and securely share Power BI reports, enabling collaboration across teams. Author(s) Greg Deckler is an expert in business intelligence and data analysis, with years of experience leveraging Power BI to solve real-world challenges. As a seasoned author, Greg combines technical depth with an approachable teaching style, making intricate concepts accessible. Through his writing, he focuses on empowering readers to unlock the full potential of Power BI for solving business challenges. Who is it for? This book is ideal for IT managers, data analysts, or professionals looking to adopt Power BI for their business intelligence needs. It's accessible to beginners with no prior Power BI experience. Additionally, if you're transitioning from other BI tools and aim to learn how to build interactive dashboards and reports in Power BI, this is the guide for you. Advance your skills and meet your business goals with confidence.

Business Intelligence (BI) has emerged as a field which seeks to support managers in decision-making. It encompasses the techniques, methods and tools for conducting analytically-based IT solutions, which are referred to as OLAP (OnLine Analytical Processing). Within this field, SQL has a role as a leader and is continuously evolving to cover both transactional and analytical data management. This book discusses the functions provided by Microsoft® SQL Server 2014/2016 in terms of business intelligence. The analytic functions are considered as an enrichment of the SQL language. They combine a series of practical functions to answer complex analysis requests with all the simplicity, elegance and acquired performance of the SQL language. Drawing on the wide experience of the author in teaching and research, as well as insights from contacts in the industry, this book focuses on the issues and difficulties faced by academics (students and teachers) and professionals engaged in data analysis with the SQL Server 2014/2016 database management system.

This IBM® Redpaper publication provides an update to the original description of IBM Reference Architecture for Genomics. This paper expands the reference architecture to cover all of the major vertical areas of healthcare and life sciences industries, such as genomics, imaging, and clinical and translational research. The architecture was renamed IBM Reference Architecture for High Performance Data and AI in Healthcare and Life Sciences to reflect the fact that it incorporates key building blocks for high-performance computing (HPC) and software-defined storage, and that it supports an expanding infrastructure of leading industry partners, platforms, and frameworks. The reference architecture defines a highly flexible, scalable, and cost-effective platform for accessing, managing, storing, sharing, integrating, and analyzing big data, which can be deployed on-premises, in the cloud, or as a hybrid of the two. IT organizations can use the reference architecture as a high-level guide for overcoming data management challenges and processing bottlenecks that are frequently encountered in personalized healthcare initiatives, and in compute-intensive and data-intensive biomedical workloads. This reference architecture also provides a framework and context for modern healthcare and life sciences institutions to adopt cutting-edge technologies, such as cognitive life sciences solutions, machine learning and deep learning, Spark for analytics, and cloud computing. To illustrate these points, this paper includes case studies describing how clients and IBM Business Partners alike used the reference architecture in the deployments of demanding infrastructures for precision medicine. This publication targets technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for providing life sciences solutions and support.

Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code. As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices. This book is a good starting point for people who want to gain practical skills to perform data science. All the code willbe available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science. Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors. What You'll Learn Play the role of a data scientist when completing increasingly challenging exercises using Python 3 Work work with proven data science techniques/technologies Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big Data Apply theory of probability, statistical inference, and algebra to understand the data sciencepractices Who This Book Is For Anyone who would like to embark into the realm of data science using Python 3.

This IBM® Redbooks® publication provides a security and compliance solution that is optimized for virtualized environments on IBM Power Systems™ servers, running IBM PowerVM® and IBM AIX®. Security control and compliance are some of the key components that are needed to defend the virtualized data center and cloud infrastructure against ever evolving new threats. The IBM business-driven approach to enterprise security that is used with solutions, such as IBM PowerSC™, makes IBM the premier security vendor in the market today. The book explores, tests, and documents scenarios using IBM PowerSC that leverage IBM Power Systems servers architecture and software solutions from IBM to help defend the virtualized data center and cloud infrastructure against ever evolving new threats. This publication helps IT and Security managers, architects, and consultants to strengthen their security and compliance posture in a virtualized environment running IBM PowerVM.

Leverage machine and deep learning models to build applications on real-time data using PySpark. This book is perfect for those who want to learn to use this language to perform exploratory data analysis and solve an array of business challenges. You'll start by reviewing PySpark fundamentals, such as Spark’s core architecture, and see how to use PySpark for big data processing like data ingestion, cleaning, and transformations techniques. This is followed by building workflows for analyzing streaming data using PySpark and a comparison of various streaming platforms. You'll then see how to schedule different spark jobs using Airflow with PySpark and book examine tuning machine and deep learning models for real-time predictions. This book concludes with a discussion on graph frames and performing network analysis using graph algorithms in PySpark. All the code presented in the book will be available in Python scripts on Github. What You'll Learn Develop pipelines for streaming data processing using PySpark Build Machine Learning & Deep Learning models using PySpark latest offerings Use graph analytics using PySpark Create Sequence Embeddings from Text data Who This Book is For Data Scientists, machine learning and deep learning engineers who want to learn and use PySpark for real time analysis on streaming data.

Use MySQL Shell, the first modern and advanced client for connecting to and interacting with MySQL. It supports SQL, Python, and JavaScript. That’s right! You can write Python scripts and execute them within the shell interactively, or in batch mode. The level of automation available from Python combined with batch mode is especially helpful to those practicing DevOps methods in their database environments. Introducing MySQL Shell covers everything you need to know about MySQL Shell. You will learn how to use the shell for SQL, as well as the new application programming interfaces for working with a document store and even automating your management of MySQL servers using Python. The book includes a look at the supporting technologies and concepts such as JSON, schema-less documents, NoSQL, MySQL Replication, Group Replication, InnoDB Cluster, and more. MySQL Shell is the client that developers and databaseadministrators have been waiting for. Far more powerful than the legacy client, MySQL Shell enables levels of automation that are useful not only for MySQL, but in the broader context of your career as well. Automate your work and build skills in one of the most in-demand languages. With MySQL Shell, you can do both! What You'll Learn Use MySQL Shell with the newest features in MySQL 8 Discover what a Document Store is and how to manage it with MySQL Shell Configure Group Replication and InnoDB Cluster from MySQL Shell Understand the new MySQL Python application programming interfaces Write Python scripts for managing your data and the MySQL high availability features Who This Book Is For Developers and database professionals who want to automate their work and remain on the cutting edge of what MySQLhas to offer. Anyone not happy with the limited automation capabilities of the legacy command-line client will find much to like in this book on the MySQL Shell that supports powerful automation through the Python scripting language.

A revised edition that explores random numbers, probability, and statistical inference at an introductory mathematical level Written in an engaging and entertaining manner, the revised and updated second edition of Probably Not continues to offer an informative guide to probability and prediction. The expanded second edition contains problem and solution sets. In addition, the book’s illustrative examples reveal how we are living in a statistical world, what we can expect, what we really know based upon the information at hand and explains when we only think we know something. The author introduces the principles of probability and explains probability distribution functions. The book covers combined and conditional probabilities and contains a new section on Bayes Theorem and Bayesian Statistics, which features some simple examples including the Presecutor’s Paradox, and Bayesian vs. Frequentist thinking about statistics. New to this edition is a chapter on Benford’s Law that explores measuring the compliance and financial fraud detection using Benford’s Law. This book: Contains relevant mathematics and examples that demonstrate how to use the concepts presented Features a new chapter on Benford’s Law that explains why we find Benford’s law upheld in so many, but not all, natural situations Presents updated Life insurance tables Contains updates on the Gantt Chart example that further develops the discussion of random events Offers a companion site featuring solutions to the problem sets within the book Written for mathematics and statistics students and professionals, the updated edition of Probably Not: Future Prediction Using Probability and Statistical Inference, Second Edition combines the mathematics of probability with real-world examples. LAWRENCE N. DWORSKY, PhD, is a retired Vice President of the Technical Staff and Director of Motorola’s Components Research Laboratory in Schaumburg, Illinois, USA. He is the author of Introduction to Numerical Electrostatics Using MATLAB from Wiley.

A collection of popular essays from security guru Bruce Schneier In his latest collection of essays, security expert Bruce Schneier tackles a range of cybersecurity, privacy, and real-world security issues ripped from the headlines. Essays cover the ever-expanding role of technology in national security, war, transportation, the Internet of Things, elections, and more. Throughout, he challenges the status quo with a call for leaders, voters, and consumers to make better security and privacy decisions and investments. Bruce’s writing has previously appeared in some of the world's best-known and most-respected publications, including The Atlantic, the Wall Street Journal, CNN, the New York Times, the Washington Post, Wired, and many others. And now you can enjoy his essays in one place—at your own speed and convenience. • Timely security and privacy topics • The impact of security and privacy on our world • Perfect for fans of Bruce’s blog and newsletter • Lower price than his previous essay collections The essays are written for anyone who cares about the future and implications of security and privacy for society.

Time has become a precious commodity, so business leaders who can save their customers' time more effectively than competitors do will win their loyalty. This book shows how it's done. Business survival requires valuing what customers value—and in our overworked and distraction-rich era, customers value their time above all else. Real-time companies beat their rivals by being faster and more responsive in meeting customer needs. To become a real-time company, as top scholars Jerry Power and Tom Ferratt explain, you need a real-time monitoring and response system. They offer detailed advice on how to put procedures in place that will collect data on how well products or services are saving customer time; identify strengths, weaknesses, threats, and opportunities; and specify innovations needed to save even more customer time. Where should leaders look to innovate? Powers and Ferratt say to search every step in the life of a product or service, from development to production to usage. And for each step, they identify four possible levers for innovation: the design of the products or services themselves, the process used to produce them, the data that can be gathered on their use, and the people who make or provide the product or service. The book features dozens of examples of companies that are getting it right and the innovations they used to help their customers save time, all while helping themselves to a hefty slice of market share. This is a comprehensive, authoritative guide to thriving in a revolution that is sweeping every industry and sector.

Real-Time Data Analytics for Large-Scale Sensor Data covers the theory and applications of hardware platforms and architectures, the development of software methods, techniques and tools, applications, governance and adoption strategies for the use of massive sensor data in real-time data analytics. It presents the leading-edge research in the field and identifies future challenges in this fledging research area. The book captures the essence of real-time IoT based solutions that require a multidisciplinary approach for catering to on-the-fly processing, including methods for high performance stream processing, adaptively streaming adjustment, uncertainty handling, latency handling, and more. Examines IoT applications, the design of real-time intelligent systems, and how to manage the rapid growth of the large volume of sensor data Discusses intelligent management systems for applications such as healthcare, robotics and environment modeling Provides a focused approach towards the design and implementation of real-time intelligent systems for the management of sensor data in large-scale environments

Learn Python by Building Data Science Applications takes a hands-on approach to teaching Python programming by guiding you through building engaging real-world data science projects. This book introduces Python's rich ecosystem and equips you with the skills to analyze data, train models, and deploy them as efficient applications. What this Book will help me do Get proficient in Python programming by learning core topics like data structures, loops, and functions. Explore data science libraries such as NumPy, Pandas, and scikit-learn to analyze and process data. Learn to create visualizations with Matplotlib and Altair, simplifying data communication. Build and deploy machine learning models using Python and share them as web services. Understand development practices such as testing, packaging, and continuous integration for professional workflows. Author(s) None Kats and None Katz are seasoned Python developers with years of experience in teaching programming and deploying data science applications. Their expertise spans providing learners with practical knowledge and versatile skills. They combine clear explanations with engaging projects to ensure a rewarding learning experience. Who is it for? This book is ideal for individuals new to programming or data science who want to learn Python through practical projects. Researchers, analysts, and ambitious students with minimal coding background but a keen interest in data analysis and application development will find this book beneficial. It's a perfect choice for anyone eager to explore and leverage Python for real-world solutions.

Private Security and the Investigative Process, Fourth Edition targets those students in the early phases of their study of private sector justice and the principles of investigative practice most relevant to the private security industry. The book lays out not only the basic steps taken by entry level as well advanced security professionals conducting investigations, but also provides an overview of the professional security industry and landscape as a whole.