IBM® Netcool® Operations Insight empowers your IT operations to use real-time and historical analytics to identify, isolate, and resolve problems before they affect your business. Powered by IBM Tivoli® Netcool/OMNIbus and the transformative capabilities of cognitive analytics, Netcool Operations Insight consolidates millions of alerts from across local, cloud, and hybrid environments into a few actionable problems. This IBM Redbooks® publication gives a broad understanding of Netcool Operations Insight and describes several scenarios that show the capabilities of this solution in a real-life environment. Each scenario features a different capability of Netcool Operations Insight. The scenarios are documented by using step-by-step figures with explanations to make them easier to implement in your own environment. The scenarios in this book are broken into the following categories: - Network Management-related scenarios - Network Event and cognitive-related scenarios - Network Event-related scenarios The target audience of this book is network specialists, network administrators, and network operators.

Data-driven techniques have improved decision-making processes for people in industries such as finance and real estate. Yet, despite promising solutions that data analytics and artificial intelligence/machine learning (ML) tools can bring to healthcare, the industry remains largely unconvinced. In this O’Reilly report, you’ll explore the potential of—and impediments to—widespread adoption of AI and ML in the medical field. You’ll also learn how extensive government regulation and resistance from the medical community have so far stymied full-scale acceptance of sophisticated data analytics in healthcare. Through interviews with several professionals working at the intersection of medicine and data science, author Mike Barlow examines five areas where the application of AI/ML strategies can spur a beneficial revolution in healthcare: Identifying risks and interventions for healthcare management of entire populations Closing gaps in care by designing plans for individual patients Supporting customized self-care treatment plans and monitoring patient health in real time Optimizing healthcare processes through data analysis to improve care and reduce costs Helping doctors and patients choose proper medications, dosages, and promising surgical options

To satisfy end users who want easily accessible answers, many software vendors are looking to add analytics and reporting capabilities to their applications. Embedding analytics into applications can lead to wider adoption and product use, improved user experience, and differentiated products, but embedding analytics can also come with challenges and complexities. In this report, author Courtney Webster reviews several approaches and methods for embedding analytics capabilities into your applications. Should you implement a separate reporting portal, an in-application reporting tab, or go all in with a fully embedded in-page analytics solution? And do you build your own or buy a solution out of the box? To help you choose the right embedded analytics tool, Webster examines seven challenges—from customization, usability, and capabilities to scalability, performance, and data structure support—and presents best practice solutions for each.

Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community’s leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid. Presents the wisdom of community experts, derived from a summit on software analytics Provides contributed chapters that share discrete ideas and technique from the trenches Covers top areas of concern, including mining security and social data, data visualization, and cloud-based data Presented in clear chapters designed to be applicable across many domains

In this episode, we dive deep on a 1988 classic: Tom Hanks, under the direction of Penny Marshall, was a 12-year-old in a 30-year-old's body... Actually, that's a different "Big" from what we actually cover in this episode. In this instant classic, the star is BigQuery, the director is Google, and Michael Healy, a data scientist from Search Discovery, delivers an Oscar-worthy performance as Zoltar. In under 48 minutes, Michael (Helbling) and Tim drastically increased their understanding of what Google BigQuery is and where it fits in the analytics landscape. If you'd like to do the same, give it a listen! Technologies, books, and sites referenced in this episode were many, including: Google BigQuery and the BigQuery API Libraries, Google Cloud Services, Google Dremel, Apache Drill, Amazon Redshift (AWS), Rambo III (another 1988 movie!), Hadoop, Cloudera, the Observepoint Tag Debugger, Our Mathematical Universe by Max Tegmark, A Brief History of Time by Stephen Hawking, and a video of math savant Scott Flansburg.

With Microsoft SQL Server 2016, a variety of new features and enhancements to the data platform deliver breakthrough performance, advanced security, and richer, integrated reporting and analytics capabilities. In this ebook, we introduce new security features: Always Encrypted, Row-Level Security, and dynamic data masking; discuss enhancements that enable you to better manage performance and storage: TemDB configuration, query store, and Stretch Database; review several improvements to Reporting Services; and also describe AlwaysOn Availability Groups, tabular enhancements, and R integration.

Kristian Lum (@KLdivergence) joins me this week to discuss her work at @hrdag on predictive policing. We also discuss Multiple Systems Estimation, a technique for inferring statistical information about a population from separate sources of observation. If you enjoy this discussion, check out the panel Tyranny of the Algorithm? Predictive Analytics & Human Rights which was mentioned in the episode.

As Dr. Phil says, "Never put more into a relationship than you can afford to lose." Not sure what that has to do with Excel but it sounds vaguely wise, which is the whole point. Tim and Michael try to be your relationship coach for Microsoft Excel. Despised by data scientists, but used by everyone else, where are the boundaries and who has what it takes to enforce them. Join us in an exploration of our digital analytics love/hate affair with that most ubiquitous of analytics tools. (Cell) references made in this episode include: Chandoo.org, Juice Analytics, ggplot2, Bullet Charts in Excel, Geeks and Greeks by Steve Altes, Google Firebase.

Relevant Search demystifies relevance work. Using Elasticsearch, it teaches you how to return engaging search results to your users, helping you understand and leverage the internals of Lucene-based search engines. About the Technology Users are accustomed to and expect instant, relevant search results. To achieve this, you must master the search engine. Yet for many developers, relevance ranking is mysterious or confusing. About the Book Relevant Search demystifies the subject and shows you that a search engine is a programmable relevance framework. You'll learn how to apply Elasticsearch or Solr to your business's unique ranking problems. The book demonstrates how to program relevance and how to incorporate secondary data sources, taxonomies, text analytics, and personalization. In practice, a relevance framework requires softer skills as well, such as collaborating with stakeholders to discover the right relevance requirements for your business. By the end, you'll be able to achieve a virtuous cycle of provable, measurable relevance improvements over a search product's lifetime. What's Inside Techniques for debugging relevance Applying search engine features to real problems Using the user interface to guide searchers A systematic approach to relevance A business culture focused on improving search About the Reader For developers trying to build smarter search with Elasticsearch or Solr. About the Authors Doug Turnbull is lead relevance consultant at OpenSource Connections, where he frequently speaks and blogs. John Berryman is a data engineer at Eventbrite, where he specializes in recommendations and search. Quotes One of the best and most engaging technical books I’ve ever read. - From the Foreword by Trey Grainger, Author of "Solr in Action" Will help you solve real-world search relevance problems for Lucene-based search engines. - Dimitrios Kouzis-Loukas, Bloomberg L.P. An inspiring book revealing the essence and mechanics of relevant search. - Ursin Stauss, Swiss Post Arms you with invaluable knowledge to temper the relevancy of search results and harness the powerful features provided by modern search engines. - Russ Cam, Elastic

The book is divided into three parts – (1) prerequisite to regression analysis followed by a discussion on simple regression, (2) multiple regression analysis with applications, and (3) regression and modeling including the second order models, nonlinear regression, and interaction models in regressions. All these sections provide examples with complete computer analysis and instructions commonly used in modeling and analyzing these problems. The book deals with detailed analysis and interpretation of computer results. This will help readers to appreciate the power of computer in applying regression models. The readers will find that the understanding of computer results is critical to implementing regression and modeling in real world situation. The book is written for juniors, seniors and graduate students in business, MBAs, professional MBAs, and working people in business and industry. Managers, practitioners, professionals, quality professionals, quality engineers, and anyone involved in data analysis, business analytics, and quality and six sigma will find the book to be a valuable resource.

One area where data analytics can have profound effect is your company’s procurement process. Some organizations spend more than two thirds of their revenue buying goods and services, making procurement—out of all business activities—a key element in achieving cost reduction. This report examines how your company can significantly improve procurement analytics to solve business questions quickly and effectively. Author Federico Castanedo, Chief Data Scientist at WiseAthena.com, explains how a probabilistic, bottom-up approach can significantly increase the quality, speed, and scalability of your data preparation operations—whether you’re integrating datasets or cleaning and classifying them. You’ll learn how new solutions leverage automation and machine learning, including the Tamr platform, and help you take advantage of several data-driven actions for procurement—including compliance, price arbitrage, and spend recovery.

Consider this scenario: You walk into a building and a sensor identifies you through your mobile phone. You then receive a welcoming text telling you when lunch will be served, or perhaps a health warning based on allergy information you’ve stored in your profile. Maybe you’ll be flagged as a security threat. How is that possible? This O’Reilly report explores ambient computing—hands-free, 24/7 wireless connectivity to hardware, data, and IT systems. Enabling that scenario requires a lot of work behind the scenes to determine network connectivity, device security, and personal privacy. With an ambient-computing technology stack already in the works, resolving those issues is only a matter of time. Through interviews with front-line tech pioneers—including Ari Gesher (Kairos Aerospace) and Matthew Gast (Aerohive Networks)—author Mike Barlow explores how real-time analytics can enable real-time decision making. How will simple beacons broadcast information to your phone as you pass businesses on your morning walk? How can emotional speech analysis monitor the emotional state of employees, students, or people in crowds? Pick up this report and find out.

Digital business has been driving the transformation of underlying information technology (IT) infrastructure to be more efficient, secure, adaptive, and integrated. IT must be able to handle the explosive growth of mobile clients and employees. It also must be able to process enormous amounts of data to provide deep and real-time insights to help achieve the greatest business impact. This IBM® Redbooks® publication addresses the new IBM z Systems™ single frame, the IBM z13s server. IBM z Systems servers are the trusted enterprise platform for integrating data, transactions, and insight. A data-centric infrastructure must always be available with a 99.999% or better availability, have flawless data integrity, and be secured from misuse. It needs to be an integrated infrastructure that can support new applications. It also needs to have integrated capabilities that can provide new mobile capabilities with real-time analytics delivered by a secure cloud infrastructure. IBM z13s servers are designed with improved scalability, performance, security, resiliency, availability, and virtualization. The superscalar design allows z13s servers to deliver a record level of capacity over the prior single frame z Systems server. In its maximum configuration, the z13s server is powered by up to 20 client characterizable microprocessors (cores) running at 4.3 GHz. This configuration can run more than 18,000 millions of instructions per second (MIPS) and up to 4 TB of client memory. The IBM z13s Model N20 is estimated to provide up to 100% more total system capacity than the IBM zEnterprise® BC12 Model H13. This book provides information about the IBM z13s server and its functions, features, and associated software support. Greater detail is offered in areas relevant to technical planning. It is intended for systems engineers, consultants, planners, and anyone who wants to understand the IBM z Systems™ functions and plan for their usage. It is not intended as an introduction to mainframes. Readers are expected to be generally familiar with existing IBM z Systems technology and terminology.

Learn the right cutting-edge skills and knowledge to leverage Spark Streaming to implement a wide array of real-time, streaming applications. This book walks you through end-to-end real-time application development using real-world applications, data, and code. Taking an application-first approach, each chapter introduces use cases from a specific industry and uses publicly available datasets from that domain to unravel the intricacies of production-grade design and implementation. The domains covered in Pro Spark Streaming include social media, the sharing economy, finance, online advertising, telecommunication, and IoT. In the last few years, Spark has become synonymous with big data processing. DStreams enhance the underlying Spark processing engine to support streaming analysis with a novel micro-batch processing model. Pro Spark Streaming by Zubair Nabi will enable you to become a specialist of latency sensitive applications by leveraging the key features of DStreams, micro-batch processing, and functional programming. To this end, the book includes ready-to-deploy examples and actual code. Pro Spark Streaming will act as the bible of Spark Streaming. What You'll Learn Discover Spark Streaming application development and best practices Work with the low-level details of discretized streams Optimize production-grade deployments of Spark Streaming via configuration recipes and instrumentation using Graphite, collectd, and Nagios Ingest data from disparate sources including MQTT, Flume, Kafka, Twitter, and a custom HTTP receiver Integrate and couple with HBase, Cassandra, and Redis Take advantage of design patterns for side-effects and maintaining state across the Spark Streaming micro-batch model Implement real-time and scalable ETL using data frames, SparkSQL, Hive, and SparkR Use streaming machine learning, predictive analytics, and recommendations Mesh batch processing with stream processing via the Lambda architecture Who This Book Is For Data scientists, big data experts, BI analysts, and data architects.

Spark GraphX in Action starts out with an overview of Apache Spark and the GraphX graph processing API. This example-based tutorial then teaches you how to configure GraphX and how to use it interactively. Along the way, you'll collect practical techniques for enhancing applications and applying machine learning algorithms to graph data. About the Technology GraphX is a powerful graph processing API for the Apache Spark analytics engine that lets you draw insights from large datasets. GraphX gives you unprecedented speed and capacity for running massively parallel and machine learning algorithms. About the Book Spark GraphX in Action begins with the big picture of what graphs can be used for. This example-based tutorial teaches you how to use GraphX interactively. You'll start with a crystal-clear introduction to building big data graphs from regular data, and then explore the problems and possibilities of implementing graph algorithms and architecting graph processing pipelines. Along the way, you'll collect practical techniques for enhancing applications and applying machine learning algorithms to graph data. What's Inside Understanding graph technology Using the GraphX API Developing algorithms for big graphs Machine learning with graphs Graph visualization About the Reader Readers should be comfortable writing code. Experience with Apache Spark and Scala is not required. About the Authors Michael Malak has worked on Spark applications for Fortune 500 companies since early 2013. Robin East has worked as a consultant to large organizations for over 15 years and is a data scientist at Worldpay. Quotes Learn complex graph processing from two experienced authors…A comprehensive guide. - Gaurav Bhardwaj, 3Pillar Global The best resource to go from GraphX novice to expert in the least amount of time. - Justin Fister, PaperRater A must-read for anyone serious about large-scale graph data mining! - Antonio Magnaghi, OpenMail Reveals the awesome and elegant capabilities of working with linked data for large-scale datasets. - Sumit Pal, Independent consultant

Unleash the power of Python and its robust data science capabilities About This Book Unleash the power of Python 3 objects Learn to use powerful Python libraries for effective data processing and analysis Harness the power of Python to analyze data and create insightful predictive models Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics Who This Book Is For Entry-level analysts who want to enter in the data science world will find this course very useful to get themselves acquainted with Python's data science capabilities for doing real-world data analysis. What You Will Learn Install and setup Python Implement objects in Python by creating classes and defining methods Get acquainted with NumPy to use it with arrays and array-oriented computing in data analysis Create effective visualizations for presenting your data using Matplotlib Process and analyze data using the time series capabilities of pandas Interact with different kind of database systems, such as file, disk format, Mongo, and Redis Apply data mining concepts to real-world problems Compute on big data, including real-time data from the Internet Explore how to use different machine learning models to ask different questions of your data In Detail The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you'll have gained key skills and be ready for the material in the next module. The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, it's time that you dive into the field of data science. In the second module, you'll learn how to perform data analysis using Python in a practical and example-driven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, we'll discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls. Style and approach This course includes all the resources that will help you jump into the data science field with Python and learn how to make sense of data. The aim is to create a smooth learning path that will teach you how to get started with powerful Python libraries and perform various data science techniques in depth.

To outsource or not to outsource -- that is the question: Whether 'tis more efficient to tap The skills and talents of those who bill by the hour, Or to bring resources inside as full-time staff, And, by doing so, manage them. To contract, to outsource -- No more -- and by outsource to say we get Our insights and our implementation work Managed by others -- 'tis a scenario Devoutly to be wished. To contract, to outsource -- To outsource, perchance to analyze. Aye, there's the rub. Besides ignoring iambic pentameter in the process of butchering a Shakespearean reference, this episode, perchance, also makes reference to the following: House of Lies Analytics Made Skeezy Data Smart by John Forman Sim Daltonism

Learn how to configure, implement, enhance, and customize SAP OEE to address manufacturing performance management. Manufacturing Performance Management using SAP OEE will show you how to connect your business processes with your plant systems and how to integrate SAP OEE with ERP through standard workflows and shop floor systems for automated data collection. Manufacturing Performance Management using SAP OEE is a must-have comprehensive guide to implementing SAP OEE. It will ensure that SAP consultants and users understand how SAP OEE can offer solutions for manufacturing performance management in process industries. With this book in hand, managing shop floor execution effectively will become easier than ever. Authors Dipankar Saha and Mahalakshmi Symsunder, both SAP manufacturing solution experts, and Sumanta Chakraborty, product owner of SAP OEE, will explain execution and processing related concepts, manual and automatic data collection through the OEE Worker UI, and how to enhance and customize interfaces and dashboards for your specific purposes. You'll learn how to capture and categorize production and loss data and use it effectively for root-cause analysis. In addition, this book will show you: Various down-time handling scenarios. How to monitor, calculate, and define standard as well as industry-specific KPIs. How to carry out standard operational analytics for continuous improvement on the shop floor, at local plant level using MII and SAP Lumira, and also global consolidated analytics at corporation level using SAP HANA. Steps to benchmark manufacturing performance to compare similar manufacturing plants' performance, leading to a more efficient and effective shop floor. Manufacturing Performance Management using SAP OEE will provide you with in-depth coverage of SAP OEE and how to effectively leverage its features. This will allow you to efficiently manage the manufacturing process and to enhance the shop floor's overall performance, making you the sought-after SAP OEE expert in the organization. Manufacturing Performance Management using SAP OEE will provide you with in-depth coverage of SAP OEE and how to effectively leverage its features. This will allow you to efficiently manage the manufacturing process and to enhance the shop floor's overall performance, making you the sought-after SAP OEE expert in the organization. What You Will Learn Configure your ERP OEE add-on to build your plant and global hierarchy and relevant master data and KPIs Use the SAP OEE standard integration (SAP OEEINT) to integrate your ECC and OEE system to establish bi-directional integration between the enterprise and the shop floor Enable your shop floor operator on the OEE Worker UI to handle shop floor production execution Use SAP OEE as a tool for measuring manufacturing performance Enhance and customize SAP OEE to suit your specific requirements Create local plant-based reporting using SAP Lumira and MII Use standard SAP OEE HANA analytics Who This Book Is For SAP MII, ME, and OEE consultants and users who will implement and use the solution.

This IBM® Redbooks® publication addresses topics to use the virtualization strengths of the IBM POWER8® platform to solve clients' system resource utilization challenges and maximize systems' throughput and capacity. This book addresses performance tuning topics that will help answer clients' complex analytic workload requirements, help maximize systems' resources, and provide expert-level documentation to transfer the how-to-skills to the worldwide teams. This book strengthens the position of IBM Analytics and Big Data solutions with a well-defined and documented deployment model within a POWER8 virtualized environment, offering clients a planned foundation for security, scaling, capacity, resilience, and optimization for analytics workloads. This book is targeted toward technical professionals (analytics consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for providing analytics solutions and support on IBM Power Systems™.

Learning Pentaho CTools is a comprehensive guide to building sophisticated and custom analytics dashboards using the powerful capabilities of Pentaho CTools. This book walks you through the process of creating interactive dashboards, integrating data sources, and applying data visualization best practices. You'll quickly gain the expertise needed to create impactful dashboards with ease. What this Book will help me do Master installing and configuring CTools for Pentaho to jumpstart dashboard development. Harness diverse data sources and deliver data in formats like CSV, JSON, and XML for customized analytics. Design and implement dynamic, visually stunning dashboards using Community Dashboard Framework (CDF). Deploy and integrate plugins, leverage widgets, and manage dashboards effectively with version control. Enhance interactivity by customizing dashboard components, charts, and filters to suit unique requirements. Author(s) None Gaspar, an expert in Pentaho and its tools, has been a Senior Consultant at Pentaho, where he gained in-depth experience crafting analytics solutions. He brings to this book his teaching passion and field expertise, combining theoretical insights with practical applications. His approachable style ensures readers can follow technical concepts effectively. Who is it for? This book is ideal for developers who are looking to enhance their understanding of Pentaho's CTools portfolio to build advanced dashboards. A working knowledge of JavaScript and CSS will enable readers to get the most out of this guide. Whether you aim to extend your analytics capabilities or learn the tools from scratch, this book bridges the gap between learning and application.