Ibm Cognos Tm1 Developer S Certification Guide
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Ibm Cognos Tm1 Developer S Certification Guide
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Author : James D. Miller
language : en
Publisher: Packt Publishing Ltd
Release Date : 2012-01-01
Ibm Cognos Tm1 Developer S Certification Guide written by James D. Miller and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-01-01 with Computers categories.
This book is packed with real word 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!
Ibm Cognos Tm1 Developers Certification Guide
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Author : James D. Miller
language : en
Publisher: Packt Publishing, Limited
Release Date : 2012
Ibm Cognos Tm1 Developers Certification Guide written by James D. Miller and has been published by Packt Publishing, Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
This book is packed with real word 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!
Mastering Predictive Analytics With R
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Author : James D. Miller
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-08-18
Mastering Predictive Analytics With R written by James D. Miller and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-18 with Computers categories.
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.
Big Data Visualization
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Author : James D. Miller
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-02-28
Big Data Visualization written by James D. Miller and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-28 with Computers categories.
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.
Mastering Non Functional Requirements
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Author : Sameer Paradkar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-05-18
Mastering Non Functional Requirements written by Sameer Paradkar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-18 with Computers categories.
This book covers the most critical 24 NFRs that are applicable to IT applications and systems. About This Book Explains three stages of nonfunctional requirements, that is, analysis, architecture, and assessment In-depth knowledge of NFR framework and taxonomy that provides guidance around the modelling phase for the NFRs Coverage of 24 critical and pivotal NFRs, including the analysis, architecture, and assessment. Who This Book Is For The primary audience for this title are the gamut of roles starting from IT consultant to chief architects who are responsible to deliver strategic, tactical, and operational engagements for fortune 100 customers worldwide. Nonfunctional requirements are the key to any software / IT program. They cannot be overlooked or ignored. The book provides a comprehensive approach from analysis, architecture, and measurement of nonfunctional requirements. The book includes considerations for bespoke (Java, .Net, and COTS applications). These are applicable to IT applications from various domains. The book outlines the methodology for capturing the NFRs and also describes a framework that can be leveraged by analysts and architects for tackling NFRs for various engagements. The audience for this book include business analysts, enterprise architects, business architects, solution architects, technical architects/designers, domain/security/integration architects, software developers, support engineers and test engineers, technical project managers, project leads/technical leads/technical project managers, and students from the computer science/IT stream What You Will Learn Learn techniques related to the analysis, architecture, and monitoring of NFRs Understand the various tools, techniques, and processes in order to improve the overall quality of the desired outcomes Embrace the best practices of architecting, metrics, and success factors for NFRs Identify the common pitfalls to be avoided and the patterns to leverage Understand taxonomy and framework for NFRs Learn the design guidelines for architecting applications and systems relating to NFRs Abstract different methodologies to analyze and gather NFRs In Detail Non-functional Requirements are key to any software/IT program and cannot be overlooked or ignored. This book provides a comprehensive approach to the analysis, architecture, and measurement of NFRs. It includes considerations for bespoke Java, .NET, and COTS applications that are applicable to IT applications/systems in different domains. The book outlines the methodology for capturing the NFRs and also describes a framework that can be leveraged by analysts and architects for tackling NFRs for various engagements. This book starts off by explaining the various KPIs, taxonomies, and methods for identifying NFRs. Learn the design guidelines for architecting applications and systems relating to NFRs and design principles to achieve the desired outcome. We will then move on to various key tiers/layers and patterns pertaining to the business, database, and integrating tiers. After this, we will dive deep into the topics pertaining to techniques related to monitoring and measurement of NFRs, such as sizing, analytical modeling, and quality assurance. Lastly, we end the book by describing some pivotal NFRs and checklists for the software quality attributes related to the business, application, data, and infrastructure domains. Style and approach The book takes a pragmatic approach, describing various techniques related to the analysis of NFRs, the architecture of NFRs, and assessment of NFRs.
Cracking The It Architect Interview
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Author : Sameer Paradkar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-11-30
Cracking The It Architect Interview written by Sameer Paradkar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-30 with Computers categories.
The ultimate guide to successful interviews for Enterprise, Business, Domain, Solution, and Technical Architect roles as well as IT Advisory Consultant and Software Designer roles About This Book Learn about Enterprise Architects IT strategy and NFR – this book provides you with methodologies, best practices, and frameworks to ace your interview A holistic view of key architectural skills and competencies with 500+ questions that cover 12 domains 100+ diagrams depicting scenarios, models, and methodologies designed to help you prepare for your interview Who This Book Is For This book is for aspiring enterprise, business, domain, solution, and technical architects. It is also ideal for IT advisory consultants and IT designers who wish to interview for such a role. Interviewers will be able leverage this book to make sure they hire candidates with the right competencies to meet the role requirements. What You Will Learn Learn about IT strategies, NFR, methodologies, best practices, and frameworks to ace your interview Get a holistic view of key concepts, design principles, and patterns related to evangelizing web and Java enterprise applications Discover interview preparation guidelines through case studies Use this as a reference guide for adopting best practices, standards, and design guidelines Get a better understanding with 60+ diagrams depicting various scenarios, models, and methodologies Benefit from coverage of all architecture domains including EA (Business, Data, Infrastructure, and Application), SA, integration, NFRs, security, and SOA, with extended coverage from IT strategies to the NFR domain In Detail An architect attends multiple interviews for jobs or projects during the course of his or her career. This book is an interview resource created for designers, consultants, technical, solution, domain, enterprise, and chief architects to help them perform well in interview discussions and launch a successful career. The book begins by providing descriptions of architecture skills and competencies that cover the 12 key domains, including 350+ questions relating to these domains. The goal of this book is to cover all the core architectural domains. From an architect's perspective, it is impossible to revise or learn about all these key areas without a good reference guide – this book is the solution. It shares experiences, learning, insights, and proven methodologies that will benefit practitioners, SMEs, and aspirants in the long run. This book will help you tackle the NFR domain, which is a key aspect pertaining to architecting applications. It typically takes years to understand the core concepts, fundamentals, patterns, and principles related to architecture and designs. This book is a goldmine for the typical questions asked during an interview and will help prepare you for success! Style and approach This book will help you prepare for interviews for architectural profiles by providing likely questions, explanations, and expected answers. It is an insight-rich guide that will help you develop strategic, tactical, and operational thinking for your interview.
Ibm Cognos Dynamic Cubes
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Author : Dmitriy Beryoza
language : en
Publisher: IBM Redbooks
Release Date : 2015-07-31
Ibm Cognos Dynamic Cubes written by Dmitriy Beryoza and has been published by IBM Redbooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-31 with Computers categories.
IBM® Cognos® Business Intelligence (BI) provides a proven enterprise BI platform with an open data strategy. Cognos BI provides customers with the ability to use data from any source, package it into a business model, and make it available to consumers in various interfaces that are tailored to the task. IBM Cognos Dynamic Cubes complements the existing Cognos BI capabilities and continues the tradition of an open data model. It focuses on extending the scalability of the IBM Cognos platform to enable speed-of-thought analytics over terabytes of enterprise data, without having to invest in a new data warehouse appliance. This capability adds a new level of query intelligence so you can unleash the power of your enterprise data warehouse. This IBM Redbooks® publication addresses IBM Cognos Business Intelligence V10.2.2 and specifically, the IBM Cognos Dynamic Cubes capabilities. This book can help you in the following ways: Understand core features of the Cognos Dynamic Cubes capabilities of Cognos BI V10.2 Learn by example with practical scenarios by using the IBM Cognos samples This book uses fictional business scenarios to demonstrate the power and capabilities of IBM Cognos Dynamic Cubes. It primarily focuses on the roles of the modeler, administrator, and IT architect.
Ibm Cognos Insight
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Author : Sanjeev Datta
language : en
Publisher: Packt Publishing Ltd
Release Date : 2012
Ibm Cognos Insight written by Sanjeev Datta and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Business categories.
This book takes a practical tutorial approach to teaching users the features of Cognos Insight. New and existing users of Cognos Insight who are looking to gain more knowledge about the product and Business Analytics in general.
Ibm Software For Sap Solutions
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Author : Yaro Dunchych
language : en
Publisher: IBM Redbooks
Release Date : 2015-09-29
Ibm Software For Sap Solutions written by Yaro Dunchych and has been published by IBM Redbooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-29 with Computers categories.
SAP is a market leader in enterprise business application software. SAP solutions provide a rich set of composable application modules, and configurable functional capabilities that are expected from a comprehensive enterprise business application software suite. In most cases, companies that adopt SAP software remain heterogeneous enterprises running both SAP and non-SAP systems to support their business processes. Regardless of the specific scenario, in heterogeneous enterprises most SAP implementations must be integrated with a variety of non-SAP enterprise systems: Portals Messaging infrastructure Business process management (BPM) tools Enterprise Content Management (ECM) methods and tools Business analytics (BA) and business intelligence (BI) technologies Security Systems of record Systems of engagement The tooling included with SAP software addresses many needs for creating SAP-centric environments. However, the classic approach to implementing SAP functionality generally leaves the business with a rigid solution that is difficult and expensive to change and enhance. When SAP software is used in a large, heterogeneous enterprise environment, SAP clients face the dilemma of selecting the correct set of tools and platforms to implement SAP functionality, and to integrate the SAP solutions with non-SAP systems. This IBM® Redbooks® publication explains the value of integrating IBM software with SAP solutions. It describes how to enhance and extend pre-built capabilities in SAP software with best-in-class IBM enterprise software, enabling clients to maximize return on investment (ROI) in their SAP investment and achieve a balanced enterprise architecture approach. This book describes IBM Reference Architecture for SAP, a prescriptive blueprint for using IBM software in SAP solutions. The reference architecture is focused on defining the use of IBM software with SAP, and is not intended to address the internal aspects of SAP components. The chapters of this book provide a specific reference architecture for many of the architectural domains that are each important for a large enterprise to establish common strategy, efficiency, and balance. The majority of the most important architectural domain topics, such as integration, process optimization, master data management, mobile access, Enterprise Content Management, business intelligence, DevOps, security, systems monitoring, and so on, are covered in the book. However, there are several other architectural domains which are not included in the book. This is not to imply that these other architectural domains are not important or are less important, or that IBM does not offer a solution to address them. It is only reflective of time constraints, available resources, and the complexity of assembling a book on an extremely broad topic. Although more content could have been added, the authors feel confident that the scope of architectural material that has been included should provide organizations with a fantastic head start in defining their own enterprise reference architecture for many of the important architectural domains, and it is hoped that this book provides great value to those reading it. This IBM Redbooks publication is targeted to the following audiences: Client decision makers and solution architects leading enterprise transformation projects and wanting to gain further insight so that they can benefit from the integration of IBM software in large-scale SAP projects. IT architects and consultants integrating IBM technology with SAP solutions.
Statistics For Data Science
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Author : James D. Miller
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-11-17
Statistics For Data Science written by James D. Miller and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-17 with Computers categories.
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