Download or read book Big Data Analytics Made Easy written by Y. Lakshmi Prasad and published by Notion Press. This book was released on 2016-12-14 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics Made Easy is a must-read for everybody as it explains the power of Analytics in a simple and logical way along with an end to end code in R. Even if you are a novice in Big Data Analytics, you will still be able to understand the concepts explained in this book. If you are already working in Analytics and dealing with Big Data, you will still find this book useful, as it covers exhaustive Data Mining Techniques, which are considered to be Advanced topics. It covers Machine Learning concepts and provides in-depth knowledge on unsupervised as well as supervised Learning, which is very important for decision-making. The toughest Data Analytics concepts are made simpler, It features examples from all the domains so that the reader gets connected to the book easily. This book is like a personal trainer that will help you master the Art of Data Science.

Download or read book Data Analytics Made Easy written by Andrea De Mauro and published by Packt Publishing Ltd. This book was released on 2021-08-30 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to gain insights from your data as well as machine learning and become a presentation pro who can create interactive dashboards Key FeaturesEnhance your presentation skills by implementing engaging data storytelling and visualization techniquesLearn the basics of machine learning and easily apply machine learning models to your dataImprove productivity by automating your data processesBook Description Data Analytics Made Easy is an accessible beginner's guide for anyone working with data. The book interweaves four key elements: Data visualizations and storytelling – Tired of people not listening to you and ignoring your results? Don't worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience. Automating your data workflows – Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You'll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components. Machine learning – Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You'll not only be able to understand data scientists' machine learning models; you'll be able to challenge them and build your own. Creating interactive dashboards – Follow the book's simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results. What you will learnUnderstand the potential of data and its impact on your businessImport, clean, transform, combine data feeds, and automate your processesInfluence business decisions by learning to create engaging presentationsBuild real-world models to improve profitability, create customer segmentation, automate and improve data reporting, and moreCreate professional-looking and business-centric visuals and dashboardsOpen the lid on the black box of AI and learn about and implement supervised and unsupervised machine learning modelsWho this book is for This book is for beginners who work with data and those who need to know how to interpret their business/customer data. The book also covers the high-level concepts of data workflows, machine learning, data storytelling, and visualizations, which are useful for managers. No previous math, statistics, or computer science knowledge is required.

Download or read book Data Analytics Made Easy written by Andrea de Mauro and published by . This book was released on 2021-08-30 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: Make informed decisions using data analytics, machine learning, and data visualizations Key Features: Take raw data and transform it to add value to your organization Learn the art of telling stories with your data to engage with your audience Apply machine learning algorithms to your data with a few clicks of a button Book Description: Data analytics has become a necessity in modern business, and skills such as data visualization, machine learning, and digital storytelling are now essential in every field. If you want to make sense of your data and add value with informed decisions, this is the book for you. Data Analytics Made Easy is an accessible guide to help you start analyzing data and quickly apply these skills to your work. It focuses on how to generate insights from your data at the click of a few buttons, using the popular tools KNIME and Microsoft Power BI. The book introduces the concepts of data analytics and shows you how to get your data ready and apply ML algorithms. Implement a full predictive analytics solution with KNIME and assess its level of accuracy. Create impressive visualizations with Microsoft Power BI and learn the greatest secret in successful analytics - how to tell a story with your data. You'll connect the dots on the various stages of the data-to-insights process and gain an overview of alternative tools, including Tableau and H20 Driverless AI. By the end of this book, you will have learned how to implement machine learning algorithms and sell the results to your customers without writing a line of code. What You Will Learn: Understand the potential of data and its impact on any business Influence business decisions with effective data storytelling when delivering insights Use KNIME to import, clean, transform, combine data feeds, and automate recurring workflows Learn the basics of machine learning and AutoML to add value to your organization Build, test, and validate simple supervised and unsupervised machine learning models with KNIME Use Power BI and Tableau to build professional-looking and business-centric visuals and dashboards Who this book is for: Whether you are working with data experts or want to find insights in your business' data, you'll find this book an effective way to add analytics to your skill stack. No previous math, statistics, or computer science knowledge is required.

Download or read book Healthcare Analytics Made Simple written by Vikas (Vik) Kumar and published by Packt Publishing Ltd. This book was released on 2018-07-31 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Add a touch of data analytics to your healthcare systems and get insightful outcomes Key Features Perform healthcare analytics with Python and SQL Build predictive models on real healthcare data with pandas and scikit-learn Use analytics to improve healthcare performance Book Description In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists’ work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples. What you will learn Gain valuable insight into healthcare incentives, finances, and legislation Discover the connection between machine learning and healthcare processes Use SQL and Python to analyze data Measure healthcare quality and provider performance Identify features and attributes to build successful healthcare models Build predictive models using real-world healthcare data Become an expert in predictive modeling with structured clinical data See what lies ahead for healthcare analytics Who this book is for Healthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare.

Download or read book Big Data at Work written by Thomas Davenport and published by Harvard Business Review Press. This book was released on 2014-02-04 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Go ahead, be skeptical about big data. The author was—at first. When the term “big data” first came on the scene, bestselling author Tom Davenport (Competing on Analytics, Analytics at Work) thought it was just another example of technology hype. But his research in the years that followed changed his mind. Now, in clear, conversational language, Davenport explains what big data means—and why everyone in business needs to know about it. Big Data at Work covers all the bases: what big data means from a technical, consumer, and management perspective; what its opportunities and costs are; where it can have real business impact; and which aspects of this hot topic have been oversold. This book will help you understand: • Why big data is important to you and your organization • What technology you need to manage it • How big data could change your job, your company, and your industry • How to hire, rent, or develop the kinds of people who make big data work • The key success factors in implementing any big data project • How big data is leading to a new approach to managing analytics With dozens of company examples, including UPS, GE, Amazon, United Healthcare, Citigroup, and many others, this book will help you seize all opportunities—from improving decisions, products, and services to strengthening customer relationships. It will show you how to put big data to work in your own organization so that you too can harness the power of this ever-evolving new resource.

Download or read book Creating Value with Big Data Analytics written by Peter C. Verhoef and published by Routledge. This book was released on 2016-01-08 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: Our newly digital world is generating an almost unimaginable amount of data about all of us. Such a vast amount of data is useless without plans and strategies that are designed to cope with its size and complexity, and which enable organisations to leverage the information to create value. This book is a refreshingly practical, yet theoretically sound roadmap to leveraging big data and analytics. Creating Value with Big Data Analytics provides a nuanced view of big data development, arguing that big data in itself is not a revolution but an evolution of the increasing availability of data that has been observed in recent times. Building on the authors’ extensive academic and practical knowledge, this book aims to provide managers and analysts with strategic directions and practical analytical solutions on how to create value from existing and new big data. By tying data and analytics to specific goals and processes for implementation, this is a much-needed book that will be essential reading for students and specialists of data analytics, marketing research, and customer relationship management.

Download or read book Big Data written by Viktor Mayer-Schönberger and published by Houghton Mifflin Harcourt. This book was released on 2013 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: A exploration of the latest trend in technology and the impact it will have on the economy, science, and society at large.

Download or read book Data Smart written by John W. Foreman and published by John Wiley & Sons. This book was released on 2013-10-31 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the "data scientist," toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know.

Download or read book Big Data in Practice written by Bernard Marr and published by John Wiley & Sons. This book was released on 2016-03-22 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: The best-selling author of Big Data is back, this time with a unique and in-depth insight into how specific companies use big data. Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective. From technology, media and retail, to sport teams, government agencies and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety and so much more. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved and the processes put it place to make it practical, as well as the technical details, challenges and lessons learned from each unique scenario. Learn how predictive analytics helps Amazon, Target, John Deere and Apple understand their customers Discover how big data is behind the success of Walmart, LinkedIn, Microsoft and more Learn how big data is changing medicine, law enforcement, hospitality, fashion, science and banking Develop your own big data strategy by accessing additional reading materials at the end of each chapter

Download or read book Big Data Analytics with Hadoop 3 written by Sridhar Alla and published by Packt Publishing Ltd. This book was released on 2018-05-31 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore big data concepts, platforms, analytics, and their applications using the power of Hadoop 3 Key Features Learn Hadoop 3 to build effective big data analytics solutions on-premise and on cloud Integrate Hadoop with other big data tools such as R, Python, Apache Spark, and Apache Flink Exploit big data using Hadoop 3 with real-world examples Book Description Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples. Once you have taken a tour of Hadoop 3’s latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases. By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly. What you will learn Explore the new features of Hadoop 3 along with HDFS, YARN, and MapReduce Get well-versed with the analytical capabilities of Hadoop ecosystem using practical examples Integrate Hadoop with R and Python for more efficient big data processing Learn to use Hadoop with Apache Spark and Apache Flink for real-time data analytics Set up a Hadoop cluster on AWS cloud Perform big data analytics on AWS using Elastic Map Reduce Who this book is for Big Data Analytics with Hadoop 3 is for you if you are looking to build high-performance analytics solutions for your enterprise or business using Hadoop 3’s powerful features, or you’re new to big data analytics. A basic understanding of the Java programming language is required.

Download or read book Big Data Analytics written by Frank J. Ohlhorst and published by John Wiley & Sons. This book was released on 2012-11-15 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unique insights to implement big data analytics and reap big returns to your bottom line Focusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in Big Data Analytics. This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportunities. Reveals big data analytics as the next wave for businesses looking for competitive advantage Takes an in-depth look at the financial value of big data analytics Offers tools and best practices for working with big data Once the domain of large on-line retailers such as eBay and Amazon, big data is now accessible by businesses of all sizes and across industries. From how to mine the data your company collects, to the data that is available on the outside, Big Data Analytics shows how you can leverage big data into a key component in your business's growth strategy.

Download or read book Big Data Analytics with Spark written by Mohammed Guller and published by Apress. This book was released on 2015-12-29 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics with Spark is a step-by-step guide for learning Spark, which is an open-source fast and general-purpose cluster computing framework for large-scale data analysis. You will learn how to use Spark for different types of big data analytics projects, including batch, interactive, graph, and stream data analysis as well as machine learning. In addition, this book will help you become a much sought-after Spark expert. Spark is one of the hottest Big Data technologies. The amount of data generated today by devices, applications and users is exploding. Therefore, there is a critical need for tools that can analyze large-scale data and unlock value from it. Spark is a powerful technology that meets that need. You can, for example, use Spark to perform low latency computations through the use of efficient caching and iterative algorithms; leverage the features of its shell for easy and interactive Data analysis; employ its fast batch processing and low latency features to process your real time data streams and so on. As a result, adoption of Spark is rapidly growing and is replacing Hadoop MapReduce as the technology of choice for big data analytics. This book provides an introduction to Spark and related big-data technologies. It covers Spark core and its add-on libraries, including Spark SQL, Spark Streaming, GraphX, and MLlib. Big Data Analytics with Spark is therefore written for busy professionals who prefer learning a new technology from a consolidated source instead of spending countless hours on the Internet trying to pick bits and pieces from different sources. The book also provides a chapter on Scala, the hottest functional programming language, and the program that underlies Spark. You’ll learn the basics of functional programming in Scala, so that you can write Spark applications in it. What's more, Big Data Analytics with Spark provides an introduction to other big data technologies that are commonly used along with Spark, like Hive, Avro, Kafka and so on. So the book is self-sufficient; all the technologies that you need to know to use Spark are covered. The only thing that you are expected to know is programming in any language. There is a critical shortage of people with big data expertise, so companies are willing to pay top dollar for people with skills in areas like Spark and Scala. So reading this book and absorbing its principles will provide a boost—possibly a big boost—to your career.

Download or read book Big Data Analytics with Java written by Rajat Mehta and published by Packt Publishing Ltd. This book was released on 2017-07-31 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the basics of analytics on big data using Java, machine learning and other big data tools About This Book Acquire real-world set of tools for building enterprise level data science applications Surpasses the barrier of other languages in data science and learn create useful object-oriented codes Extensive use of Java compliant big data tools like apache spark, Hadoop, etc. Who This Book Is For This book is for Java developers who are looking to perform data analysis in production environment. Those who wish to implement data analysis in their Big data applications will find this book helpful. What You Will Learn Start from simple analytic tasks on big data Get into more complex tasks with predictive analytics on big data using machine learning Learn real time analytic tasks Understand the concepts with examples and case studies Prepare and refine data for analysis Create charts in order to understand the data See various real-world datasets In Detail This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naive Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world. Style and approach The approach of book is to deliver practical learning modules in manageable content. Each chapter is a self-contained unit of a concept in big data analytics. Book will step by step builds the competency in the area of big data analytics. Examples using real world case studies to give ideas of real applications and how to use the techniques mentioned. The examples and case studies will be shown using both theory and code.

Download or read book Foundations of Data Science written by Avrim Blum and published by Cambridge University Press. This book was released on 2020-01-23 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.

Download or read book Big Data For Dummies written by Judith S. Hurwitz and published by John Wiley & Sons. This book was released on 2013-04-02 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Find the right big data solution for your business or organization Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work. Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals Authors are experts in information management, big data, and a variety of solutions Explains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more Provides essential information in a no-nonsense, easy-to-understand style that is empowering Big Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization.

Download or read book Storage Area Networks For Dummies written by Christopher Poelker and published by John Wiley & Sons. This book was released on 2009-01-09 with total page 467 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you’ve been charged with setting up storage area networks for your company, learning how SANs work and managing data storage problems might seem challenging. Storage Area Networks For Dummies, 2nd Edition comes to the rescue with just what you need to know. Whether you already a bit SAN savvy or you’re a complete novice, here’s the scoop on how SANs save money, how to implement new technologies like data de-duplication, iScsi, and Fibre Channel over Ethernet, how to develop SANs that will aid your company’s disaster recovery plan, and much more. For example, you can: Understand what SANs are, whether you need one, and what you need to build one Learn to use loops, switches, and fabric, and design your SAN for peak performance Create a disaster recovery plan with the appropriate guidelines, remote site, and data copy techniques Discover how to connect or extend SANs and how compression can reduce costs Compare tape and disk backups and network vs. SAN backup to choose the solution you need Find out how data de-duplication makes sense for backup, replication, and retention Follow great troubleshooting tips to help you find and fix a problem Benefit from a glossary of all those pesky acronyms From the basics for beginners to advanced features like snapshot copies, storage virtualization, and heading off problems before they happen, here’s what you need to do the job with confidence!

Download or read book Unstructured Data Analytics written by Jean Paul Isson and published by John Wiley & Sons. This book was released on 2018-03-13 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Turn unstructured data into valuable business insight Unstructured Data Analytics provides an accessible, non-technical introduction to the analysis of unstructured data. Written by global experts in the analytics space, this book presents unstructured data analysis (UDA) concepts in a practical way, highlighting the broad scope of applications across industries, companies, and business functions. The discussion covers key aspects of UDA implementation, beginning with an explanation of the data and the information it provides, then moving into a holistic framework for implementation. Case studies show how real-world companies are leveraging UDA in security and customer management, and provide clear examples of both traditional business applications and newer, more innovative practices. Roughly 80 percent of today's data is unstructured in the form of emails, chats, social media, audio, and video. These data assets contain a wealth of valuable information that can be used to great advantage, but accessing that data in a meaningful way remains a challenge for many companies. This book provides the baseline knowledge and the practical understanding companies need to put this data to work. Supported by research with several industry leaders and packed with frontline stories from leading organizations such as Google, Amazon, Spotify, LinkedIn, Pfizer Manulife, AXA, Monster Worldwide, Under Armour, the Houston Rockets, DELL, IBM, and SAS Institute, this book provide a framework for building and implementing a successful UDA center of excellence. You will learn: How to increase Customer Acquisition and Customer Retention with UDA The Power of UDA for Fraud Detection and Prevention The Power of UDA in Human Capital Management & Human Resource The Power of UDA in Health Care and Medical Research The Power of UDA in National Security The Power of UDA in Legal Services The Power of UDA for product development The Power of UDA in Sports The future of UDA From small businesses to large multinational organizations, unstructured data provides the opportunity to gain consumer information straight from the source. Data is only as valuable as it is useful, and a robust, effective UDA strategy is the first step toward gaining the full advantage. Unstructured Data Analytics lays this space open for examination, and provides a solid framework for beginning meaningful analysis.