Data Science And Big Data Analytics Review

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Data Science And Big Data Analytics Features Comparison Tables

Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data

Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data

Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data Features

  • Wiley
Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software.

This book will help you:

  • Become a contributor on a data science team
  • Deploy a structured lifecycle approach to data analytics problems
  • Apply appropriate analytic techniques and tools to analyzing big data
  • Learn how to tell a compelling story with data to drive business action
  • Prepare for EMC Proven Professional Data Science Certification

Corresponding data sets are available at www.wiley.com/go/9781118876138.

Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

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Big Data Science & Analytics: A Hands-On Approach

Big Data Science & Analytics: A Hands-On Approach

Big Data Science & Analytics: A Hands-On Approach Features

    We are living in the dawn of what has been termed as the "Fourth Industrial Revolution", which is marked through the emergence of "cyber-physical systems" where software interfaces seamlessly over networks with physical systems, such as sensors, smartphones, vehicles, power grids or buildings, to create a new world of Internet of Things (IoT). Data and information are fuel of this new age where powerful analytics algorithms burn this fuel to generate decisions that are expected to create a smarter and more efficient world for all of us to live in. This new area of technology has been defined as Big Data Science and Analytics, and the industrial and academic communities are realizing this as a competitive technology that can generate significant new wealth and opportunity. Big data is defined as collections of datasets whose volume, velocity or variety is so large that it is difficult to store, manage, process and analyze the data using traditional databases and data processing tools. Big data science and analytics deals with collection, storage, processing and analysis of massive-scale data. Industry surveys, by Gartner and e-Skills, for instance, predict that there will be over 2 million job openings for engineers and scientists trained in the area of data science and analytics alone, and that the job market is in this area is growing at a 150 percent year-over-year growth rate. We have written this textbook, as part of our expanding "A Hands-On Approach"(TM) series, to meet this need at colleges and universities, and also for big data service providers who may be interested in offering a broader perspective of this emerging field to accompany their customer and developer training programs. The typical reader is expected to have completed a couple of courses in programming using traditional high-level languages at the college-level, and is either a senior or a beginning graduate student in one of the science, technology, engineering or mathematics (STEM) fields. An accompanying website for this book contains additional support for instruction and learning (www.big-data-analytics-book.com) The book is organized into three main parts, comprising a total of twelve chapters. Part I provides an introduction to big data, applications of big data, and big data science and analytics patterns and architectures. A novel data science and analytics application system design methodology is proposed and its realization through use of open-source big data frameworks is described. This methodology describes big data analytics applications as realization of the proposed Alpha, Beta, Gamma and Delta models, that comprise tools and frameworks for collecting and ingesting data from various sources into the big data analytics infrastructure, distributed filesystems and non-relational (NoSQL) databases for data storage, and processing frameworks for batch and real-time analytics. This new methodology forms the pedagogical foundation of this book. Part II introduces the reader to various tools and frameworks for big data analytics, and the architectural and programming aspects of these frameworks, with examples in Python. We describe Publish-Subscribe messaging frameworks (Kafka & Kinesis), Source-Sink connectors (Flume), Database Connectors (Sqoop), Messaging Queues (RabbitMQ, ZeroMQ, RestMQ, Amazon SQS) and custom REST, WebSocket and MQTT-based connectors. The reader is introduced to data storage, batch and real-time analysis, and interactive querying frameworks including HDFS, Hadoop, MapReduce, YARN, Pig, Oozie, Spark, Solr, HBase, Storm, Spark Streaming, Spark SQL, Hive, Amazon Redshift and Google BigQuery. Also described are serving databases (MySQL, Amazon DynamoDB, Cassandra, MongoDB) and the Django Python web framework. Part III introduces the reader to various machine learning algorithms with examples using the Spark MLlib and H2O frameworks, and visualizations using frameworks such as Lightning, Pygal and Seaborn.

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    Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

    Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

    Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking Features

    • O'Reilly Media

    Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

    Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.

    • Understand how data science fits in your organization—and how you can use it for competitive advantage
    • Treat data as a business asset that requires careful investment if you’re to gain real value
    • Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way
    • Learn general concepts for actually extracting knowledge from data
    • Apply data science principles when interviewing data science job candidates

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    Numsense! Data Science for the Layman: No Math Added

    Numsense! Data Science for the Layman: No Math Added

    Numsense! Data Science for the Layman: No Math Added Features

      Used in Stanford's CS102 Big Data (Spring 2017) course.

      Want to get started on data science?
      Our promise: no math added.

      This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations, as well as lots of visuals, all of which are colorblind-friendly.

      Popular concepts covered include:

      • A/B Testing
      • Anomaly Detection
      • Association Rules
      • Clustering
      • Decision Trees and Random Forests
      • Regression Analysis
      • Social Network Analysis
      • Neural Networks

      Features:

      • Intuitive explanations and visuals
      • Real-world applications to illustrate each algorithm
      • Point summaries at the end of each chapter
      • Reference sheets comparing the pros and cons of algorithms
      • Glossary list of commonly-used terms

      With this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions.

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      Data Analytics: Become A Master In Data Analytics

      Data Analytics: Become A Master In Data Analytics

      Data Analytics: Become A Master In Data Analytics Features

        Data Analytics: Become A Master In Data Analytics

        Analyzing data is not easy, due to the fact that you have to figure out which type of data analytics you are going to use, as well as defeat the challenges that you will come up against when it comes to analyzing data.

        With this book, it is our goal to show you the easiest way to work with data analytics and how you are going to avoid some of the challenges and risks that you will be putting yourself up against when you are working with data.

        You will realize that analyzing data is not the easiest thing in the world. However, it is going to get easier the more that you practice. Just guarantee that you are taking the time to practice and do not put too much pressure on yourself.

        In this book, you are going to learn:

        • The risks of data analytics
        • The types of data analytics that are out there in the world
        • What the decision tree is
        • The benefits of using data analytics
        • Real world examples that will show you how you are going to be able to take this knowledge and apply it to your everyday life.

        Data analysis happens no matter what line of work you are in, and it is my hope that with this book, you are able to learn everything that pushes you further in your knowledge of data analysis!

        Get Your Copy Today!

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        Data Smart: Using Data Science to Transform Information into Insight

        Data Smart: Using Data Science to Transform Information into Insight

        Data Smart: Using Data Science to Transform Information into Insight Features

        • John Wiley & Sons
        Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.

        But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.

        Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet.

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        Big Data Analytics with Spark: A Practitioner's Guide to Using Spark for Large Scale Data Analysis

        Big Data Analytics with Spark: A Practitioner's Guide to Using Spark for Large Scale Data Analysis

        Big Data Analytics with Spark: A Practitioner's Guide to Using Spark for Large Scale Data Analysis Features

        • Big Data Analytics with Spark A Practitioner s Guide to Using Spark for Large Scale Data Analysis

        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.

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        Data Analytics: A Practical Guide To Data Analytics For Business, Beginner To Expert(Data Analytics, Prescriptive Analytics, Statistics, Big Data, Intelligence, Master Data, Data Science, Data Mining)

        Data Analytics: A Practical Guide To Data Analytics For Business, Beginner To Expert(Data Analytics, Prescriptive Analytics, Statistics, Big Data, Intelligence, Master Data, Data Science, Data Mining)

        Data Analytics: A Practical Guide To Data Analytics For Business, Beginner To Expert(Data Analytics, Prescriptive Analytics, Statistics, Big Data, Intelligence, Master Data, Data Science, Data Mining) Features

          Understand Data Analytics and Implement it in Your Business Today

          Do you want improve your revenue and stop missing out on profit?

          Do you want to learn about how data analytics in a style and approach that is suitable for you, regardless of your current knowledge?

          This book not only provides step-by-step guide to data analytics, but teaches you actionable steps to improve your analysis in all environments! Are you ready to learn? If so, Data Analytics: A Practical Guide To Data Analytics For Business, Beginner To Expert(Data Analytics, Prescriptive Analytics, Statistics, Big Data, Intelligence, Master Data, Data Science, Data Mining)by James Fahl is THE book for you! It covers the most essential topics you must learn to become a master of Data Analytics.

          What Separates This Book From The Rest?

          What separates this book from the rest? The unique way you will learn with examples and steps. Many books leave you more confused than before you picked them up, not this book, it’s clear concise and implementable. We make it our goal to write this book in plain easy to understand English that anyone can understand. Gone are the days of highly technical language. This allows you to quickly learn topics, and use your new skills immediately. To aid you in learning the topics quickly and effectively this book has been designed to be the ultimate step-by-step guide. Making sure that you’re confident and clear with each topic before moving on!

          You Will Learn The Following:

          • What is Data Analytics?
          • Why use Data Analytics
          • The importance of Data Analytics
          • Types of Data Analytics
          • Explanations of Different models
          • Collecting Data
          • Mistakes to avoid
          Whether you just want to learn more about Data Analysis or already know but want a step-by-step guide to implement it in your life, this is the book for you! So don’t delay it any longer. Take this opportunity and invest in your self by buying this guide now. You will be shocked by how fast you learn about Data Analytics!

          Don’t Delay And Scroll Up To Buy With 1 Click

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          Analytics: Data Science, Data Analysis and Predictive Analytics for Business

          Analytics: Data Science, Data Analysis and Predictive Analytics for Business

          Analytics: Data Science, Data Analysis and Predictive Analytics for Business Features

            SO MANY PEOPLE DREAM OF BECOMING THEIR OWN BOSS OR SUCCEEDING IN THEIR CHOSEN PROFESSION, AND WITH THE RESOURCES AVAILABLE TODAY, MORE ENTREPRENEURS AND PROFESSIONALS ARE ACHIEVING GREAT SUCCESS! HOWEVER, SUCCESS SHOULD BE DEFINED FOR THE LONG TERM, AND AS OPPORTUNITIES START TO GROW, SO DOES THE COMPETITION.

            Getting your business up and running or starting on your career path is one thing, but have a sustainable business or career is completely another. Many people make the mistake of making plans but having no follow-through. This is where analytics comes in.

            Don’t you wish to have the power to know what your target consumers are thinking? Won’t you want to have a preview of what future trends to expect in the market you are in?

            Well, this book is just the one you need. This book will teach you, in simple and easy-to-understand terms, how to take advantage of data from your daily operations and make such data a powerful tool that can influence how well your business does over time. The contents of this book are designed to help you use data to your advantage to enhance business outcomes!

            Here’s what this book will teach you:
            • Why data is your single most powerful tool
            • How to conduct data analysis to enhance your business
            • Which steps to take in performing predictive analysis
            • What techniques you need to employ to achieve sustainable success
            PLUS:
            • Regression techniques
            • Machine learning strategies
            • Risk management tips
            • And much, much, more

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            Data Analytics: Practical Guide to Leveraging the Power of Algorithms, Data Science, Data Mining, Statistics, Big Data, and Predictive Analysis to Improve Business, Work, and Life

            Data Analytics: Practical Guide to Leveraging the Power of Algorithms, Data Science, Data Mining, Statistics, Big Data, and Predictive Analysis to Improve Business, Work, and Life

            Data Analytics: Practical Guide to Leveraging the Power of Algorithms, Data Science, Data Mining, Statistics, Big Data, and Predictive Analysis to Improve Business, Work, and Life Features

              The Ultimate Guide to Data Science and Analytics

              This practical guide is accessible for the reader who is relatively new to the field of data analytics, while still remaining robust and detailed enough to function as a helpful guide to those already experienced in the field. Data science is expanding in breadth and growing rapidly in importance as technology rapidly integrates ever deeper into business and our daily lives. The need for a succinct and informal guide to this important field has never been greater.

              RIGHT NOW you can get ahead of the pack!

              This coherent guide covers everything you need to know on the subject of data science, with numerous concrete examples, and invites the reader to dive further into this exciting field. Students from a variety of academic backgrounds, including computer science, business, engineering, statistics, anyone interested in discovering new ideas and insights derived from data can use this as a textbook. At the same time, professionals such as managers, executives, professors, analysts, doctors, developers, computer scientists, accountants, and others can use this book to make a quantum leap in their knowledge of big data in a matter of only a few hours. Learn how to understand this field and uncover actionable insights from data through analytics.

              UNDERSTAND the following key insights when you grab your copy today:

              • WHY DATA IS IMPORTANT TO YOUR BUSINESS
              • DATA SOURCES
              • HOW DATA CAN IMPROVE YOUR BUSINESS
              • HOW BIG DATA CREATES VALUE
              • DEVELOPMENT OF BIG DATA
              • CONSIDERING THE PROS AND CONS OF BIG DATA
              • BIG DATA FOR SMALL BUSINESSES
              • THE COST EFFECTIVENESS OF DATA ANALYTICS
              • WHAT TO CONSIDER WHEN PREPARING FOR A NEW BIG DATA SOLUTION
              • DATA GATHERING
              • DATA SCRUBBING
              • DESCRIPTIVE ANALYTICS
              • INFERENTIAL STATISTICS
              • PREDICTIVE ANALYTICS
              • PREDICTIVE MODELS
              • DESCRIPTIVE MODELING
              • DECISION MODELING
              • PREDICTIVE ANALYSIS METHODS
              • MACHINE LEARNING TECHNIQUES
              • DATA ANALYSIS WITH "R"
              • ANALYTICAL CUSTOMER RELATIONSHIP MANAGEMENT (CRM)
              • THE USE OF PREDICTIVE ANALYTICS IN HEALTHCARE
              • THE USE OF PREDICTIVE ANALYTICS IN THE FINANCIAL SECTOR
              • PREDICTIVE ANALYTICS & BUSINESS
              • MARKETING STRATEGIES
              • FRAUD DETECTION
              • SHIPPING BUSINESS
              • CONTROLLING RISK FACTORS
              • THE REVOLUTION OF PREDICTIVE ANALYSIS ACROSS A VARIETY OF INDUSTRIES
              • DESCRIPTIVE AND PREDICTIVE ANALYSIS
              • CRUCIAL FACTORS FOR DATA ANALYSIS
              • RESOURCES AND FLEXIBLE TECHNICAL STRUCTURE
              • BUSINESS INTELLIGENCE
              • HYPER TARGETING
              • WHAT IS DATA SCIENCE?
              • DATA MUNGING
              • DEMYSTIFYING DATA SCIENCE
              • SECURITY RISKS TODAY
              • BIG DATA AND IMPACTS ON EVERYDAY LIFE
              • FINANCE AND BIG DATA
              • APPLYING SENTIMENT ANALYSIS
              • RISK EVALUATION AND THE DATA SCIENTIST
              • THE FINANCE INDUSTRY AND REAL-TIME ANALYTICS
              • HOW BIG DATA IS BENEFICIAL TO THE CUSTOMER
              • CUSTOMER SEGMENTATION IS GOOD FOR BUSINESS
              • USE OF BIG DATA BENEFITS IN MARKETING
              • GOOGLE TRENDS
              • THE PROFILE OF A PERFECT CUSTOMER
              • LEAD SCORING IN PREDICTIVE ANALYSIS
              • EVALUATING THE WORTH OF LIFETIME VALUE
              • BIG DATA ADVANTAGES AND DISADVANTAGES
              • MAKING COMPARISONS WITH COMPETITORS
              • DATA SCIENCE IN THE TRAVEL SECTOR
              • SAFETY ENHANCEMENTS THANKS TO BIG DATA
              • BIG DATA AND AGRICULTURE
              • BIG DATA AND LAW ENFORCEMENT
              • THE USE OF BIG DATA IN THE PUBLIC SECTOR
              • BIG DATA AND GAMING
              • PRESCRIPTIVE ANALYTICS
              • GOOGLE’S “SELF-DRIVING CAR”
              • AND MUCH MORE!

              WANT MORE?

              Scroll up and grab this helpful guide toady!

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