The 7 best data science big analytics
Finding your suitable data science big analytics is not easy. You may need consider between hundred or thousand products from many store. In this article, we make a short list of the best data science big analytics including detail information and customer reviews. Let’s find out which is your favorite one.
Best data science big analytics
1. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data
Feature
WileyDescription
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!
2. Big Data Science & Analytics: A Hands-On Approach
Description
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.3. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
Feature
O'Reilly MediaDescription
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. Youll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your companys data science projects. Youll 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 organizationand how you can use it for competitive advantage
- Treat data as a business asset that requires careful investment if youre 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
4. Analytics in a Big Data World: The Essential Guide to Data Science and its Applications (Wiley and SAS Business Series)
Description
The guide to targeting and leveraging business opportunitiesusing big data & analyticsBy leveraging big data & analytics, businesses create thepotential to better understand, manage, and strategicallyexploiting the complex dynamics of customer behavior. Analyticsin a Big Data World reveals how to tap into the powerful toolof data analytics to create a strategic advantage and identify newbusiness opportunities. Designed to be an accessible resource, thisessential book does not include exhaustive coverage of allanalytical techniques, instead focusing on analytics techniquesthat really provide added value in business environments.
The book draws on author Bart Baesens' expertise on the topicsof big data, analytics and its applications in e.g. credit risk,marketing, and fraud to provide a clear roadmap for organizationsthat want to use data analytics to their advantage, but need a goodstarting point. Baesens has conducted extensive research on bigdata, analytics, customer relationship management, web analytics,fraud detection, and credit risk management, and uses thisexperience to bring clarity to a complex topic.
- Includes numerous case studies on risk management, frauddetection, customer relationship management, and web analytics
- Offers the results of research and the author's personalexperience in banking, retail, and government
- Contains an overview of the visionary ideas and currentdevelopments on the strategic use of analytics for business
- Covers the topic of data analytics in easy-to-understand termswithout an undo emphasis on mathematics and the minutiae ofstatistical analysis
For organizations looking to enhance their capabilities via dataanalytics, this resource is the go-to reference for leveraging datato enhance business capabilities.
5. Data Smart: Using Data Science to Transform Information into Insight
Feature
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.
Description
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 inData Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet.
6. Data Analytics: 3 Books in 1 - The Concise Guide For Understanding & Using Data Analytics, Data Science & Big Data
Description
Data Analytics: 3 Books in 1 - The Concise Guide For Understanding & Using Data Analytics, Data Science & Big Data Data analytics is used in the real world in a lot of jobs that you may be looking to get yourself into. In order to get yourself into data analytics and get hired into a job that will pay you more money, you are going to want to ensure that you have everything that you need to know under your belt so that you are placing yourself one step ahead of the competition and get yourself hired! In this Book set you will find all the information you need to get ahead and understand, Data Analytics, Data Science and Big Data. Here Is A Preview Of What You'll Learn In Book 1: Data Analytics For Beginners: The Ultimate Beginners Guide to Understanding Data Science and Using Data Analytics What is Data Analytics Basics of Data Analytics to Business Statistical Thinking Big Data Defined Challenges of Data Analytics In Book 2: Data Science For Business: The Complete Guide To Using Data Analytics and Data Mining in Business Methodologies of Data Analytics Importance of Data Data Science and Data Analytics Data Gathering and Mining Data Analytics in Business and Industry In Book 3: Data Analytics: Analytical Guide For Science and Big Data Myths Surrounding Data Analytics The Important Principles Analytics Delivery Process Big Data Governance Data Mining Best Practices And Much Much More.. Get Your Copy Right Now!7. Big Data: Principles and best practices of scalable realtime data systems
Feature
Manning PublicationsDescription
Summary
Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Book
Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive.
Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases.
This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful.
What's Inside
- Introduction to big data systems
- Real-time processing of web-scale data
- Tools like Hadoop, Cassandra, and Storm
- Extensions to traditional database skills
About the Authors
Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing.
Table of Contents
- A new paradigm for Big Data
- Data model for Big Data
- Data model for Big Data: Illustration
- Data storage on the batch layer
- Data storage on the batch layer: Illustration
- Batch layer
- Batch layer: Illustration
- An example batch layer: Architecture and algorithms
- An example batch layer: Implementation
- Serving layer
- Serving layer: Illustration
- Realtime views
- Realtime views: Illustration
- Queuing and stream processing
- Queuing and stream processing: Illustration
- Micro-batch stream processing
- Micro-batch stream processing: Illustration
- Lambda Architecture in depth
Recent Comments