The Hands On Guide To Data Interpretation

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The Hands-on Guide to Data Interpretation

Sasha Abraham,Kunal Kulkarni,Rashmi Madhu,Drew Provan
The Hands on Guide to Data Interpretation Book PDF

Author : Sasha Abraham,Kunal Kulkarni,Rashmi Madhu,Drew Provan
Publisher : John Wiley & Sons
Page : 278 pages
File Size : 54,6 Mb
Release : 2011-08-02
Category : Medical
ISBN : 9781444322538

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The Hands-on Guide to Data Interpretation by Sasha Abraham,Kunal Kulkarni,Rashmi Madhu,Drew Provan Pdf

Not sure how to interpret the wealth of data in front of you? Do you lack confidence in applying the results of investigations to your clinical decision making? Then this pocket-sized, quick reference guide to data interpretation may be just right for you. The Hands-on Guide to Data Interpretation is the perfect companion for students, doctors, nurses and other health care professionals who need a reference guide on the ward or when preparing for exams. It focuses on the most common investigations and tests encountered in clinical practice, providing concise summaries of how to confidently interpret investigative findings and, most importantly, how to apply this to clinical decision making. The benefits of this book include: An overview of the normal ranges of test results, followed by a consideration of the differential diagnoses suggested by variance from these values Arranged by system to allow quick access to the key investigations encountered in different specialties A summary 'patient data' chapter to bring the different specialties together, providing an overview to completing investigation documentation and charts Summary table and bullet point format, with a full index, to aid rapid retrieval of information Each chapter reviewed by a specialist to ensure an accurate, practical approach to data interpretation Take the stress out of data interpretation with The Hands-on Guide!

The Hands-on Guide to Data Interpretation

Anonim
The Hands on Guide to Data Interpretation Book PDF

Author : Anonim
Publisher : Unknown
Page : 254 pages
File Size : 44,7 Mb
Release : 2010
Category : Diagnosis, Laboratory
ISBN : 1119548861

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The Hands-on Guide to Data Interpretation by Anonim Pdf

Not sure how to interpret the wealth of data in front of you?Do you lack confidence in applying the results of investigations to your clinical decision making?Then this pocket-sized, quick reference guide to data interpretation may be just right for you. The Hands-on Guide to Data Interpretation is the perfect companion for students, doctors, nurses and other health care professionals who need a reference guide on the ward or when preparing for exams. It focuses on the most common investigations and tests encountered in clinical practice, providing concise summaries of how to confidently interp.

Data Analysis with Open Source Tools

Philipp K. Janert
Data Analysis with Open Source Tools Book PDF

Author : Philipp K. Janert
Publisher : "O'Reilly Media, Inc."
Page : 540 pages
File Size : 45,5 Mb
Release : 2010-11-11
Category : Computers
ISBN : 1449396658

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Data Analysis with Open Source Tools by Philipp K. Janert Pdf

Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications. Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you. Use graphics to describe data with one, two, or dozens of variables Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments Mine data with computationally intensive methods such as simulation and clustering Make your conclusions understandable through reports, dashboards, and other metrics programs Understand financial calculations, including the time-value of money Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations Become familiar with different open source programming environments for data analysis "Finally, a concise reference for understanding how to conquer piles of data."--Austin King, Senior Web Developer, Mozilla "An indispensable text for aspiring data scientists."--Michael E. Driscoll, CEO/Founder, Dataspora

Doing Meta-Analysis with R

Mathias Harrer,Pim Cuijpers,Toshi A. Furukawa,David D. Ebert
Doing Meta Analysis with R Book PDF

Author : Mathias Harrer,Pim Cuijpers,Toshi A. Furukawa,David D. Ebert
Publisher : CRC Press
Page : 500 pages
File Size : 47,9 Mb
Release : 2021-09-15
Category : Mathematics
ISBN : 9781000435634

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Doing Meta-Analysis with R by Mathias Harrer,Pim Cuijpers,Toshi A. Furukawa,David D. Ebert Pdf

Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book

Guide to Intelligent Data Analysis

Michael R. Berthold,Christian Borgelt,Frank Höppner,Frank Klawonn
Guide to Intelligent Data Analysis Book PDF

Author : Michael R. Berthold,Christian Borgelt,Frank Höppner,Frank Klawonn
Publisher : Springer Science & Business Media
Page : 399 pages
File Size : 48,5 Mb
Release : 2010-06-23
Category : Computers
ISBN : 9781848822603

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Guide to Intelligent Data Analysis by Michael R. Berthold,Christian Borgelt,Frank Höppner,Frank Klawonn Pdf

Each passing year bears witness to the development of ever more powerful computers, increasingly fast and cheap storage media, and even higher bandwidth data connections. This makes it easy to believe that we can now – at least in principle – solve any problem we are faced with so long as we only have enough data. Yet this is not the case. Although large databases allow us to retrieve many different single pieces of information and to compute simple aggregations, general patterns and regularities often go undetected. Furthermore, it is exactly these patterns, regularities and trends that are often most valuable. To avoid the danger of “drowning in information, but starving for knowledge” the branch of research known as data analysis has emerged, and a considerable number of methods and software tools have been developed. However, it is not these tools alone but the intelligent application of human intuition in combination with computational power, of sound background knowledge with computer-aided modeling, and of critical reflection with convenient automatic model construction, that results in successful intelligent data analysis projects. Guide to Intelligent Data Analysis provides a hands-on instructional approach to many basic data analysis techniques, and explains how these are used to solve data analysis problems. Topics and features: guides the reader through the process of data analysis, following the interdependent steps of project understanding, data understanding, data preparation, modeling, and deployment and monitoring; equips the reader with the necessary information in order to obtain hands-on experience of the topics under discussion; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; includes numerous examples using R and KNIME, together with appendices introducing the open source software; integrates illustrations and case-study-style examples to support pedagogical exposition. This practical and systematic textbook/reference for graduate and advanced undergraduate students is also essential reading for all professionals who face data analysis problems. Moreover, it is a book to be used following one’s exploration of it. Dr. Michael R. Berthold is Nycomed-Professor of Bioinformatics and Information Mining at the University of Konstanz, Germany. Dr. Christian Borgelt is Principal Researcher at the Intelligent Data Analysis and Graphical Models Research Unit of the European Centre for Soft Computing, Spain. Dr. Frank Höppner is Professor of Information Systems at Ostfalia University of Applied Sciences, Germany. Dr. Frank Klawonn is a Professor in the Department of Computer Science and Head of the Data Analysis and Pattern Recognition Laboratory at Ostfalia University of Applied Sciences, Germany. He is also Head of the Bioinformatics and Statistics group at the Helmholtz Centre for Infection Research, Braunschweig, Germany.

Making Sense of Data I

Glenn J. Myatt,Wayne P. Johnson
Making Sense of Data I Book PDF

Author : Glenn J. Myatt,Wayne P. Johnson
Publisher : John Wiley & Sons
Page : 320 pages
File Size : 52,6 Mb
Release : 2014-07-02
Category : Mathematics
ISBN : 9781118422106

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Making Sense of Data I by Glenn J. Myatt,Wayne P. Johnson Pdf

Praise for the First Edition “...a well-written book on data analysis and data mining that provides an excellent foundation...” —CHOICE “This is a must-read book for learning practical statistics and data analysis...” —Computing Reviews.com A proven go-to guide for data analysis, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects. Based on the authors’ practical experience in implementing data analysis and data mining, the new edition provides clear explanations that guide readers from almost every field of study. In order to facilitate the needed steps when handling a data analysis or data mining project, a step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The tools to summarize and interpret data in order to master data analysis are integrated throughout, and the Second Edition also features: Updated exercises for both manual and computer-aided implementation with accompanying worked examples New appendices with coverage on the freely available TraceisTM software, including tutorials using data from a variety of disciplines such as the social sciences, engineering, and finance New topical coverage on multiple linear regression and logistic regression to provide a range of widely used and transparent approaches Additional real-world examples of data preparation to establish a practical background for making decisions from data Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition is an excellent reference for researchers and professionals who need to achieve effective decision making from data. The Second Edition is also an ideal textbook for undergraduate and graduate-level courses in data analysis and data mining and is appropriate for cross-disciplinary courses found within computer science and engineering departments.

Practical Data Analysis

Hector Cuesta,Dr. Sampath Kumar
Practical Data Analysis Book PDF

Author : Hector Cuesta,Dr. Sampath Kumar
Publisher : Packt Publishing Ltd
Page : 338 pages
File Size : 52,5 Mb
Release : 2016-09-30
Category : Computers
ISBN : 9781785286667

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Practical Data Analysis by Hector Cuesta,Dr. Sampath Kumar Pdf

A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn Acquire, format, and visualize your data Build an image-similarity search engine Generate meaningful visualizations anyone can understand Get started with analyzing social network graphs Find out how to implement sentiment text analysis Install data analysis tools such as Pandas, MongoDB, and Apache Spark Get to grips with Apache Spark Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.

Medical Statistics

Jennifer Peat,Belinda Barton
Medical Statistics Book PDF

Author : Jennifer Peat,Belinda Barton
Publisher : John Wiley & Sons
Page : 336 pages
File Size : 52,5 Mb
Release : 2008-04-15
Category : Medical
ISBN : 9780470755204

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Medical Statistics by Jennifer Peat,Belinda Barton Pdf

Holistic approach to understanding medical statistics This hands-on guide is much more than a basic medical statistics introduction. It equips you with the statistical tools required for evidence-based clinical research. Each chapter provides a clear step-by-step guide to each statistical test with practical instructions on how to generate and interpret the numbers, and present the results as scientific tables or graphs. Showing you how to: analyse data with the help of data set examples (Click here to download datasets) select the correct statistics and report results for publication or presentation understand and critically appraise results reported in the literature Each statistical test is linked to the research question and the type of study design used. There are also checklists for critically appraising the literature and web links to useful internet sites. Clear and concise explanations, combined with plenty of examples and tabulated explanations are based on the authors’ popular medical statistics courses. Critical appraisal guidelines at the end of each chapter help the reader evaluate the statistical data in their particular contexts.

The SPSS Guide to the New Statistical Analysis of Data

Susan B. Gerber,Kristin E. Voelkl
The SPSS Guide to the New Statistical Analysis of Data Book PDF

Author : Susan B. Gerber,Kristin E. Voelkl
Publisher : Springer Science & Business Media
Page : 202 pages
File Size : 52,9 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461222620

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The SPSS Guide to the New Statistical Analysis of Data by Susan B. Gerber,Kristin E. Voelkl Pdf

This companion to The New Statistical Analysis of Data by Anderson and Finn provides a hands-on guide to data analysis using SPSS. Included with this guide are instructions for obtaining the data sets to be analysed via the World Wide Web. First, the authors provide a brief review of using SPSS, and then, corresponding to the organisation of The New Statistical Analysis of Data, readers participate in analysing many of the data sets discussed in the book. In so doing, students both learn how to conduct reasonably sophisticated statistical analyses using SPSS whilst at the same time gaining an insight into the nature and purpose of statistical investigation.

Head First Data Analysis

Michael Milton
Head First Data Analysis Book PDF

Author : Michael Milton
Publisher : "O'Reilly Media, Inc."
Page : 486 pages
File Size : 42,6 Mb
Release : 2009-07-24
Category : Business & Economics
ISBN : 9780596153939

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Head First Data Analysis by Michael Milton Pdf

A guide for data managers and analyzers shares guidelines for identifying patterns, predicting future outcomes, and presenting findings to others; drawing on current research in cognitive science and learning theory while covering such additional topics as assessing data quality, handling ambiguous information, and organizing data within market groups. Original.

Making Sense of Data II

Glenn J. Myatt,Wayne P. Johnson
Making Sense of Data II Book PDF

Author : Glenn J. Myatt,Wayne P. Johnson
Publisher : John Wiley & Sons
Page : 307 pages
File Size : 40,9 Mb
Release : 2009-03-04
Category : Mathematics
ISBN : 9780470417393

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Making Sense of Data II by Glenn J. Myatt,Wayne P. Johnson Pdf

A hands-on guide to making valuable decisions from data using advanced data mining methods and techniques This second installment in the Making Sense of Data series continues to explore a diverse range of commonly used approaches to making and communicating decisions from data. Delving into more technical topics, this book equips readers with advanced data mining methods that are needed to successfully translate raw data into smart decisions across various fields of research including business, engineering, finance, and the social sciences. Following a comprehensive introduction that details how to define a problem, perform an analysis, and deploy the results, Making Sense of Data II addresses the following key techniques for advanced data analysis: Data Visualization reviews principles and methods for understanding and communicating data through the use of visualization including single variables, the relationship between two or more variables, groupings in data, and dynamic approaches to interacting with data through graphical user interfaces. Clustering outlines common approaches to clustering data sets and provides detailed explanations of methods for determining the distance between observations and procedures for clustering observations. Agglomerative hierarchical clustering, partitioned-based clustering, and fuzzy clustering are also discussed. Predictive Analytics presents a discussion on how to build and assess models, along with a series of predictive analytics that can be used in a variety of situations including principal component analysis, multiple linear regression, discriminate analysis, logistic regression, and Naïve Bayes. Applications demonstrates the current uses of data mining across a wide range of industries and features case studies that illustrate the related applications in real-world scenarios. Each method is discussed within the context of a data mining process including defining the problem and deploying the results, and readers are provided with guidance on when and how each method should be used. The related Web site for the series (www.makingsenseofdata.com) provides a hands-on data analysis and data mining experience. Readers wishing to gain more practical experience will benefit from the tutorial section of the book in conjunction with the TraceisTM software, which is freely available online. With its comprehensive collection of advanced data mining methods coupled with tutorials for applications in a range of fields, Making Sense of Data II is an indispensable book for courses on data analysis and data mining at the upper-undergraduate and graduate levels. It also serves as a valuable reference for researchers and professionals who are interested in learning how to accomplish effective decision making from data and understanding if data analysis and data mining methods could help their organization.

100 Cases for Medical Data Interpretation

David Howlett,Nicola Gainsborough
100 Cases for Medical Data Interpretation Book PDF

Author : David Howlett,Nicola Gainsborough
Publisher : CRC Press
Page : 496 pages
File Size : 47,9 Mb
Release : 2013-01-22
Category : Medical
ISBN : 9781444149043

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100 Cases for Medical Data Interpretation by David Howlett,Nicola Gainsborough Pdf

Data interpretation questions based on clinical cases are a popular means of testing medical students both during undergraduate studies and as an element of finals examinations. Written by a small team of authors with extensive teaching experience, 100 Cases in Medical Data Interpretation provides invaluable guidance from lecturers who understand from personal experience that detailed and accurate explanations are the key to successful revision. This book presents 100 cases arranged by specialty area—radiology, clinical chemistry, haematology and cardiology—as well as a random section of miscellaneous cases. Questions accompanying each case prompt the reader to consider how the data presented might be correctly understood. A clear discussion of how the correct answer was reached, with boxed highlights and bullet lists of key points, makes this book an excellent learning aid during all stages of clinical studies, and particularly while preparing for medical finals.

Making Sense of Data

Glenn J. Myatt
Making Sense of Data Book PDF

Author : Glenn J. Myatt
Publisher : John Wiley & Sons
Page : 294 pages
File Size : 51,6 Mb
Release : 2007-02-26
Category : Mathematics
ISBN : 9780470101018

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Making Sense of Data by Glenn J. Myatt Pdf

A practical, step-by-step approach to making sense out of data Making Sense of Data educates readers on the steps and issues that need to be considered in order to successfully complete a data analysis or data mining project. The author provides clear explanations that guide the reader to make timely and accurate decisions from data in almost every field of study. A step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. With a comprehensive collection of methods from both data analysis and data mining disciplines, this book successfully describes the issues that need to be considered, the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision making from data. Readers are given a solid foundation in the procedures associated with complex data analysis or data mining projects and are provided with concrete discussions of the most universal tasks and technical solutions related to the analysis of data, including: * Problem definitions * Data preparation * Data visualization * Data mining * Statistics * Grouping methods * Predictive modeling * Deployment issues and applications Throughout the book, the author examines why these multiple approaches are needed and how these methods will solve different problems. Processes, along with methods, are carefully and meticulously outlined for use in any data analysis or data mining project. From summarizing and interpreting data, to identifying non-trivial facts, patterns, and relationships in the data, to making predictions from the data, Making Sense of Data addresses the many issues that need to be considered as well as the steps that need to be taken to master data analysis and mining.

Guide to Intelligent Data Science

Michael R. Berthold,Christian Borgelt,Frank Höppner,Frank Klawonn,Rosaria Silipo
Guide to Intelligent Data Science Book PDF

Author : Michael R. Berthold,Christian Borgelt,Frank Höppner,Frank Klawonn,Rosaria Silipo
Publisher : Springer Nature
Page : 427 pages
File Size : 49,5 Mb
Release : 2020-08-06
Category : Computers
ISBN : 9783030455743

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Guide to Intelligent Data Science by Michael R. Berthold,Christian Borgelt,Frank Höppner,Frank Klawonn,Rosaria Silipo Pdf

Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results. Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website. This practical and systematic textbook/reference is a “need-to-have” tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a “need to use, need to keep” resource following one's exploration of the subject.

Practical Data Analysis - Second Edition

Hector Cuesta,Sampath Kumar
Practical Data Analysis Second Edition Book PDF

Author : Hector Cuesta,Sampath Kumar
Publisher : Unknown
Page : 338 pages
File Size : 52,9 Mb
Release : 2016-09-30
Category : Electronic
ISBN : 1785289713

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Practical Data Analysis - Second Edition by Hector Cuesta,Sampath Kumar Pdf

A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache SparkAbout This Book- Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data- Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images- A hands-on guide to understanding the nature of data and how to turn it into insightWho This Book Is ForThis book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed.What You Will Learn- Acquire, format, and visualize your data- Build an image-similarity search engine- Generate meaningful visualizations anyone can understand- Get started with analyzing social network graphs- Find out how to implement sentiment text analysis- Install data analysis tools such as Pandas, MongoDB, and Apache Spark- Get to grips with Apache Spark- Implement machine learning algorithms such as classification or forecastingIn DetailBeyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service.This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark.Style and approachThis is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.