Getting Started with Data Science

Making Sense of Data with Analytics

Author: Murtaza Haider

Publisher: IBM Press

ISBN: 0133991237

Category: Business & Economics

Page: 400

View: 8267

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Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon.

Getting Started with Data Science

Making Sense of Data with Analytics

Author: Murtaza Haider

Publisher: IBM Press

ISBN: 9780133991024

Category: Computers

Page: 250

View: 7608

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Master Data Analytics Hands-On by Solving Fascinating Problems You'll Actually Enjoy! Harvard Business Review recently called data science "The Sexiest Job of the 21st Century." It's not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it's indispensable. Unfortunately, there's been nothing easy about learning data science-until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell's Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything's software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you'll always know why you're doing what you're doing. You'll master data science by answering fascinating questions, such as: * Are religious individuals more or less likely to have extramarital affairs? * Do attractive professors get better teaching evaluations? * Does the higher price of cigarettes deter smoking? * What determines housing prices more: lot size or the number of bedrooms? * How do teenagers and older people differ in the way they use social media? * Who is more likely to use online dating services? * Why do some purchase iPhones and others Blackberry devices? * Does the presence of children influence a family's spending on alcohol? For each problem, you'll walk through defining your question and the answers you'll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon.

Developing Analytic Talent

Becoming a Data Scientist

Author: Vincent Granville

Publisher: John Wiley & Sons

ISBN: 1118810090

Category: Computers

Page: 336

View: 3004

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Learn what it takes to succeed in the the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates.

Learning IBM Watson Analytics

Author: James D Miller

Publisher: Packt Publishing Ltd

ISBN: 1785882805

Category: Computers

Page: 244

View: 448

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Make the most advanced predictive analytical processes easy using Watson Analytics with this easy-to-follow practical guide About This Book This is the first and the only book on IBM Watson Analytics, and it shows you how to leverage Watson in an enterprise environment through rich use cases Incorporate Watson Analytics into your business strategy and confidently add this cutting edge expertise to your resume This book is written by James D Miller, IBM-certified expert and accomplished Director and Sr. Project Leader Who This Book Is For If you want to perform data discovery and analysis and make sense of data you have, this book for you. Data scientists can also use this book to explore a new way to perform data analysis tasks on cloud with ease. This book does not require a programming background. What You Will Learn Study the language of Watson while you discover how easy it is to access and configure Review what a Watson use case is, why it's important, and how to identify one Design Watson Analytical solutions based upon your use cases Understand the basic concepts behind the content analysis cycle and where Watson fits in Explore all the features of Watson, such as Explore, Predict, and Assemble Customize and extend your Watson solutions Use Watson at the Enterprise level Integrate Watson with other toolsets In Detail Today, only a small portion of businesses actually use a real analytical tool as part of routine decision making. IBM Watson Analytics is changing that making the most advanced and predictive analytical techniques understandable and usable for any industry. This book will be the vital tour guide for your trip, starting with what IBM Watson Analytics is. We'll start off with introduction to Watson Analytics and then quickly move on to various use cases under which one can use the different analytics functionalities offered by Watson. During the course of the book, you will learn how to design solutions, and customize and extend Watson analytics. We will conclude by taking Watson Analytics to enterprise and integrating it with other solutions (other IBM solutions and analytics). Now is the time for you to learn IBM Watson to compete in the world. Style and approach Watson provides individuals with the ability to perform sophisticated data discovery and analysis without all of the complexity that usually goes along with it. This book will get you started with Watson analytics and how you can use it in day-to-day data analysis. The book introduces the key concepts and terminology and then uses practical use case examples to reinforce your understanding.

Data Science from Scratch

First Principles with Python

Author: Joel Grus

Publisher: "O'Reilly Media, Inc."

ISBN: 1491904402

Category: BUSINESS & ECONOMICS

Page: 330

View: 7578

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Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

Making Sense of Data I

A Practical Guide to Exploratory Data Analysis and Data Mining

Author: Glenn J. Myatt,Wayne P. Johnson

Publisher: John Wiley & Sons

ISBN: 1118422104

Category: Mathematics

Page: 248

View: 2772

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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 Traceis™ 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.

Mainframe Basics for Security Professionals

Getting Started with RACF

Author: Ori Pomerantz,Barbara Vander Weele,Mark Nelson,Tim Hahn

Publisher: Pearson Education

ISBN: 013270434X

Category: Computers

Page: 192

View: 7124

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Leverage Your Security Expertise in IBM® System z™ Mainframe Environments For over 40 years, the IBM mainframe has been the backbone of the world’s largest enterprises. If you’re coming to the IBM System z mainframe platform from UNIX ® , Linux ® , or Windows ® , you need practical guidance on leveraging its unique security capabilities. Now, IBM experts have written the first authoritative book on mainframe security specifically designed to build on your experience in other environments. Even if you’ve never logged onto a mainframe before, this book will teach you how to run today’s z/OS ® operating system command line and ISPF toolset and use them to efficiently perform every significant security administration task. Don’t have a mainframe available for practice? The book contains step-by-step videos walking you through dozens of key techniques. Simply log in and register your book at www.ibmpressbooks.com/register to gain access to these videos. The authors illuminate the mainframe’s security model and call special attention to z/OS security techniques that differ from UNIX, Linux, and Windows. They thoroughly introduce IBM’s powerful Resource Access Control Facility (RACF) security subsystem and demonstrate how mainframe security integrates into your enterprise-wide IT security infrastructure. If you’re an experienced system administrator or security professional, there’s no faster way to extend your expertise into “big iron” environments. Coverage includes Mainframe basics: logging on, allocating and editing data sets, running JCL jobs, using UNIX System Services, and accessing documentation Creating, modifying, and deleting users and groups Protecting data sets, UNIX file system files, databases, transactions, and other resources Manipulating profiles and managing permissions Configuring the mainframe to log security events, filter them appropriately, and create usable reports Using auditing tools to capture static configuration data and dynamic events, identify weaknesses, and remedy them Creating limited-authority administrators: how, when, and why

Get Bold

Using Social Media to Create a New Type of Social Business

Author: Sandy Carter

Publisher: IBM Press

ISBN: 0132618311

Category: Business & Economics

Page: 267

View: 9819

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A systematic approach to creating and implementing winning social media strategies worldwide - from renowned expert and IBM VP Sandy Carter • •A specific, detailed, patented framework for driving more business value from social media on every continent, in any marketplace. •By an award-winning social media evangelist who has helped make social media work in 60+ countries. •Includes case studies and lessons learned from multiple business segments. •Links with IBM's new, high-profile 'Social Business' initiative. Get Bold offers the most systematic approach to creating a worldwide social media strategy that delivers maximum business value. Drawing on her unsurpassed social media experience in 60 countries, IBM Vice President Sandy Carter details a six-step 'A.G.E.N.D.A.' for integrating social media into any marketing strategy, and building a true 'social business.' Using practical examples and case studies, Carter shows how to: • •Align organizational goals and culture. •Gain friends through social trust. •Engage through experiences. •Network your business processes. •Design for PR recovery. •Analyze your data Get Bold helps you customize social media strategies to reflect the needs and preferences of customers in diverse regions and industries, as well as specific 'lessons learned' about your sectors and markets. Carter doesn't just offer a unique global perspective on social media: she provides 'patterns of success' that help you accelerate strategy formulation for your business. You'll find specific guidance for addressing a wide spectrum of challenges, from reducing cost and increasing revenue to building personalized and consistent interactions across customer touchpoints.

Analytics in a Big Data World

The Essential Guide to Data Science and its Applications

Author: Bart Baesens

Publisher: John Wiley & Sons

ISBN: 1118892747

Category: Business & Economics

Page: 256

View: 9880

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The guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments. The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic. Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics Offers the results of research and the author's personal experience in banking, retail, and government Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.

Performing Information Governance

A Step-by-step Guide to Making Information Governance Work

Author: Anthony David Giordano

Publisher: IBM Press

ISBN: 0133385639

Category: Business & Economics

Page: 672

View: 2298

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Make Information Governance Work : Best Practices, Step-by-Step Tasks, and Detailed Deliverables Most enterprises recognize the crucial importance of effective information governance. However, few are satisfied with the value of their efforts to date. Information governance is difficult because it is a pervasive function, touching multiple processes, systems, and stakeholders. Fortunately, there are best practices that work. Now, a leading expert in the field offers a complete, step-by-step guide to successfully governing information in your organization. Using case studies and hands-on activities, Anthony Giordano fully illuminates the “who, what, how, and when” of information governance. He explains how core governance components link with other enterprise information management disciplines, and provides workable “job descriptions” for each project participant. Giordano helps you successfully integrate key data stewardship processes as you develop large-scale applications and Master Data Management (MDM) environments. Then, once you’ve deployed an information asset, he shows how to consistently get reliable regulatory and financial information from it. Performing Information Governance will be indispensable to CIOs and Chief Data Officers…data quality, metadata, and MDM specialists…anyone responsible for making information governance work. Coverage Includes Recognizing the hidden development and operational implications of information governance—and why it needs to be integrated in the broader organization Integrating information governance activities with transactional processing, BI, MDM, and other enterprise information management functions Establishing the information governance organization: defining roles, launching projects, and integrating with ongoing operations Performing information governance in transactional projects, including those using agile methods and COTS products Bringing stronger information governance to MDM: strategy, architecture, development, and beyond Governing information throughout your BI or Big Data project lifecycle Effectively performing ongoing information governance and data stewardship operational processes Auditing and enforcing data quality management in the context of enterprise information management Maintaining and evolving metadata management for maximum business value

Data Mining and Predictive Analytics

Author: Daniel T. Larose,Chantal D. Larose

Publisher: John Wiley & Sons

ISBN: 1118868676

Category: Computers

Page: 824

View: 8574

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Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics, Second Edition: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant.com, with exclusive password-protected instructor content Data Mining and Predictive Analytics, Second Edition will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

Social Media Analytics

Techniques and Insights for Extracting Business Value Out of Social Media

Author: Matthew Ganis,Avinash Kohirkar

Publisher: IBM Press

ISBN: 0133892948

Category: Business & Economics

Page: 350

View: 6196

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Transform Raw Social Media Data into Real Competitive Advantage There’s real competitive advantage buried in today’s deluge of social media data. If you know how to analyze it, you can increase your relevance to customers, establishing yourself as a trusted supplier in a cutthroat environment where consumers rely more than ever on “public opinion” about your products, services, and experiences. Social Media Analytics is the complete insider’s guide for all executives and marketing analysts who want to answer mission-critical questions and maximize the business value of their social media data. Two leaders of IBM’s pioneering Social Media Analysis Initiative offer thorough and practical coverage of the entire process: identifying the right unstructured data, analyzing it, and interpreting and acting on the knowledge you gain. Their expert guidance, practical tools, and detailed examples will help you learn more from all your social media conversations, and avoid pitfalls that can lead to costly mistakes. You’ll learn how to: Focus on the questions that social media data can realistically answer Determine which information is actually useful to you—and which isn’t Cleanse data to find and remove inaccuracies Create data models that accurately represent your data and lead to more useful answers Use historical data to validate hypotheses faster, so you don’t waste time Identify trends and use them to improve predictions Drive value “on-the-fly” from real-time/ near-real-time and ad hoc analyses Analyze text, a.k.a. “data at rest” Recognize subtle interrelationships that impact business performance Improve the accuracy of your sentiment analyses Determine eminence, and distinguish “talkers” from true influencers Optimize decisions about marketing and advertising spend Whether you’re a marketer, analyst, manager, or technologist, you’ll learn how to use social media data to compete more effectively, respond more rapidly, predict more successfully…grow profits, and keep them growing.

Big Data

Understanding How Data Powers Big Business

Author: Bill Schmarzo

Publisher: John Wiley & Sons

ISBN: 1118740009

Category: Business & Economics

Page: 240

View: 1666

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Leverage big data to add value to your business Social media analytics, web-tracking, and other technologies help companies acquire and handle massive amounts of data to better understand their customers, products, competition, and markets. Armed with the insights from big data, companies can improve customer experience and products, add value, and increase return on investment. The tricky part for busy IT professionals and executives is how to get this done, and that's where this practical book comes in. Big Data: Understanding How Data Powers Big Business is a complete how-to guide to leveraging big data to drive business value. Full of practical techniques, real-world examples, and hands-on exercises, this book explores the technologies involved, as well as how to find areas of the organization that can take full advantage of big data. Shows how to decompose current business strategies in order to link big data initiatives to the organization’s value creation processes Explores different value creation processes and models Explains issues surrounding operationalizing big data, including organizational structures, education challenges, and new big data-related roles Provides methodology worksheets and exercises so readers can apply techniques Includes real-world examples from a variety of organizations leveraging big data Big Data: Understanding How Data Powers Big Business is written by one of Big Data's preeminent experts, William Schmarzo. Don't miss his invaluable insights and advice.

Machine Learning For Dummies

Author: John Paul Mueller,Luca Massaron

Publisher: John Wiley & Sons

ISBN: 111924577X

Category: Computers

Page: 432

View: 1379

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Your no-nonsense guide to making sense of machine learning Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly. Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!

Building Data Science Teams

Author: DJ Patil

Publisher: "O'Reilly Media, Inc."

ISBN: 1449316778

Category: Computers

Page: 24

View: 3052

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As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills, perspectives, tools and processes that position data science teams for success. Topics include: What it means to be "data driven." The unique roles of data scientists. The four essential qualities of data scientists. Patil's first-hand experience building the LinkedIn data science team.

Doing Data Science

Straight Talk from the Frontline

Author: Cathy O'Neil,Rachel Schutt

Publisher: "O'Reilly Media, Inc."

ISBN: 144936389X

Category: Computers

Page: 408

View: 6734

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Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

Think Bigger

Developing a Successful Big Data Strategy for Your Business

Author: Mark Van Rijmenam

Publisher: AMACOM

ISBN: 0814434169

Category: Business & Economics

Page: 288

View: 477

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Big data--the enormous amount of data that is created as virtually every movement, transaction, and choice we make becomes digitized--is revolutionizing business. Offering real-world insight and explanations, this book provides a roadmap for organizations looking to develop a profitable big data strategy...and reveals why it's not something they can leave to the I.T. department. Sharing best practices from companies that have implemented a big data strategy including Walmart, InterContinental Hotel Group, Walt Disney, and Shell, Think Bigger covers the most important big data trends affecting organizations, as well as key technologies like Hadoop and MapReduce, and several crucial types of analyses. In addition, the book offers guidance on how to ensure security, and respect the privacy rights of consumers. It also examines in detail how big data is impacting specific industries--and where opportunities can be found. Big data is changing the way businesses--and even governments--are operated and managed. Think Bigger is an essential resource for anyone who wants to ensure that their company isn't left in the dust.

Predictive Analytics and Data Mining

Concepts and Practice with RapidMiner

Author: Vijay Kotu,Bala Deshpande

Publisher: Morgan Kaufmann

ISBN: 0128016507

Category: Computers

Page: 446

View: 8707

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Put Predictive Analytics into Action Learn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining. You’ll be able to: 1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process. 2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases. 3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.com Demystifies data mining concepts with easy to understand language Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis Explains the process of using open source RapidMiner tools Discusses a simple 5 step process for implementing algorithms that can be used for performing predictive analytics Includes practical use cases and examples

Analytics Across the Enterprise

How IBM Realizes Business Value from Big Data and Analytics

Author: Brenda L. Dietrich,Emily C. Plachy,Maureen F. Norton

Publisher: IBM Press

ISBN: 013383588X

Category: Business & Economics

Page: 224

View: 4547

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How to Transform Your Organization with Analytics: Insider Lessons from IBM’s Pioneering Experience Analytics is not just a technology: It is a better way to do business. Using analytics, you can systematically inform human judgment with data-driven insight. This doesn’t just improve decision-making: It also enables greater innovation and creativity in support of strategy. Your transformation won’t happen overnight; however, it is absolutely achievable, and the rewards are immense. This book demystifies your analytics journey by showing you how IBM has successfully leveraged analytics across the enterprise, worldwide. Three of IBM’s pioneering analytics practitioners share invaluable real-world perspectives on what does and doesn’t work and how you can start or accelerate your own transformation. This book provides an essential framework for becoming a smarter enterprise and shows through 31 case studies how IBM has derived value from analytics throughout its business. Coverage Includes Creating a smarter workforce through big data and analytics More effectively optimizing supply chain processes Systematically improving financial forecasting Managing financial risk, increasing operational efficiency, and creating business value Reaching more B2B or B2C customers and deepening their engagement Optimizing manufacturing and product management processes Deploying your sales organization to increase revenue and effectiveness Achieving new levels of excellence in services delivery and reducing risk Transforming IT to enable wider use of analytics “Measuring the immeasurable” and filling gaps in imperfect data Whatever your industry or role, whether a current or future leader, analytics can make you smarter and more competitive. Analytics Across the Enterprise shows how IBM did it--and how you can, too. Learn more about IBM Analytics