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

Revered by many, reviled by some, technical analysis is the art and science of deciphering price activity to better understand market behavior and identify trading opportunities. In this accessible guide, Jack Schwager-perhaps the most recognized and respected name in the field-demystifies technical analysis for beginning investors, clearly explaining such basics as trends, trading ranges, chart patterns, stops, entry, and exit and pyramiding approaches. The book's numerous examples and clear, simple explanations provide a solid framework for using technical analysis to make better, more informed investment decisions and as the basis for mechanical trading systems. Along with Schwager's invaluable trading rules and market observations culled from years of real-world trading experience, Getting Started in Technical Analysis offers in-depth coverage of:* Types of charts-bar, close-only, point-and-figure, candlestick.* Chart patterns-one-day, continuation, top and bottom formations, the importance of failed signals.* Trading systems-trend-following, counter-trend, pattern recognition.* Charting and analysis software-price data issues, time frame/trading style considerations, software research.* he planned trading approach-trading philosophy, choosing markets, risk control strategies, establishing a trading routine.

Elementary Real Analysis is a core course in nearly all mathematics departments throughout the world. It enables students to develop a deep understanding of the key concepts of calculus from a mature perspective. Elements of Real Analysis is a student-friendly guide to learning all the important ideas of elementary real analysis, based on the author's many years of experience teaching the subject to typical undergraduate mathematics majors. It avoids the compact style of professional mathematics writing, in favor of a style that feels more comfortable to students encountering the subject for the first time. It presents topics in ways that are most easily understood, yet does not sacrifice rigor or coverage. In using this book, students discover that real analysis is completely deducible from the axioms of the real number system. They learn the powerful techniques of limits of sequences as the primary entry to the concepts of analysis, and see the ubiquitous role sequences play in virtually all later topics. They become comfortable with topological ideas, and see how these concepts help unify the subject. Students encounter many interesting examples, including "pathological" ones, that motivate the subject and help fix the concepts. They develop a unified understanding of limits, continuity, differentiability, Riemann integrability, and infinite series of numbers and functions.

By introducing logic and by emphasizing the structure and nature of the arguments used, this book helps readers transition from computationally oriented mathematics to abstract mathematics with its emphasis on proofs. Uses clear expositions and examples, helpful practice problems, numerous drawings, and selected hints/answers. Offers a new boxed review of key terms after each section. Rewrites many exercises. Features more than 250 true/false questions. Includes more than 100 practice problems. Provides exceptionally high-quality drawings to illustrate key ideas. Provides numerous examples and more than 1,000 exercises. A thorough reference for readers who need to increase or brush up on their advanced mathematics skills.

Residents in Boston, Massachusetts are automatically reporting potholes and road hazards via their smartphones. Progressive Insurance tracks real-time customer driving patterns and uses that information to offer rates truly commensurate with individual safety. Google accurately predicts local flu outbreaks based upon thousands of user search queries. Amazon provides remarkably insightful, relevant, and timely product recommendations to its hundreds of millions of customers. Quantcast lets companies target precise audiences and key demographics throughout the Web. NASA runs contests via gamification site TopCoder, awarding prizes to those with the most innovative and cost-effective solutions to its problems. Explorys offers penetrating and previously unknown insights into healthcare behavior.How do these organizations and municipalities do it? Technology is certainly a big part, but in each case the answer lies deeper than that. Individuals at these organizations have realized that they don't have to be Nate Silver to reap massive benefits from today's new and emerging types of data. And each of these organizations has embraced Big Data, allowing them to make astute and otherwise impossible observations, actions, and predictions.It's time to start thinking big.In Too Big to Ignore, recognized technology expert and award-winning author Phil Simon explores an unassailably important trend: Big Data, the massive amounts, new types, and multifaceted sources of information streaming at us faster than ever. Never before have we seen data with the volume, velocity, and variety of today. Big Data is no temporary blip of fad. In fact, it is only going to intensify in the coming years, and its ramifications for the future of business are impossible to overstate.Too Big to Ignore explains why Big Data is a big deal. Simon provides commonsense, jargon-free advice for people and organizations looking to understand and leverage Big Data. Rife with case studies, examples, analysis, and quotes from real-world Big Data practitioners, the book is required reading for chief executives, company owners, industry leaders, and business professionals.

The guide technicians turn to for answers--tuned upto provide an advantage in today's global economyThe face of investing has significantly changed in the 30 years since this book's first publication, but one essential component of the markets has not--human behavior. Whether you're trading cornerstone commodities or innovative investment products, observing how investors responded to past events through technical analysis is your key to forecasting when to buy and sell in the future. This fully updated fifth edition shows you how to maximize your profits in today's complex markets by tailoring your application of this powerful tool.Tens of thousands of individual and professionalinvestors have used the guidance in this book to grow their wealth by understanding, interpreting, and forecasting significant moves in both individual stocks and entire markets. This new edition streamlines itstime-honored, profit-driven approach, while updating every chapter with new examples, tables, charts, and comments that reflect the real-world situations you encounter in everyday trading. Required reading among manyprofessionals, this authoritative resource now features: Brand-new chapters that analyze and explain secular trends with unique technical indicators that measure investor confidence, as well as an introduction to Pring's new Special K indicator Expanded coverage on the profit-making opportunities ETFs create in international markets, sectors, and commodities Practical advice for avoiding false, contratrendsignals that may arise in short-term time spans Additional material on price patterns, candlestick charts, relative strength, momentum, sentiment indicators, and global stock marketsProperly reading and balancing the variety of indicators used in technical analysis is an art, and no other book better illustrates the repeatable steps you need to take to master it.When used with patience and discipline, Technical Analysis Explained, Fifth Edition, will make you a better decision maker and increase your chances of greater profits.

This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data.The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinctheft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well.Additional learning tools:Contains “War Stories,” offering perspectives on how data science applies in the real worldIncludes “Homework Problems,” providing a wide range of exercises and projects for self-studyProvides a complete set of lecture slides and online video lectures at www.data-manual.comProvides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapterRecommends exciting “Kaggle Challenges” from the online platform KaggleHighlights “False Starts,” revealing the subtle reasons why certain approaches failOffers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)

This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis. The book covers a wide range of topics, including ARIMA models, forecasting methods, spectral analysis, linear systems, state-space models, the Kalman filters, nonlinear models, volatility models, and multivariate models.

Most startups fail. But many of those failures are preventable. The Lean Startup is a new approach being adopted across the globe, changing the way companies are built and new products are launched.Eric Ries defines a startup as an organization dedicated to creating something new under conditions of extreme uncertainty. This is just as true for one person in a garage or a group of seasoned professionals in a Fortune 500 boardroom. What they have in common is a mission to penetrate that fog of uncertainty to discover a successful path to a sustainable business.The Lean Startup approach fosters companies that are both more capital efficient and that leverage human creativity more effectively. Inspired by lessons from lean manufacturing, it relies on “validated learning,” rapid scientific experimentation, as well as a number of counter-intuitive practices that shorten product development cycles, measure actual progress without resorting to vanity metrics, and learn what customers really want. It enables a company to shift directions with agility, altering plans inch by inch, minute by minute.Rather than wasting time creating elaborate business plans, The Lean Startup offers entrepreneurs—in companies of all sizes—a way to test their vision continuously, to adapt and adjust before it’s too late. Ries provides a scientific approach to creating and managing successful startups in a age when companies need to innovate more than ever.

Mathematics education in schools has seen a revolution in recent years. Students everywhere expect the subject to be well-motivated, relevant and practical. When such students reach higher education, the traditional development of analysis, often divorced from the calculus they learned at school, seems highly inappropriate. Shouldn't every step in a first course in analysis arise naturally from the student's experience of functions and calculus in school? And shouldn't such a course take every opportunity to endorse and extend the student's basic knowledge of functions? In Yet Another Introduction to Analysis, the author steers a simple and well-motivated path through the central ideas of real analysis. Each concept is introduced only after its need has become clear and after it has already been used informally. Wherever appropriate, new ideas are related to common topics in math curricula and are used to extend the reader's understanding of those topics. In this book the readers are led carefully through every step in such a way that they will soon be predicting the next step for themselves. In this way students will not only understand analysis, but also enjoy it.

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

"Mesmerizing & fascinating..." —The Seattle Post-Intelligencer"The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.comAward-winning | Used by over 30 universities | Translated into 9 languagesAn introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques.Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die.Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections.How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.Predictive analytics(aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more.How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more.A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

Rework shows you a better, faster, easier way to succeed in business.Most business books give you the same old advice: Write a business plan, study the competition, seek investors, yadda yadda. If you're looking for a book like that, put this one back on the shelf.Read it and you'll know why plans are actually harmful, why you don't need outside investors, and why you're better off ignoring the competition. The truth is, you need less than you think. You don't need to be a workaholic. You don't need to staff up. You don't need to waste time on paperwork or meetings. You don't even need an office. Those are all just excuses.What you really need to do is stop talking and start working. This book shows you the way. You'll learn how to be more productive, how to get exposure without breaking the bank, and tons more counterintuitive ideas that will inspire and provoke you.With its straightforward language and easy-is-better approach, Rework is the perfect playbook for anyone who’s ever dreamed of doing it on their own. Hardcore entrepreneurs, small-business owners, people stuck in day jobs they hate, victims of "downsizing," and artists who don’t want to starve anymore will all find valuable guidance in these pages.

Mit dem "Heuser", dem ausführlichen Klassiker unter den Analysis-Lehrbüchern, wurden Generationen von Mathematik-Anfängern mit den Grundlagen der Analysis bekannt gemacht und behutsam in die Denkweise der Mathematik eingeführt. Die praktischen Auswirkungen der Theorie werden an zahlreichen mit Bedacht ausgewählten Beispielen aus den verschiedensten Wissens- und Lebensgebieten demonstriert: aus Physik, Chemie, Biologie, Psychologie, Medizin, Wirtschaftswissenschaft und Technik.

For courses in undergraduate Analysis and Transition to Advanced Mathematics.Analysis with an Introduction to Proof, Fifth Edition helps fill in the groundwork students need to succeed in real analysis―often considered the most difficult course in the undergraduate curriculum. By introducing logic and emphasizing the structure and nature of the arguments used, this text helps students move carefully from computationally oriented courses to abstract mathematics with its emphasis on proofs. Clear expositions and examples, helpful practice problems, numerous drawings, and selected hints/answers make this text readable, student-oriented, and teacher- friendly.

Forecasting is required in many situations. Deciding whether to build another power generation plant in the next five years requires forecasts of future demand. Scheduling staff in a call centre next week requires forecasts of call volumes. Stocking an inventory requires forecasts of stock requirements. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning.This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.In this second edition, all chapters have been updated to cover the latest research and forecasting methods. Three new chapters have been added on dynamic regression forecasting, hierarchical forecasting and practical forecasting issues. The latest version of the book is freely available online at http://OTexts.com/fpp2.

Are you drowning in a sea of data? Would you like to take control of your data and analysis to quickly answer your business questions and make critical decisions? Do you want to confidently present results and solutions to managers, colleagues, clients and the public?If so, you are an Accidental Analyst! Although you didn't plan for a career as a data analyst, you're now in a position where you have to analyze data to be successful. Whether you've been working with data for a few years or are just getting started, you can learn how to analyze your data to find answers to real-world questions. Even if you're an expert, you'll find creative ideas on how to work with accidental analysts. Using illustrated examples, we'll walk you through a clear, step-by-step framework that we call The Seven C's of Data Analysis.Read this book for inspiration, ideas and confidence to begin tackling the problems you face at work. Keep it by your desk as a reference on how to organize, analyze and display your data using best practices of visual analytics. Don't worry, you can continue to use your favorite spreadsheet or data analysis software—this information is not tied to any particular application. Throughout the book, we also include tips, tricks, and shortcuts that took years of analyzing data to discover and understand!This book is valuable for users of Microsoft Excel, Microsoft Access, Business Objects, Cognos, JMP, Microstrategy, Panopticon Software, QlikView, R, SAS, SPSS, Tableau Software or Tibco Spotfire.Please visit us at www.AccidentalAnalyst.com for articles, our free newsletter and upcoming events."This is a wonderful book, filled with practical advice.... a great resource for building analytical prowess."Stephen FewBest-selling author of "Show Me the Numbers" and "Now You See It""Finally, a book that clearly explains the fundamentals of business analytics!"Tim LatendressFinancial Analyst"This book is an amazing resource for regular business people."Diego SaenzPresident, Petplace and former CIO of Pepsi Latin AmericaIn his talk at the 2012 Tableau Conference, Pat Hanrahan, PhD,explained that the book changed how he thinks about analytics andinspired him to develop a new approach to teaching.Professor at Stanford UniversityCo-founder of Tableau and PixarTwo Academy AwardsEileen McDaniel, PhDCo-Founder and Managing Partner of Freakalytics, specializing in educational materials and analytical training with the goal of empowering people to get the most out of their data and take decisive action in their daily work. Her unique experience in science and business inspired her to adapt the scientific method for business, resulting in the Seven C's framework. She also is co-author of Rapid Graphs with Tableau and the Rapid Dashboards Reference Card and App.Stephen McDanielCo-Founder and Principal Data Scientist of Freakalytics. He has over 25 years of experience as a statistician, analyst, data architect, instructor, data miner and software innovator. He has been a faculty member at The Data Warehouse Institute (TDWI) and at The American Marketing Association. He is lead author of SAS for Dummies and has worked with over 100 organizations including Netflix, SAS, Tableau, UC-Berkeley, Duke and the US Navy.

Financial Times Business Book of the Year Finalist“Illuminating and very timely . . . a fascinating — and sometimes alarming — survey of big data’s growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think.”—New York TimesIt seems like “big data” is in the news every day, as we read the latest examples of how powerful algorithms are teasing out the hidden connections between seemingly unrelated things. Whether it is used by the NSA to fight terrorism or by online retailers to predict customers’ buying patterns, big data is a revolution occurring around us, in the process of forever changing economics, science, culture, and the very way we think. But it also poses new threats, from the end of privacy as we know it to the prospect of being penalized for things we haven’t even done yet, based on big data’s ability to predict our future behavior. What we have already seen is just the tip of the iceberg.Big Data is the first major book about this earthshaking subject, with two leading experts explaining what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards.“An optimistic and practical look at the Big Data revolution — just the thing to get your head around the big changes already underway and the bigger changes to come.”—Cory Doctorow, boingboing.com

In this revised and expanded second edition of the bestselling Encyclopedia of Chart Patterns, Thomas Bulkowski updates the classic with new performance statistics for both bull and bear markets and 23 new patterns, including a second section devoted to ten event patterns. Bulkowski tells you how to trade the significant events -- such as quarterly earnings announcements, retail sales, stock upgrades and downgrades -- that shape today?s trading and uses statistics to back up his approach. This comprehensive new edition is a must-have reference if you're a technical investor or trader. Place your order today."The most complete reference to chart patterns available. It goes where no one has gone before. Bulkowski gives hard data on how good and bad the patterns are. A must-read for anyone that's ever looked at a chart and wondered what was happening."-- Larry Williams, trader and author of Long-Term Secrets to Short-Term Trading

Marc Andreesen once said that "markets that don't exist don't care how smart you are." Whether you're a startup founder trying to disrupt an industry, or an intrapreneur trying to provoke change from within, your biggest risk is building something nobody wants.Lean Analytics can help. By measuring and analyzing as you grow, you can validate whether a problem is real, find the right customers, and decide what to build, how to monetize it, and how to spread the word. Focusing on the One Metric That Matters to your business right now gives you the focus you need to move ahead--and the discipline to know when to change course.Written by Alistair Croll (Coradiant, CloudOps, Startupfest) and Ben Yoskovitz (Year One Labs, GoInstant), the book lays out practical, proven steps to take your startup from initial idea to product/market fit and beyond. Packed with over 30 case studies, and based on a year of interviews with over a hundred founders and investors, the book is an invaluable, practical guide for Lean Startup practitioners everywhere.

Business intelligence (BI) used to be so simple - in theory anyway. Integrate and copy data from your transactional systems into a specialized relational database, apply BI reporting and query tools and add business users. Job done.No longer. Analytics, big data and an array of diverse technologies have changed everything. More importantly, business is insisting on ever more value, ever faster from information and from IT in general. An emerging biz-tech ecosystem demands that business and IT work together.Business unIntelligence reflects the new reality that in today's socially complex and rapidly changing world, business decisions must be based on a combination of rational and intuitive thinking. Integrating cues from diverse information sources and tacit knowledge, decision makers create unique meaning to innovate heuristically at the speed of thought. This book provides a wealth of new models that business and IT can use together to design support systems for tomorrow's successful organizations.Dr. Barry Devlin, one of the earliest proponents of data warehousing, goes back to basics to explore how the modern trinity of information, process and people must be reinvented and restructured to deliver the value, insight and innovation required by modern businesses. From here, he develops a series of novel architectural models that provide a new foundation for holistic information use across the entire business. From discovery to analysis and from decision making to action taking, he defines a fully integrated, closed-loop business environment. Covering every aspect of business analytics, big data, collaborative working and more, this book takes over where BI ends to deliver the definitive framework for information use in the coming years.As the person who defined the conceptual framework and physical architecture for data warehousing in the 1980s, Barry Devlin has been an astute observer of the movement he initiated ever since. Now, in Business unintelligence, Devlin provides a sweeping view of the past, present, and future of business intelligence, while delivering new conceptual and physical models for how to turn information into insights and action. Reading Devlin's prose and vision of BI are comparable to reading Carl Sagan's view of the cosmos. The book is truly illuminating and inspiring.--Wayne Eckerson, President, BI Leader ConsultingAuthor, "Secrets of Analytical Leaders: Insights from Information Insiders"

This new edition aims to guide undergraduate students through the first year of their mathematics course. It provides a rigorous introduction to Analysis, which takes into account the difficulties students often face when making the transition from A-level mathematics to this higher level. Plenty of examples are provided, some of which have full, detailed solutions, and others which encourage the student to discover and investigate the ideas themselves. Hints are provided, but the book aims to build confidence and understanding in all topics.This second edition has two new substantial chapters, covering integration and powere series, and is updated throughout, taking into account changes in notation.

Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation.Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it.

Data Analytics: Become A Master In Data AnalyticsAnalyzing 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!

Learn what it takes to succeed in the the most in-demand tech jobHarvard 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 situationsDeveloping Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates.

Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, youâ??ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts.The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what youâ??ve learned, and concludes with exercises, most of which involve writing R code. Write a simple R program, and discover what the language can do Use data types such as vectors, arrays, lists, data frames, and strings Execute code conditionally or repeatedly with branches and loops Apply R add-on packages, and package your own work for others Learn how to clean data you import from a variety of sources Understand data through visualization and summary statistics Use statistical models to pass quantitative judgments about data and make predictions Learn what to do when things go wrong while writing data analysis code

This old edition of Now You See It is no longer in print. Please see the new edition.

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

Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Seventh Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments.