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

    But what if you could have your computer do them for you?In "Automate the Boring Stuff with Python," you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand no prior programming experience required. Once you've mastered the basics of programming, you'll create Python programs that effortlessly perform useful and impressive feats of automation to: Search for text in a file or across multiple filesCreate, update, move, and rename files and foldersSearch the Web and download online contentUpdate and format data in Excel spreadsheets of any sizeSplit, merge, watermark, and encrypt PDFsSend reminder emails and text notificationsFill out online formsStep-by-step instructions walk you through each program, and practice projects at the end of each chapter challenge you to improve those programs and use your newfound skills to automate similar tasks.Don't spend your time doing work a well-trained monkey could do. Even if you've never written a line of code, you can make your computer do the grunt work. Learn how in "Automate the Boring Stuff with Python.""

    The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process.Create vectors, handle variables, and perform other basic functionsInput and output dataTackle data structures such as matrices, lists, factors, and data framesWork with probability, probability distributions, and random variablesCalculate statistics and confidence intervals, and perform statistical testsCreate a variety of graphic displaysBuild statistical models with linear regressions and analysis of variance (ANOVA)Explore advanced statistical techniques, such as finding clusters in your dataWonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language--one practical example at a time.--Jeffrey Ryan, software consultant and R package author

    Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include:Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collectionBayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

    This report provides a brief yet comprehensive introduction to Python for engineers, researchers, and data scientists who are already familiar with another programming language.Author Jake VanderPlas, an interdisciplinary research director at the University of Washington, explains Python’s essential syntax and semantics, built-in data types and structures, function definitions, control flow statements, and more, using Python 3 syntax.You’ll explore:- Python syntax basics and running Python codeBasic semantics of Python variables, objects, and operators- Built-in simple types and data structures- Control flow statements for executing code blocks conditionally- Methods for creating and using reusable functionsIterators, list comprehensions, and generators- String manipulation and regular expressions- Python’s standard library and third-party modules- Python’s core data science tools- Recommended resources to help you learn more

    With this book, you'll learn how to load data, assemble and disassemble data objects, navigate R's environment system, write your own functions, and use all of R's programming tools.RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You'll gain valuable programming skills and support your work as a data scientist at the same time.Work hands-on with three practical data analysis projects based on casino gamesStore, retrieve, and change data values in your computer's memoryWrite programs and simulations that outperform those written by typical R usersUse R programming tools such as if else statements, for loops, and S3 classesLearn how to write lightning-fast vectorized R codeTake advantage of R's package system and debugging toolsPractice and apply R programming concepts as you learn them

    We still remember the first time we turned on our Kindle Fire! Although it may feel like everybody else already knows how to use their Kindle Fire the truth is they don't. And if this is the first time you've held one in your hands, then we would just like to say..... WE'RE HERE TO HELP!From the Number #1 Amazon Bestselling Authors in Computers and Technology.When we first published our Kindle Fire app review guide - 250+ Best Kindle Fire HD Apps for the New Kindle Fire Owner - we added in a small bonus section which included a few tips and tricks for the new Kindle Fire user. We soon started receiving emails from customers asking for more of the same.Well this easy-to-use guide is our way of welcoming all you new Kindle Fire owners who have just pulled your device from its box for the first time. This short user guide has been written to introduce you to some of the most basic, but essential, elements of using your new handheld computer. This is information that you need to know.This Amazon Kindle e-Book Includes:Navigating your way around the Kindle Fire HDGetting rid of adsDownloading and transferring contentConnecting to social mediaSetting up emailLearning to 'swype'Getting to grips with parental controls

    Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you're ready to get started.Use R's default graphics for quick exploration of dataCreate a variety of bar graphs, line graphs, and scatter plotsSummarize data distributions with histograms, density curves, box plots, and other examplesProvide annotations to help viewers interpret dataControl the overall appearance of graphicsRender data groups alongside each other for easy comparisonUse colors in plotsCreate network graphs, heat maps, and 3D scatter plotsStructure data for graphing

    In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they’ve recovered from nasty data problems.From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line? Bad data is data that gets in the way. This book explains effective ways to get around it.Among the many topics covered, you’ll discover how to:Test drive your data to see if it’s ready for analysisWork spreadsheet data into a usable formHandle encoding problems that lurk in text dataDevelop a successful web-scraping effortUse NLP tools to reveal the real sentiment of online reviewsAddress cloud computing issues that can impact your analysis effortAvoid policies that create data analysis roadblocksTake a systematic approach to data quality analysis

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    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.

    You’ll learn how to combine social web data, analysis techniques, and visualization to find what you’ve been looking for in the social haystack—as well as useful information you didn’t know existed.Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools.Get a straightforward synopsis of the social web landscapeUse adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, LinkedIn, and Google+Learn how to employ easy-to-use Python tools to slice and dice the data you collectExplore social connections in microformats with the XHTML Friends NetworkApply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detectionBuild interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits"A rich, compact, useful, practical introduction to a galaxy of tools, techniques, and theories for exploring structured and unstructured data." --Alex Martelli, Senior Staff Engineer, Google

    A real life case study with sample project documents and diagrams is used to more practically explain these international tools, techniques, and lean principles to a broad range of practitioners, including: - Business analysts, systems analysts, developers and project managers - Entrepreneurs, product owners and product managers - Consultants, UX designers and marketing specialists - C-suite executives, investors and managers of companies of all sizes.

    From shopping for a first electric guitar to setting customized action, this do-it-yourself player's primer for owning and maintaining an electric guitar explains the ins and outs of: choosing the right guitar; cleaning, tools and basic maintenance; personalizing and improving on a "factory setup"; trouble-shooting; basic guitar electronics; setups of the pros; and much more. Written by Dan Erlewine, author of the bestselling Guitar Player Repair Guide and the popular "Repairs" column in Guitar Player magazine, this book also includes a plastic sheet with specialized guitar tools you can snap-out!

    Fundamentals of Biostatistics with CD-Rom.