Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization’s massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you’ll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It’s a big, big data world out there – let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

    Learn how to interpret questions and how to plan, structure and write your assignment or report. This free course, Essay and report writing skills, is designed to help you develop the skills you need to write effectively for academic purposes.

    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.""

    Based on intensive study of presentations developed by leading management consulting firms, this step-by-step playbook shows you how to craft stories using proven narrative frameworks, design data-driven slides, and master your verbal and non-verbal delivery.

    Many of us are also teaching, even when it's not in our job descriptions. Whether it's giving a presentation, writing documentation, or creating a website or blog, we need and want to share our knowledge with other people. But if you've ever fallen asleep over a boring textbook, or fast-forwarded through a tedious e-learning exercise, you know that creating a great learning experience is harder than it seems.In Design For How People Learn, you'll discover how to use the key principles behind learning, memory, and attention to create materials that enable your audience to both gain and retain the knowledge and skills you're sharing. Using accessible visual metaphors and concrete methods and examples, Design For How People Learn will teach you how to leverage the fundamental concepts of instructional design both to improve your own learning and to engage your audience.

    Blending theory with practice, the book helps pre-service and in-service teachers develop the knowledge, skills, attitudes, and beliefs they'll need to construct learning environments that make it possible for all students to reach their potential.

    Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    A Problem2. Peter Pigeonhole Prepared A Petition3. What's Your Problem?Part 2: What is The Problem?4. Billy Brighteyes Bests The Bidders5. Billy Bites His Tongue6. Billy Back To The BiddersPart 3: What is The Problem Really?7. The Endless Chain8. Missing The Misfit9. Landing On The Level10. Mind Your MeaningPart 4: Whose Problem Is It?11. Smoke Gets In Your Eyes12. The Campus That Was All Spaced Out13. The Lights At The End Of The TunnelPart 5: Where Does It Come From?14. Janet Jaworski Joggles A Jerk15. Mister Matczyszyn Mends The Matter16. Make-Works And Take-Credits17. Examinations And Other PuzzlesPart 6: Do We Really Want To Solve It?18. Tom Tireless Tinkers With Toys19. Patience Plays Politics20. A Priority Assignment

    Your product or service works beautifully - but something is missing. People just don’t see the big idea - and it’s keeping you from being successful. Your idea has an explanation problem.The Art of Explanation is for business people, educators and influencers who want to improve their explanation skills and start solving explanation problems.Author Lee LeFever is the founder of Common Craft, a company known around the world for making complex ideas easy to understand through short animated videos. He is your guide to helping audiences fall in love with your ideas, products or services through better explanations in any medium. You will learn to:• Plan: Learn explanation basics, what causes them to fail and how to diagnose explanation problems.• Package: Using simple elements, create an explanation strategy that builds confidence and motivates your audience. • Present: Produce remarkable explanations with visuals and media. The Art of Explanation is your invitation to become an explanation specialist and see why explanation is now a fundamental skill for professionals.

    Packed with practical recipes written and tested with Python 3.3, this unique cookbook is for experienced Python programmers who want to focus on modern tools and idioms.Inside, you’ll find complete recipes for more than a dozen topics, covering the core Python language as well as tasks common to a wide variety of application domains. Each recipe contains code samples you can use in your projects right away, along with a discussion about how and why the solution works.Topics include:Data Structures and AlgorithmsStrings and TextNumbers, Dates, and TimesIterators and GeneratorsFiles and I/OData Encoding and ProcessingFunctionsClasses and ObjectsMetaprogrammingModules and PackagesNetwork and Web ProgrammingConcurrencyUtility Scripting and System AdministrationTesting, Debugging, and ExceptionsC Extensions

    Is it?She says she agrees. Does she?The interview went great - or did it?He said he'd never do it again. But he did.Read this book and send your nonverbal intelligence soaring. Joe Navarro, a former FBI counterintelligence officer and a recognized expert on nonverbal behavior, explains how to "speed-read" people: decode sentiments and behaviors, avoid hidden pitfalls, and look for deceptive behaviors. You'll also learn how your body language can influence what your boss, family, friends, and strangers think of you. You will discover:The ancient survival instincts that drive body languageWhy the face is the least likely place to gauge a person's true feelingsWhat thumbs, feet, and eyelids reveal about moods and motivesThe most powerful behaviors that reveal our confidence and true sentimentsSimple nonverbals that instantly establish trustSimple nonverbals that instantly communicate authorityFilled with examples from Navarro's professional experience, this definitive book offers a powerful new way to navigate your world.

    Read it in under an hour, and you’ll be ready to tackle any type of sticky situation in work or life.Meet a stressed Young Manager, whose teams are at each other’s throats. At his local coffee bar he shares his frustrations with his barista – who turns out to be more than he seems. It’s the start of a journey into Pig Wrestling – a process that can be used to resolve any seemingly impossible problem. By reframing the issue we can all create change, whenever and wherever we need it most.Developed out of the authors’ work in elite sports and business – including Manchester City, Olympic champion Jessica Ennis-Hill and the England Cricket team – this instantly memorable story will help you thrive in complex and messy times.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    Humans have become subservient to algorithms. Every day brings a new Moneyball fix--a math whiz who will crack open an industry with clean fact-based analysis rather than human intuition and experience. As a result, we have stopped thinking. Machines do it for us. Christian Madsbjerg argues that our fixation with data often masks stunning deficiencies, and the risks for humankind are enormous. Blind devotion to number crunching imperils our businesses, our educations, our governments, and our life savings. Too many companies have lost touch with the humanity of their customers, while marginalizing workers with liberal arts-based skills. Contrary to popular thinking, Madsbjerg shows how many of today's biggest success stories stem not from "quant" thinking but from deep, nuanced engagement with culture, language, and history. He calls his method sensemaking. In this landmark book, Madsbjerg lays out five principles for how business leaders, entrepreneurs, and individuals can use it to solve their thorniest problems. He profiles companies using sensemaking to connect with new customers, and takes readers inside the work process of sensemaking "connoisseurs" like investor George Soros, architect Bjarke Ingels, and others. Both practical and philosophical, Sensemaking is a powerful rejoinder to corporate groupthink and an indispensable resource for leaders and innovators who want to stand out from the pack.

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data