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Think Like A Data Scientist

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Think Like a Data Scientist

Think Like a Data Scientist Book
Author : Brian Godsey
Publisher : Simon and Schuster
Release : 2017-03-09
ISBN : 1638355207
Language : En, Es, Fr & De

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Summary Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Data collected from customers, scientific measurements, IoT sensors, and so on is valuable only if you understand it. Data scientists revel in the interesting and rewarding challenge of observing, exploring, analyzing, and interpreting this data. Getting started with data science means more than mastering analytic tools and techniques, however; the real magic happens when you begin to think like a data scientist. This book will get you there. About the Book Think Like a Data Scientist teaches you a step-by-step approach to solving real-world data-centric problems. By breaking down carefully crafted examples, you'll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. As you read, you'll discover (or remember) valuable statistical techniques and explore powerful data science software. More importantly, you'll put this knowledge together using a structured process for data science. When you've finished, you'll have a strong foundation for a lifetime of data science learning and practice. What's Inside The data science process, step-by-step How to anticipate problems Dealing with uncertainty Best practices in software and scientific thinking About the Reader Readers need beginner programming skills and knowledge of basic statistics. About the Author Brian Godsey has worked in software, academia, finance, and defense and has launched several data-centric start-ups. Table of Contents PART 1 - PREPARING AND GATHERING DATA AND KNOWLEDGE Philosophies of data science Setting goals by asking good questions Data all around us: the virtual wilderness Data wrangling: from capture to domestication Data assessment: poking and prodding PART 2 - BUILDING A PRODUCT WITH SOFTWARE AND STATISTICS Developing a plan Statistics and modeling: concepts and foundations Software: statistics in action Supplementary software: bigger, faster, more efficient Plan execution: putting it all together PART 3 - FINISHING OFF THE PRODUCT AND WRAPPING UP Delivering a product After product delivery: problems and revisions Wrapping up: putting the project away

Think Like a Data Scientist

Think Like a Data Scientist Book
Author : Brian Godsey
Publisher : Manning Publications
Release : 2017-02-28
ISBN : 9781633430273
Language : En, Es, Fr & De

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Book Description :

Data science is more than just a set of tools and techniques for extracting knowledge from data sets and data streams. Data science is also a process of getting from goals and questions to real, valuable outcomes by exploring, observing, and manipulating a world of data. Traversing this world can be difficult and confusing. Software developers and non-technical folks may struggle with the uncertainty and fuzzy answers that data invariably provide, and statisticians may have trouble working with any of the multitude of relevant software tools that lie outside of their expertise. Others may not even know where to begin. Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems. This book helps you fill in conceptual knowledge gaps in the daunting fields of statistics and software development, and relates those skills to the real concerns of data science in the business world. As you work though the many practical examples, you'll use your existing knowledge of statistics and programming to solve real problems in data science. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Big Data MBA

Big Data MBA Book
Author : Bill Schmarzo
Publisher : John Wiley & Sons
Release : 2015-12-11
ISBN : 1119238846
Language : En, Es, Fr & De

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Book Description :

Integrate big data into business to drive competitive advantage and sustainable success Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce. Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity. Understand where and how to leverage big data Integrate analytics into everyday operations Structure your organization to drive analytic insights Optimize processes, uncover opportunities, and stand out from the rest Help business stakeholders to “think like a data scientist” Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions.

Becoming a Data Head

Becoming a Data Head Book
Author : Alex J. Gutman,Jordan Goldmeier
Publisher : John Wiley & Sons
Release : 2021-04-13
ISBN : 1119741718
Language : En, Es, Fr & De

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Book Description :

"Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful." Thomas H. Davenport, Research Fellow, Author of Competing on Analytics, Big Data @ Work, and The AI Advantage You’ve heard the hype around data—now get the facts. In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it. You’ll learn how to: Think statistically and understand the role variation plays in your life and decision making Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace Understand what’s really going on with machine learning, text analytics, deep learning, and artificial intelligence Avoid common pitfalls when working with and interpreting data Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head—an active participant in data science, statistics, and machine learning. Whether you’re a business professional, engineer, executive, or aspiring data scientist, this book is for you.

Data Science for Business

Data Science for Business Book
Author : Foster Provost,Tom Fawcett
Publisher : "O'Reilly Media, Inc."
Release : 2013-07-27
ISBN : 144937428X
Language : En, Es, Fr & De

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Book Description :

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

HT THINK LIKE A COMPUTER SCIEN

HT THINK LIKE A COMPUTER SCIEN Book
Author : Jeffrey Elkner,Allen B. Downey,Chris Meyers
Publisher : Samurai Media Limited
Release : 2016-10-04
ISBN : 9789888406784
Language : En, Es, Fr & De

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Book Description :

The goal of this book is to teach you to think like a computer scientist. This way of thinking combines some of the best features of mathematics, engineering, and natural science. Like mathematicians, computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating tradeoffs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions. The single most important skill for a computer scientist is problem solving. Problem solving means the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem-solving skills. That's why this chapter is called, The way of the program. On one level, you will be learning to program, a useful skill by itself. On another level, you will use programming as a means to an end. As we go along, that end will become clearer.

Doing Data Science

Doing Data Science Book
Author : Cathy O'Neil,Rachel Schutt
Publisher : "O'Reilly Media, Inc."
Release : 2013-10-09
ISBN : 144936389X
Language : En, Es, Fr & De

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Book Description :

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.

R for Data Science

R for Data Science Book
Author : Hadley Wickham,Garrett Grolemund
Publisher : "O'Reilly Media, Inc."
Release : 2016-12-12
ISBN : 1491910364
Language : En, Es, Fr & De

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Book Description :

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Making Sense of NoSQL

Making Sense of NoSQL Book
Author : Ann Kelly,Dan McCreary
Publisher : Simon and Schuster
Release : 2013-09-02
ISBN : 1638351422
Language : En, Es, Fr & De

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Book Description :

Summary Making Sense of NoSQL clearly and concisely explains the concepts, features, benefits, potential, and limitations of NoSQL technologies. Using examples and use cases, illustrations, and plain, jargon-free writing, this guide shows how you can effectively assemble a NoSQL solution to replace or augment the traditional RDBMS you have now. About this Book If you want to understand and perhaps start using the new data storage and analysis technologies that go beyond the SQL database model, this book is for you. Written in plain language suitable for technical managers and developers, and using many examples, use cases, and illustrations, this book explains the concepts, features, benefits, potential, and limitations of NoSQL. Making Sense of NoSQL starts by comparing familiar database concepts to the new NoSQL patterns that augment or replace them. Then, you'll explore case studies on big data, search, reliability, and business agility that apply these new patterns to today's business problems. You'll see how NoSQL systems can leverage the resources of modern cloud computing and multiple-CPU data centers. The final chaptersshow you how to choose the right NoSQL technologies for your own needs. Managers and developers will welcome this lucid overview of the potential and capabilities of NoSQL technologies. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. What's Inside NoSQL data architecture patterns NoSQL for big data Search, high availability, and security Choosing an architecture About the Authors Dan McCreary and Ann Kelly lead an independent training and consultancy firm focused on NoSQL solutions and are cofounders of the NoSQL Now! Conference. Table of Contents PART 1 INTRODUCTION NoSQL: It's about making intelligent choices NoSQL concepts PART 2 DATABASE PATTERNS Foundational data architecture patterns NoSQL data architecture patterns Native XML databases PART 3 NOSQL SOLUTIONS Using NoSQL to manage big data Finding information with NoSQL search Building high-availability solutions with NoSQL Increasing agility with NoSQL PART 4 ADVANCED TOPICS NoSQL and functional programming Security: protecting data in your NoSQL systems Selecting the right NoSQL solution

Build a Career in Data Science

Build a Career in Data Science Book
Author : Emily Robinson,Jacqueline Nolis
Publisher : Simon and Schuster
Release : 2020-03-06
ISBN : 1638350159
Language : En, Es, Fr & De

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Book Description :

Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder

The Decision Maker s Handbook to Data Science

The Decision Maker s Handbook to Data Science Book
Author : Stylianos Kampakis
Publisher : Apress
Release : 2019-11-26
ISBN : 1484254945
Language : En, Es, Fr & De

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Book Description :

Data science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. It is easy for novices to the subject to feel paralyzed by intimidating buzzwords, but what many don’t realize is that data science is in fact quite multidisciplinary—useful in the hands of business analysts, communications strategists, designers, and more. With the second edition of The Decision Maker’s Handbook to Data Science, you will learn how to think like a veteran data scientist and approach solutions to business problems in an entirely new way. Author Stylianos Kampakis provides you with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated and revised second edition, includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization. The Decision Maker’s Handbook to Data Science bridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide. What You Will Learn Understand how data science can be used within your business. Recognize the differences between AI, machine learning, and statistics.Become skilled at thinking like a data scientist, without being one.Discover how to hire and manage data scientists.Comprehend how to build the right environment in order to make your organization data-driven. Who This Book Is For Startup founders, product managers, higher level managers, and any other non-technical decision makers who are thinking to implement data science in their organization and hire data scientists. A secondary audience includes people looking for a soft introduction into the subject of data science.

Developing Analytic Talent

Developing Analytic Talent Book
Author : Vincent Granville
Publisher : John Wiley & Sons
Release : 2014-03-24
ISBN : 1118810090
Language : En, Es, Fr & De

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Book Description :

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.

Practical Statistics for Data Scientists

Practical Statistics for Data Scientists Book
Author : Peter Bruce,Andrew Bruce
Publisher : "O'Reilly Media, Inc."
Release : 2017-05-10
ISBN : 1491952911
Language : En, Es, Fr & De

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Book Description :

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data

Data Science from Scratch

Data Science from Scratch Book
Author : Joel Grus
Publisher : "O'Reilly Media, Inc."
Release : 2015-04-14
ISBN : 1491904402
Language : En, Es, Fr & De

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Book Description :

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

Truth Or Truthiness

Truth Or Truthiness Book
Author : Howard Wainer
Publisher : Unknown
Release : 2016
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

"Teacher tenure is a problem. Teacher tenure is a solution. Fracking is safe. Fracking causes earthquakes. Our kids are over-tested. Our kids are not tested enough. We read claims like these in the newspaper, often with no justification other than "it feels right." How can we figure out what is right? Escaping from the clutches of truthiness begins with one question: "What's the evidence?" With his usual verve, and disdain for pious nonsense, Howard Wainer offers a refreshing fact-based view of complex problems in altitude of fields, with special emphasis showing in education how to evaluate the evidence, or lack thereof, supporting various kinds of claims. His primary tool is casual inference: how can we convincingly demonstrate the cause of an effect? This wise book is a must-read for anyone who's ever wanted to challenge the pronouncements of authority figures and a captivating narrative that entertains and educates at the same time. Howard Wainer is a Distinguished Research Scientist at the National Board of Medical Examiners. He has published more than 400 articles and chapters in scholarly journals and books. His book Defeating Deception: Escaping the Shackles of Truthiness by Learning to Think like a Data Scientist, will be published by Cambridge University Press in2016"--

Data Science

Data Science Book
Author : Field Cady
Publisher : John Wiley & Sons
Release : 2020-12-30
ISBN : 1119544084
Language : En, Es, Fr & De

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Book Description :

Tap into the power of data science with this comprehensive resource for non-technical professionals Data Science: The Executive Summary – A Technical Book for Non-Technical Professionals is a comprehensive resource for people in non-engineer roles who want to fully understand data science and analytics concepts. Accomplished data scientist and author Field Cady describes both the “business side” of data science, including what problems it solves and how it fits into an organization, and the technical side, including analytical techniques and key technologies. Data Science: The Executive Summary covers topics like: Assessing whether your organization needs data scientists, and what to look for when hiring them When Big Data is the best approach to use for a project, and when it actually ties analysts’ hands Cutting edge Artificial Intelligence, as well as classical approaches that work better for many problems How many techniques rely on dubious mathematical idealizations, and when you can work around them Perfect for executives who make critical decisions based on data science and analytics, as well as mangers who hire and assess the work of data scientists, Data Science: The Executive Summary also belongs on the bookshelves of salespeople and marketers who need to explain what a data analytics product does. Finally, data scientists themselves will improve their technical work with insights into the goals and constraints of the business situation.

Programming Collective Intelligence

Programming Collective Intelligence Book
Author : Toby Segaran
Publisher : "O'Reilly Media, Inc."
Release : 2007-08-16
ISBN : 0596550685
Language : En, Es, Fr & De

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Book Description :

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect

Library Resources Technical Services

Library Resources   Technical Services Book
Author : Anonim
Publisher : Unknown
Release : 1993
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Library Resources Technical Services book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

The Data Science Design Manual

The Data Science Design Manual Book
Author : Steven S. Skiena
Publisher : Springer
Release : 2017-07-01
ISBN : 3319554441
Language : En, Es, Fr & De

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Book Description :

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 distinct heft 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 world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)

Data Science For Dummies

Data Science For Dummies Book
Author : Lillian Pierson
Publisher : John Wiley & Sons
Release : 2017-03-06
ISBN : 1119327636
Language : En, Es, Fr & De

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Book Description :

Discover how data science can help you gain in-depth insight into your business - the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here’s what to expect: Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate 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.