Doing Data Science
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Doing Data Science
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Author : Cathy O'Neil
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2013-10-09
Doing Data Science written by Cathy O'Neil and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-09 with Computers categories.
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.
Doing Data Science In R
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Author : Mark Andrews
language : en
Publisher: SAGE
Release Date : 2021-03-31
Doing Data Science In R written by Mark Andrews and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-31 with Psychology categories.
This approachable introduction to doing data science in R provides step-by-step advice on using the tools and statistical methods to carry out data analysis. Introducing the fundamentals of data science and R before moving into more advanced topics like Multilevel Models and Probabilistic Modelling with Stan, it builds knowledge and skills gradually. This book: Focuses on providing practical guidance for all aspects, helping readers get to grips with the tools, software, and statistical methods needed to provide the right type and level of analysis their data requires Explores the foundations of data science and breaks down the processes involved, focusing on the link between data science and practical social science skills Introduces R at the outset and includes extensive worked examples and R code every step of the way, ensuring students see the value of R and its connection to methods while providing hands-on practice in the software Provides examples and datasets from different disciplines and locations demonstrate the widespread relevance, possible applications, and impact of data science across the social sciences.
Data Science Using Python And R
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Author : Chantal D. Larose
language : en
Publisher: John Wiley & Sons
Release Date : 2019-04-09
Data Science Using Python And R written by Chantal D. Larose and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-09 with Computers categories.
Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.
Getting Started With Data Science
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Author : Murtaza Haider
language : en
Publisher: IBM Press
Release Date : 2015-12-14
Getting Started With Data Science written by Murtaza Haider and has been published by IBM Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-14 with Business & Economics categories.
Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon.
Data Science From Scratch
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Author : Joel Grus
language : en
Publisher: O'Reilly Media
Release Date : 2019-04-12
Data Science From Scratch written by Joel Grus and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-12 with Computers categories.
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. With this updated second edition, 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.
Practical Data Science Cookbook
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Author : Prabhanjan Tattar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-06-29
Practical Data Science Cookbook written by Prabhanjan Tattar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-29 with Computers categories.
Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Python Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization
A Beginners Guide To Data Science
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Author : Enamul Haque
language : en
Publisher:
Release Date : 2021-03-31
A Beginners Guide To Data Science written by Enamul Haque and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-31 with categories.
Calling all the Aspiring Data Scientists! This book is your "one-stop-shop" to kick start your data science career without knowing how to code! In fact, data science doesn't have to be complicated! With this book, you will grow an understanding of the foundations of data science and its applications. To master this book, you don't need technical abilities. This book is recommended for beginners and anybody who want to understand data science conveniently. You don't need a big textbook to master data science today. A straightforward language has been used to ensure ease of understanding, especially for beginners. Key features include: Introduction to data scienceHistory of data scienceData science life-cycleData science tools and technologiesData science methodologyData science modelsDeveloping data science business strategyManaging data science projectsBecoming a data scientist, data engineers etc.Doing data science without codingBig dataData MiningArtificial intelligenceMachine learningDeep learningNeural networksMathematical analysisStatistical modellingUnderstanding the fundamentals of Python and RDatabase structures and principlesRobotic Process AutomationData science acronyms you need to knowOnline free data science learning resources And a lot more
Doing Data Science
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Author : Rachel Schutt
language : en
Publisher:
Release Date : 2014
Doing Data Science written by Rachel Schutt and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Big data categories.
R For Data Science
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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-12-12
R For Data Science written by Hadley Wickham and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-12 with Computers categories.
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
Data Science
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Author : Herbert Jones
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-11
Data Science written by Herbert Jones and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11 with categories.
Did you know that the value of data usage has increased job opportunities, but that there are few specialists? These days, everyone is aware of the role that data can play, whether it is an election, business or education. But how can you start working in a wide interdisciplinary field that is occupied with so much hype? This book, Data Science: What the Best Data Scientists Know About Data Analytics, Data Mining, Statistics, Machine Learning, and Big Data - That You Don't, presents you with a step-by-step approach to Data Science as well as secrets only known by the best Data Scientists. It combines analytical engineering, Machine Learning, Big Data, Data Mining, and Statistics in an easy to read and digest method. Data gathered from scientific measurements, customers, IoT sensors, and so on is very important only when one can draw meaning from it. Data Scientists are professionals that help disclose interesting and rewarding challenges of exploring, observing, analyzing, and interpreting data. To do that, they apply special techniques that help them discover the meaning of data. Becoming the best Data Scientist is more than just mastering analytic tools and techniques. The real deal lies in the way you apply your creative ability like expert Data Scientists. This book will help you discover that and get you there. The goal with Data Science: What the Best Data Scientists Know About Data Analytics, Data Mining, Statistics, Machine Learning, and Big Data - That You Don't is to help you expand your skills from being a basic Data Scientist to becoming an expert Data Scientist ready to solve real-world data centric issues. At the end of this book, you will learn how to combine Machine Learning, Data Mining, analytics, and programming, and extract real knowledge from data. As you read, you will discover important statistical techniques and algorithms that are helpful in learning Data Science. When you have finished, you will have a strong foundation to help you explore many other fields related to Data Science. This book will discuss the following topics: What Data Science is What it takes to become an expert in Data Science Best Data Mining techniques to apply in data Data visualization Logistic regression Data engineering Machine Learning Big Data Analytics And much more! Don't waste any time. Grab your copy today and learn quick tips from the best Data scientists!