Data Science And Analytics For Ordinary People
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Data Science And Analytics For Ordinary People
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Author : Jeffrey Strickland
language : en
Publisher: Lulu.com
Release Date : 2015-06-28
Data Science And Analytics For Ordinary People written by Jeffrey Strickland and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-28 with Business & Economics categories.
Data Science and Analytics for Ordinary People is a collection of blogs I have written on LinkedIn over the past year. As I continue to perform big data analytics, I continue to discover, not only my weaknesses in communicating the information, but new insights into using the information obtained from analytics and communicating it. These are the kinds of things I blog about and are contained herein. Data science and analytics have been used as synonyms on occasion. In reality data science includes data modeling, data mining, data analysis, database architecture and so on. Analytics is what we do to make sense of the data. That is, we take data and turn it into information for business decision makers. This our course implies that we translate our data science jargon into English.
Data Science Applications Using Python And R
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Author : Jeffrey Strickland
language : en
Publisher:
Release Date : 2020-08-23
Data Science Applications Using Python And R written by Jeffrey Strickland and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-23 with categories.
Data Science Applications using Python and R is the second book in a series that began in 2018. This volume is dedicated to text analytics and natural language processing. Using real data, the author leads the reader through the analysis of Tweet sentiment analysis, banking product-group complaint analysis, presidential debate analysis, and more. The book covers text mining, natural language processing (NLP), vectorizing text data, discrete classifiers, bag-of-words (BOW) models, sentiment analysis, and Latent Dirichlet Allocation (LDA). The book offers complete Python and R code with detail explanations. It is designed for use with Jupyter Notebook and R Studio. It also includes notes on Python and R markdown and features full color graphics and text on heavy paper. All data sets used in the book are downloadable from GitHub. Some data can also be customized and download ed from the Federal Consumer Complaint Data Catalog. Finally, each chapter contains practice exercises.
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 For Business
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Author : Foster Provost
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2013-07-27
Data Science For Business written by Foster Provost 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 2013-07-27 with Business & Economics categories.
Annotation This broad, deep, but not-too-technical guide introduces you to 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. By learning data science principles, you will understand the many data-mining techniques in use today. More importantly, these principles underpin the processes and strategies necessary to solve business problems through data mining techniques.
Data Science
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Author : Field Cady
language : en
Publisher: John Wiley & Sons
Release Date : 2020-12-03
Data Science written by Field Cady 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 2020-12-03 with Business & Economics categories.
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.
Data Science For Dummies
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Author : Lillian Pierson
language : en
Publisher: John Wiley & Sons
Release Date : 2017-03-06
Data Science For Dummies written by Lillian Pierson 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 2017-03-06 with Computers categories.
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.
The Data Science Handbook
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Author : Field Cady
language : en
Publisher: John Wiley & Sons
Release Date : 2017-01-20
The Data Science Handbook written by Field Cady 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 2017-01-20 with Mathematics categories.
A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using PythonTM along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon.
Data Science Applications Using R
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Author : Jeffrey Strickland
language : en
Publisher: Lulu.com
Release Date : 2019-11-13
Data Science Applications Using R written by Jeffrey Strickland and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-13 with Business & Economics categories.
To write a single book about data science, at least as I view the discipline, would result in several volumes. I have come to view Data Science as a multidisciplinary field. People who engage in data science may be statisticians, economists, mathematicians, operations research analysts, and a myriad of other scientific professionals. Most would agree that data scientist have advance degrees in one or more of these disciplines. All practitioners would agree that Data is at center stage. This book is intended to demonstrate the multidisciplinary application of data science, using R-programming with R Studio.
Analytics
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Author : Daniel Covington
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-02-19
Analytics written by Daniel Covington 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 2016-02-19 with categories.
SO MANY PEOPLE DREAM OF BECOMING THEIR OWN BOSS OR SUCCEEDING IN THEIR CHOSEN PROFESSION, AND WITH THE RESOURCES AVAILABLE TODAY, MORE ENTREPRENEURS AND PROFESSIONALS ARE ACHIEVING GREAT SUCCESS! HOWEVER, SUCCESS SHOULD BE DEFINED FOR THE LONG TERM, AND AS OPPORTUNITIES START TO GROW, SO DOES THE COMPETITION. Getting your business up and running or starting on your career path is one thing, but have a sustainable business or career is completely another. Many people make the mistake of making plans but having no follow-through. This is where analytics comes in. Don't you wish to have the power to know what your target consumers are thinking? Won't you want to have a preview of what future trends to expect in the market you are in? Well, this book is just the one you need. This book will teach you, in simple and easy-to-understand terms, how to take advantage of data from your daily operations and make such data a powerful tool that can influence how well your business does over time. The contents of this book are designed to help you use data to your advantage to enhance business outcomes! Here's what this book will teach you: Why data is your single most powerful tool How to conduct data analysis to enhance your business Which steps to take in performing predictive analysis What techniques you need to employ to achieve sustainable success PLUS: Regression techniques Machine learning strategies Risk management tips And much, much, more
Data Science Thinking
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Author : Longbing Cao
language : en
Publisher: Springer
Release Date : 2018-08-17
Data Science Thinking written by Longbing Cao and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-17 with Computers categories.
This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective.