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Python for Data Science The Ultimate Beginner s Guide to Learn Data Science Analysis and Machine Learning from Scratch with Step by Step Exe

Python for Data Science  The Ultimate Beginner s Guide to Learn Data Science  Analysis  and Machine Learning from Scratch with Step by Step Exe Book
Author : John Russel
Publisher : Python Programming
Release : 2020-09-22
ISBN : 9781913922443
Language : En, Es, Fr & De

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Book Description :

Looking for methods that you can use to make yourself more competitive in your industry? Or are you worried about what your customers may think about your products or services? The truth is...In the modern world, data has become fundamental, and companies are finding new ways to use the insights provided by data to improve their bottom line and customer experience. This is sometimes a challenge. There is so much data that figuring out what steps to take, and what is found in that data is not always as easy as we would like. The good news is that working with data science can help you learn more about your customers and your industry, with the use of a simple coding language, and give you the insights and predictions that you need to see some great improvements with your business. And that's what you'll learn in Python for Data Science. Python for Data Science is going to spend some time looking at all of the neat things that we can do with data science, so you can finally beat out the competition and increase your bottom line, all while helping out the customer. You will learn: Why Data Science is so Important in our Fast-Paced World of Today 5 Practical Applications of Data Science Best Data Science Library that will help us to Get our Work Done with Python A Simple Technique to Set Up our Virtual Environment Proven Strategies to Get High-Quality Data Tips and Tricks to Get your Data Organized Data Analytics and Why It is so Important What Machine Learning is all About and How it Fit into your Data Science Projects Learning how data science works and how to complete your own data analysis in the process is going to be very important to the amount of success that you are going to see. Even if you're completely new to data science or you've done some programming before and are looking to switch to an exciting new career track in Data Science, Python for Data Science will teach you all the practical techniques used by real-world data scientists and analysts to solve problems. Would You Like to Know More? Get this Book Now to Master Python for Data Science!

2019 6th Swiss Conference on Data Science SDS

2019 6th Swiss Conference on Data Science  SDS   Book
Author : Melanie Geiger
Publisher : Unknown
Release : 2019
ISBN : 9781728131054
Language : En, Es, Fr & De

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Book Description :

Download 2019 6th Swiss Conference on Data Science SDS book written by Melanie Geiger, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

A Data Science Approach to Behavioural Change

A Data Science Approach to Behavioural Change Book
Author : Rodrigo Mazorra Blanco
Publisher : Unknown
Release : 2019
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download A Data Science Approach to Behavioural Change book written by Rodrigo Mazorra Blanco, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

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 :

"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"--

Spark for Data Science

Spark for Data Science Book
Author : Srinivas Duvvuri,Bikramaditya Singhal
Publisher : Packt Publishing Ltd
Release : 2016-09-30
ISBN : 1785884778
Language : En, Es, Fr & De

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Book Description :

Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0 About This Book Perform data analysis and build predictive models on huge datasets that leverage Apache Spark Learn to integrate data science algorithms and techniques with the fast and scalable computing features of Spark to address big data challenges Work through practical examples on real-world problems with sample code snippets Who This Book Is For This book is for anyone who wants to leverage Apache Spark for data science and machine learning. If you are a technologist who wants to expand your knowledge to perform data science operations in Spark, or a data scientist who wants to understand how algorithms are implemented in Spark, or a newbie with minimal development experience who wants to learn about Big Data Analytics, this book is for you! What You Will Learn Consolidate, clean, and transform your data acquired from various data sources Perform statistical analysis of data to find hidden insights Explore graphical techniques to see what your data looks like Use machine learning techniques to build predictive models Build scalable data products and solutions Start programming using the RDD, DataFrame and Dataset APIs Become an expert by improving your data analytical skills In Detail This is the era of Big Data. The words ҂ig Data' implies big innovation and enables a competitive advantage for businesses. Apache Spark was designed to perform Big Data analytics at scale, and so Spark is equipped with the necessary algorithms and supports multiple programming languages. Whether you are a technologist, a data scientist, or a beginner to Big Data analytics, this book will provide you with all the skills necessary to perform statistical data analysis, data visualization, predictive modeling, and build scalable data products or solutions using Python, Scala, and R. With ample case studies and real-world examples, Spark for Data Science will help you ensure the successful execution of your data science projects. Style and approach This book takes a step-by-step approach to statistical analysis and machine learning, and is explained in a conversational and easy-to-follow style. Each topic is explained sequentially with a focus on the fundamentals as well as the advanced concepts of algorithms and techniques. Real-world examples with sample code snippets are also included.

DATA SCIENCE

DATA SCIENCE Book
Author : William Vance
Publisher : joiningthedotstv
Release : 2021-05-17
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Discover advanced methods and strategies to learn data science for business. When the concept ‘data science’ was incorporated into some basic business decision processes, it was, at some point, neglected. But with the recent technological advancement, this method of analytics can no longer be neglected in the various decision-making process of a business. Yet, a lot of business owners are unaware of the ubiquity of data opportunities in business The book introduces various methods and strategies that are essential to facilitate your learning path into data science and how it can be used for business decisions and organizational growth. In simple terms, it provides real-world situations that can be used to explain the pervasiveness of data opportunities in business. Hence, as a business owner, you can learn how to participate smartly on your data science project even without the help of a data scientist. You will also discover advanced methods and strategies on how to think analytically while using various data mining strategies to collate data for your analysis. In this book, you will learn how to wrangle, program, explore data sets, model your data, and how to communicate business decisions and findings using data visualization techniques. While this book is a comprehensive guide on various method methods and strategies to learn data science for business, it doesn’t include the general basic knowledge of data science. Hence, the following are some of the things you should expect: · The pervasiveness of data opportunities · The overall process of business decisions and how data science is useful during this process · Various analytical approaches to business · Programming languages · And data visualization Finally, the opportunities that big data provides are vast; let this book help you harness those opportunities. Now is the time to start collating essential information, making rational predictions, and gaining a competitive advantage over other businesses using the vast array of data available online.

Python for Data Science For Dummies

Python for Data Science For Dummies Book
Author : John Paul Mueller,Luca Massaron
Publisher : John Wiley & Sons
Release : 2019-02-27
ISBN : 1119547628
Language : En, Es, Fr & De

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Book Description :

The fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library. Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud. Get started with data science and Python Visualize information Wrangle data Learn from data The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.

Practical Statistics for Data Scientists

Practical Statistics for Data Scientists Book
Author : Peter Bruce,Andrew Bruce,Peter Gedeck
Publisher : O'Reilly Media
Release : 2020-04-10
ISBN : 1492072915
Language : En, Es, Fr & De

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Book Description :

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying 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 or Python programming languages 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

An Introduction to Data Science

An Introduction to Data Science Book
Author : Jeffrey S. Saltz,Jeffrey M. Stanton
Publisher : SAGE Publications
Release : 2017-08-25
ISBN : 1506377513
Language : En, Es, Fr & De

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Book Description :

An Introduction to Data Science by Jeffrey S. Saltz and Jeffrey M. Stanton is an easy-to-read, gentle introduction for people with a wide range of backgrounds into the world of data science. Needing no prior coding experience or a deep understanding of statistics, this book uses the R programming language and RStudio® platform to make data science welcoming and accessible for all learners. After introducing the basics of data science, the book builds on each previous concept to explain R programming from the ground up. Readers will learn essential skills in data science through demonstrations of how to use data to construct models, predict outcomes, and visualize data.

Java for Data Science

Java for Data Science Book
Author : Richard M. Reese,Jennifer L. Reese
Publisher : Packt Publishing Ltd
Release : 2017-01-10
ISBN : 1785281240
Language : En, Es, Fr & De

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Book Description :

Examine the techniques and Java tools supporting the growing field of data science About This Book Your entry ticket to the world of data science with the stability and power of Java Explore, analyse, and visualize your data effectively using easy-to-follow examples Make your Java applications more capable using machine learning Who This Book Is For This book is for Java developers who are comfortable developing applications in Java. Those who now want to enter the world of data science or wish to build intelligent applications will find this book ideal. Aspiring data scientists will also find this book very helpful. What You Will Learn Understand the nature and key concepts used in the field of data science Grasp how data is collected, cleaned, and processed Become comfortable with key data analysis techniques See specialized analysis techniques centered on machine learning Master the effective visualization of your data Work with the Java APIs and techniques used to perform data analysis In Detail Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this book, we cover the important data science concepts and how they are supported by Java, as well as the often statistically challenging techniques, to provide you with an understanding of their purpose and application. The book starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. The next section examines the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. The final chapter illustrates an in-depth data science problem and provides a comprehensive, Java-based solution. Due to the nature of the topic, simple examples of techniques are presented early followed by a more detailed treatment later in the book. This permits a more natural introduction to the techniques and concepts presented in the book. Style and approach This book follows a tutorial approach, providing examples of each of the major concepts covered. With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly.

Soft Computing in Data Science

Soft Computing in Data Science Book
Author : Michael W. Berry,Azlinah Hj. Mohamed,Bee Wah Yap
Publisher : Springer
Release : 2016-09-18
ISBN : 9789811027765
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the International Conference on Soft Computing in Data Science, SCDS 2016, held in Putrajaya, Malaysia, in September 2016. The 27 revised full papers presented were carefully reviewed and selected from 66 submissions. The papers are organized in topical sections on artificial neural networks; classification, clustering, visualization; fuzzy logic; information and sentiment analytics.

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.

Ethics and Data Science

Ethics and Data Science Book
Author : Mike Loukides,Hilary Mason,Dj Patil
Publisher : Unknown
Release : 2018-07-25
ISBN : 1492078220
Language : En, Es, Fr & De

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Book Description :

As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C's) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today.

Data Science Theory and Applications

Data Science  Theory and Applications Book
Author : Anonim
Publisher : Elsevier
Release : 2021-02-15
ISBN : 0323852017
Language : En, Es, Fr & De

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Book Description :

Data Science: Theory and Applications, Volume 44 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of interesting topics, including Modeling extreme climatic events using the generalized extreme value distribution, Bayesian Methods in Data Science, Mathematical Modeling in Health Economic Evaluations, Data Science in Cancer Genomics, Blockchain Technology: Theory and Practice, Statistical outline of animal home ranges, an application of set estimation, Application of Data Handling Techniques to Predict Pavement Performance, Analysis of individual treatment effects for enhanced inferences in medicine, and more. Additional sections cover Nonparametric Data Science: Testing Hypotheses in Large Complex Data, From Urban Mobility Problems to Data Science Solutions, and Data Structures and Artificial Intelligence Methods. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Handbook of Statistics series Updated release includes the latest information on Data Science: Theory and Applications

Roundtable on Data Science Postsecondary Education

Roundtable on Data Science Postsecondary Education Book
Author : National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Division on Engineering and Physical Sciences,Board on Science Education,Computer Science and Telecommunications Board,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics
Publisher : National Academies Press
Release : 2020-10-02
ISBN : 030967770X
Language : En, Es, Fr & De

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Book Description :

Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting.

Modern Data Science with R

Modern Data Science with R Book
Author : Benjamin S. Baumer,Daniel T. Kaplan,Nicholas J. Horton
Publisher : CRC Press
Release : 2017-03-16
ISBN : 1498724582
Language : En, Es, Fr & De

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Book Description :

Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling statistical questions. Contemporary data science requires a tight integration of knowledge from statistics, computer science, mathematics, and a domain of application. This book will help readers with some background in statistics and modest prior experience with coding develop and practice the appropriate skills to tackle complex data science projects. The book features a number of exercises and has a flexible organization conducive to teaching a variety of semester courses.

Data Science For Dummies

Data Science For Dummies Book
Author : Lillian Pierson,Ryan Swanstrom,Carl Anderson
Publisher : John Wiley & Sons
Release : 2015-03-09
ISBN : 1118841557
Language : En, Es, Fr & De

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Book Description :

"Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles in organizations. 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."--Provided by publisher.

Introduction to Biomedical Data Science

Introduction to Biomedical Data Science Book
Author : Robert Hoyt,Robert Muenchen
Publisher : Lulu.com
Release : 2019-11-25
ISBN : 179476173X
Language : En, Es, Fr & De

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Book Description :

Introduction to Biomedical Data Science aims to fill the data science knowledge gap experienced by many clinical, administrative and technical staff. The textbook begins with an overview of what biomedical data science is and then embarks on a tour of topics beginning with spreadsheet tips and tricks and ending with artificial intelligence. In between, important topics are covered such as biostatistics, data visualization, database systems, big data, programming languages, bioinformatics, and machine learning. The textbook is available as a paperback and ebook. Visit the companion website at https: //www.informaticseducation.org for more information. Key features: Real healthcare datasets are used for examples and exercises; Knowledge of a programming language or higher math is not required; Multiple free or open source software programs are presented; YouTube videos are embedded in most chapters; Extensive resources chapter for further reading and learning; PowerPoints and an Instructor Manual

Neural Data Science

Neural Data Science Book
Author : Erik Lee Nylen,Pascal Wallisch
Publisher : Academic Press
Release : 2017-02-24
ISBN : 012804098X
Language : En, Es, Fr & De

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Book Description :

A Primer with MATLAB® and PythonTM present important information on the emergence of the use of Python, a more general purpose option to MATLAB, the preferred computation language for scientific computing and analysis in neuroscience. This book addresses the snake in the room by providing a beginner’s introduction to the principles of computation and data analysis in neuroscience, using both Python and MATLAB, giving readers the ability to transcend platform tribalism and enable coding versatility. Includes discussions of both MATLAB and Python in parallel Introduces the canonical data analysis cascade, standardizing the data analysis flow Presents tactics that strategically, tactically, and algorithmically help improve the organization of code

Applied Data Science

Applied Data Science Book
Author : Martin Braschler,Thilo Stadelmann,Kurt Stockinger
Publisher : Springer
Release : 2019-06-13
ISBN : 3030118215
Language : En, Es, Fr & De

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Book Description :

This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.