Practical Data Science With R Second Edition

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Practical Data Science with R

Author : Nina Zumel
Publisher : Manning Publications
ISBN 13 : 9781617291562
Total Pages : 416 pages
Book Rating : 4.2/5 (915 download)

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Book Synopsis Practical Data Science with R by : Nina Zumel

Download or read book Practical Data Science with R written by Nina Zumel and published by Manning Publications. This book was released on 2014-04-10 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics. Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels. This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed. What's Inside Data science for the business professional Statistical analysis using the R language Project lifecycle, from planning to delivery Numerous instantly familiar use cases Keys to effective data presentations About the Authors Nina Zumel and John Mount are cofounders of a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com. Table of Contents PART 1 INTRODUCTION TO DATA SCIENCE The data science process Loading data into R Exploring data Managing data PART 2 MODELING METHODS Choosing and evaluating models Memorization methods Linear and logistic regression Unsupervised methods Exploring advanced methods PART 3 DELIVERING RESULTS Documentation and deployment Producing effective presentations

Practical Data Science Cookbook

Author : Prabhanjan Tattar
Publisher : Packt Publishing Ltd
ISBN 13 : 178712326X
Total Pages : 428 pages
Book Rating : 4.7/5 (871 download)

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Book Synopsis Practical Data Science Cookbook by : Prabhanjan Tattar

Download or read book Practical Data Science Cookbook written by Prabhanjan Tattar and published by Packt Publishing Ltd. This book was released on 2017-06-29 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Practical Statistics for Data Scientists

Author : Peter Bruce
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491952911
Total Pages : 395 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Practical Statistics for Data Scientists by : Peter Bruce

Download or read book Practical Statistics for Data Scientists written by Peter Bruce and published by "O'Reilly Media, Inc.". This book was released on 2017-05-10 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

R for Data Science

Author : Hadley Wickham
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491910364
Total Pages : 521 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis R for Data Science by : Hadley Wickham

Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Modern Data Science with R

Author : Benjamin S. Baumer
Publisher : CRC Press
ISBN 13 : 0429575394
Total Pages : 830 pages
Book Rating : 4.4/5 (295 download)

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Book Synopsis Modern Data Science with R by : Benjamin S. Baumer

Download or read book Modern Data Science with R written by Benjamin S. Baumer and published by CRC Press. This book was released on 2021-03-31 with total page 830 pages. Available in PDF, EPUB and Kindle. Book excerpt: From a review of the first edition: "Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. 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 questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.

Practical Data Analysis

Author : Hector Cuesta
Publisher : Packt Publishing Ltd
ISBN 13 : 1785286668
Total Pages : 338 pages
Book Rating : 4.7/5 (852 download)

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Book Synopsis Practical Data Analysis by : Hector Cuesta

Download or read book Practical Data Analysis written by Hector Cuesta and published by Packt Publishing Ltd. This book was released on 2016-09-30 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn Acquire, format, and visualize your data Build an image-similarity search engine Generate meaningful visualizations anyone can understand Get started with analyzing social network graphs Find out how to implement sentiment text analysis Install data analysis tools such as Pandas, MongoDB, and Apache Spark Get to grips with Apache Spark Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.

Practical Data Science with Python

Author : Nathan George
Publisher : Packt Publishing Ltd
ISBN 13 : 1801076650
Total Pages : 621 pages
Book Rating : 4.8/5 (1 download)

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Book Synopsis Practical Data Science with Python by : Nathan George

Download or read book Practical Data Science with Python written by Nathan George and published by Packt Publishing Ltd. This book was released on 2021-09-30 with total page 621 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to effectively manage data and execute data science projects from start to finish using Python Key FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook Description Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source. What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is for The book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science. The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.

Data Mining

Author : Ian H. Witten
Publisher : Elsevier
ISBN 13 : 0080890369
Total Pages : 665 pages
Book Rating : 4.0/5 (88 download)

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Book Synopsis Data Mining by : Ian H. Witten

Download or read book Data Mining written by Ian H. Witten and published by Elsevier. This book was released on 2011-02-03 with total page 665 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

R for Political Data Science

Author : Francisco Urdinez
Publisher : CRC Press
ISBN 13 : 1000204510
Total Pages : 469 pages
Book Rating : 4.0/5 (2 download)

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Book Synopsis R for Political Data Science by : Francisco Urdinez

Download or read book R for Political Data Science written by Francisco Urdinez and published by CRC Press. This book was released on 2020-11-18 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: R for Political Data Science: A Practical Guide is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was written by political scientists, thinking about the many real-world problems faced in their work. The book has 16 chapters and is organized in three sections. The first, on the use of R, is for those users who are learning R or are migrating from another software. The second section, on econometric models, covers OLS, binary and survival models, panel data, and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation, fuzzy merge of large datasets, web mining, quantitative text analysis, network analysis, mapping, spatial cluster analysis, and principal component analysis. Key features: Each chapter has the most up-to-date and simple option available for each task, assuming minimal prerequisites and no previous experience in R Makes extensive use of the Tidyverse, the group of packages that has revolutionized the use of R Provides a step-by-step guide that you can replicate using your own data Includes exercises in every chapter for course use or self-study Focuses on practical-based approaches to statistical inference rather than mathematical formulae Supplemented by an R package, including all data As the title suggests, this book is highly applied in nature, and is designed as a toolbox for the reader. It can be used in methods and data science courses, at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD, political consultants, or a public official, all of whom need to transform their datasets into substantive and easily interpretable conclusions.

Introduction to Data Science

Author : Rafael A. Irizarry
Publisher : CRC Press
ISBN 13 : 1000708039
Total Pages : 794 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Introduction to Data Science by : Rafael A. Irizarry

Download or read book Introduction to Data Science written by Rafael A. Irizarry and published by CRC Press. This book was released on 2019-11-20 with total page 794 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

Practical Data Science with R, Second Edition

Author : John Mount
Publisher : Simon and Schuster
ISBN 13 : 1638352747
Total Pages : 946 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Practical Data Science with R, Second Edition by : John Mount

Download or read book Practical Data Science with R, Second Edition written by John Mount and published by Simon and Schuster. This book was released on 2019-11-17 with total page 946 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever expanding field of data science. You’ll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. About the technology Evidence-based decisions are crucial to success. Applying the right data analysis techniques to your carefully curated business data helps you make accurate predictions, identify trends, and spot trouble in advance. The R data analysis platform provides the tools you need to tackle day-to-day data analysis and machine learning tasks efficiently and effectively. About the book Practical Data Science with R, Second Edition is a task-based tutorial that leads readers through dozens of useful, data analysis practices using the R language. By concentrating on the most important tasks you’ll face on the job, this friendly guide is comfortable both for business analysts and data scientists. Because data is only useful if it can be understood, you’ll also find fantastic tips for organizing and presenting data in tables, as well as snappy visualizations. What's inside Statistical analysis for business pros Effective data presentation The most useful R tools Interpreting complicated predictive models About the reader You’ll need to be comfortable with basic statistics and have an introductory knowledge of R or another high-level programming language. About the author Nina Zumel and John Mount founded a San Francisco–based data science consulting firm. Both hold PhDs from Carnegie Mellon University and blog on statistics, probability, and computer science.

Practical Data Science with SAP

Author : Greg Foss
Publisher : O'Reilly Media
ISBN 13 : 1492046418
Total Pages : 333 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Practical Data Science with SAP by : Greg Foss

Download or read book Practical Data Science with SAP written by Greg Foss and published by O'Reilly Media. This book was released on 2019-09-18 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to fuse today's data science tools and techniques with your SAP enterprise resource planning (ERP) system. With this practical guide, SAP veterans Greg Foss and Paul Modderman demonstrate how to use several data analysis tools to solve interesting problems with your SAP data. Data engineers and scientists will explore ways to add SAP data to their analysis processes, while SAP business analysts will learn practical methods for answering questions about the business. By focusing on grounded explanations of both SAP processes and data science tools, this book gives data scientists and business analysts powerful methods for discovering deep data truths. You'll explore: Examples of how data analysis can help you solve several SAP challenges Natural language processing for unlocking the secrets in text Data science techniques for data clustering and segmentation Methods for detecting anomalies in your SAP data Data visualization techniques for making your data come to life

Data Science for Marketing Analytics

Author : Mirza Rahim Baig
Publisher : Packt Publishing Ltd
ISBN 13 : 1800563884
Total Pages : 637 pages
Book Rating : 4.8/5 (5 download)

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Book Synopsis Data Science for Marketing Analytics by : Mirza Rahim Baig

Download or read book Data Science for Marketing Analytics written by Mirza Rahim Baig and published by Packt Publishing Ltd. This book was released on 2021-09-07 with total page 637 pages. Available in PDF, EPUB and Kindle. Book excerpt: Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming language Key FeaturesUse data analytics and machine learning in a sales and marketing contextGain insights from data to make better business decisionsBuild your experience and confidence with realistic hands-on practiceBook Description Unleash the power of data to reach your marketing goals with this practical guide to data science for business. This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects. You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions. As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior. By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making. What you will learnLoad, clean, and explore sales and marketing data using pandasForm and test hypotheses using real data sets and analytics toolsVisualize patterns in customer behavior using MatplotlibUse advanced machine learning models like random forest and SVMUse various unsupervised learning algorithms for customer segmentationUse supervised learning techniques for sales predictionEvaluate and compare different models to get the best outcomesOptimize models with hyperparameter tuning and SMOTEWho this book is for This marketing book is for anyone who wants to learn how to use Python for cutting-edge marketing analytics. Whether you're a developer who wants to move into marketing, or a marketing analyst who wants to learn more sophisticated tools and techniques, this book will get you on the right path. Basic prior knowledge of Python and experience working with data will help you access this book more easily.

Practical Machine Learning in R

Author : Fred Nwanganga
Publisher : John Wiley & Sons
ISBN 13 : 1119591511
Total Pages : 464 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis Practical Machine Learning in R by : Fred Nwanganga

Download or read book Practical Machine Learning in R written by Fred Nwanganga and published by John Wiley & Sons. This book was released on 2020-05-27 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language Machine learning—a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions—allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more. Explores data management techniques, including data collection, exploration and dimensionality reduction Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost Practical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field.

Practical Data Science for Information Professionals

Author : David Stuart
Publisher : Facet Publishing
ISBN 13 : 1783303441
Total Pages : 200 pages
Book Rating : 4.7/5 (833 download)

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Book Synopsis Practical Data Science for Information Professionals by : David Stuart

Download or read book Practical Data Science for Information Professionals written by David Stuart and published by Facet Publishing. This book was released on 2020-07-24 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Data Science for Information Professionals provides an accessible introduction to a potentially complex field, providing readers with an overview of data science and a framework for its application. It provides detailed examples and analysis on real data sets to explore the basics of the subject in three principle areas: clustering and social network analysis; predictions and forecasts; and text analysis and mining. As well as highlighting a wealth of user-friendly data science tools, the book also includes some example code in two of the most popular programming languages (R and Python) to demonstrate the ease with which the information professional can move beyond the graphical user interface and achieve significant analysis with just a few lines of code. After reading, readers will understand: · the growing importance of data science · the role of the information professional in data science · some of the most important tools and methods that information professionals can use. Bringing together the growing importance of data science and the increasing role of information professionals in the management and use of data, Practical Data Science for Information Professionals will provide a practical introduction to the topic specifically designed for the information community. It will appeal to librarians and information professionals all around the world, from large academic libraries to small research libraries. By focusing on the application of open source software, it aims to reduce barriers for readers to use the lessons learned within.

An Introduction to Statistical Learning

Author : Gareth James
Publisher : Springer Nature
ISBN 13 : 3031387473
Total Pages : 617 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis An Introduction to Statistical Learning by : Gareth James

Download or read book An Introduction to Statistical Learning written by Gareth James and published by Springer Nature. This book was released on 2023-08-01 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Data Science Using Python and R

Author : Chantal D. Larose
Publisher : John Wiley & Sons
ISBN 13 : 1119526817
Total Pages : 256 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis Data Science Using Python and R by : Chantal D. Larose

Download or read book Data Science Using Python and R written by Chantal D. Larose and published by John Wiley & Sons. This book was released on 2019-04-09 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.