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| data science en español: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 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 en español: Data Science Thinking Longbing Cao, 2018-08-17 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. |
| data science en español: Data Science on AWS Chris Fregly, Antje Barth, 2021-04-07 With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more |
| data science en español: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 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. |
| data science en español: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| data science en español: Data Science from Scratch Joel Grus, 2015-04-14 Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases |
| data science en español: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-24 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| data science en español: e-Research y español LE/L2 Mar Cruz Piñol, 2021-05-03 e-Research y español LE/L2: Investigar en la era digital es el primer volumen que aborda de manera conjunta las aportaciones al español LE/L2 de la lingüística de corpus, la biblioteconomía y la edición digital. Es excelente para mejorar las técnicas de investigación a la vez que se toma conciencia sobre el uso de las tecnologías en los estudios sobre el español LE/L2. Características principales: visión interdisciplinar e internacional a partir del trabajo de expertos que ejercen su actividad docente, investigadora y profesional en diferentes ámbitos y en distintos países; planteamiento teórico-práctico mediante la exposición de una reflexión teórica y la descripción de casos prácticos; sólido marco teórico que se presenta en los dos primeros capítulos; estructura homogénea dividida en útiles apartados (necesidades, cómo ayudan las tecnologías y casos concretos) para que el lector pueda localizar los contenidos con facilidad; lectura del volumen que puede ser lineal (capítulo tras capítulo) o transversal (por ejemplo, los casos prácticos que se presentan en cada capítulo); materiales complementarios en línea, como, por ejemplo, glosario hipertextual y enlaces a los corpus y programas mencionados en los capítulos. Escrito en español, de manera clara y accesible, y con abundantes ejemplos e ilustraciones, e-Research y español LE/L2: Investigar en la era digital es ideal para todas aquellas personas vinculadas con la investigación en torno al español LE/L2: estudiantes de máster y doctorado, directores de tesis (PhD o máster) y profesores. e-Research y español LE/L2: Investigar en la era digital is the first volume that jointly addresses the contributions of corpus linguistics, librarianship and digital publishing to Spanish as a second or foreign language (LE/L2). It is excellent for improving research techniques while raising awareness about the use of technologies in studies of Spanish LE/L2. Main features: interdisciplinary and international vision based on the work of experts who carry out their teaching, research and professional activities in different fields and in different countries; theoretical-practical approach through the presentation of a theoretical reflection and the description of practical cases; solid theoretical framework which is presented in the first two chapters; each chapter is divided into three useful sections (needs, how technologies help, and specific cases) so that the reader can easily locate the contents; reading can be linear (chapter by chapter) or transversal (for example, the practical cases presented in each chapter); supplementary online materials include a hypertext glossary and links to the corpus and programs mentioned in the chapters. Written in Spanish, in a clear and accessible way, and with abundant examples and illustrations, e-Research y español LE/L2: Investigar en la era digital is ideal for all those involved in research on Spanish LE/L2, master's and doctoral students, thesis supervisors and professors. |
| data science en español: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| data science en español: Practical Statistics for Data Scientists Peter Bruce, Andrew Bruce, 2017-05-10 Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data |
| data science en español: Data Science in Education Using R Ryan A. Estrellado, Emily Freer, Joshua M. Rosenberg, Isabella C. Velásquez, 2020-10-26 Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a learn by doing approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development. |
| data science en español: Data Smart John W. Foreman, 2013-10-31 Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the data scientist, toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know. |
| data science en español: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| data science en español: An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor, 2023-08-01 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 en español: Python for Data Analysis Wes McKinney, 2017-09-25 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples |
| data science en español: Linguistic Corpora and Big Data in Spanish and Portuguese Miguel Calderón Campos, Gael Vaamonde, 2024-10-21 In recent decades, corpus linguistics has experienced tremendous development in the Hispanic world, along two opposite but complementary approaches: increase in corpus size (corpus linguistics as Big Data) and improvement in document selection and data annotation (corpus linguistics as High Quality Data). The first approach has led to the creation of massive corpora such as EsTenTen; at the same time, it has promoted the use of the web and social networks as corpora. The second perspective gives rise to specialized corpora such as Post Scriptum or Oralia Diacrónica del español (ODE). The contributions gathered in this volume combine both methods in order to exploit their advantages and to overcome their possible limitations. On the one hand, it addresses the creation and design of small corpora focused on data quality; on the other hand, it offers case studies that make use of both specialized corpora and massive data extracted from the web. Highlighting the complementary nature of both methods is the main idea of this book. |
| data science en español: Ciencia de datos John D. Kelleher, Brendan Tierney, 2021-10-30 El crecimiento en el uso de la ciencia de datos en nuestras sociedades está impulsado por la aparición del big data y las redes sociales, la aceleración de la potencia informática, la reducción masiva en el costo de la memoria de la computadora y el desarrollo de métodos más potentes para el análisis y modelado de datos, como el aprendizaje profundo. Todos estos factores juntos hacen que nunca haya sido tan fácil para las organizaciones recopilar, almacenar y procesar datos. Al mismo tiempo, estas innovaciones técnicas y la aplicación más amplia de la ciencia de datos hacen que los desafíos éticos relacionados con el uso de datos y la privacidad individual nunca han sido tan apremiantes. |
| data science en español: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| data science en español: Getting Started with Data Science Murtaza Haider, 2015-12-14 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 en español: GED Edición En Español (Spanish Edition) Murray Rockowitz, Samuel C. Brownstein, Max Peters, Ira K. Wolf, 2010-08-01 The updated Spanish language edition of Barron's GED test prep manual reflects the most recent GED High School Equivalency Exams in subject matter, length, question types, and degree of difficulty. Featuring a full-length diagnostic test, and two full-length practice exams, this manual is presented entirely in Spanish for Spanish-speakers who intend to take the GED's Spanish language version. The diagnostic test's questions come with answer keys, answer analyses, and self-appraisal charts. All questions in both GED practice exams are answered and explained. The book features extensive review in all test areas, which include Spanish grammar and essay writing, social studies, science, arts and literature, and math. |
| data science en español: Data Science John D. Kelleher, Brendan Tierney, 2018-04-13 A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects. |
| data science en español: Data Science Vijay Kotu, Bala Deshpande, 2018-11-27 Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You'll be able to: - Gain the necessary knowledge of different data science techniques to extract value from data. - Master the concepts and inner workings of 30 commonly used powerful data science algorithms. - Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... - Contains fully updated content on data science, including tactics on how to mine business data for information - Presents simple explanations for over twenty powerful data science techniques - Enables the practical use of data science algorithms without the need for programming - Demonstrates processes with practical use cases - Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language - Describes the commonly used setup options for the open source tool RapidMiner |
| data science en español: Tourism and ICTs: Advances in Data Science, Artificial Intelligence and Sustainability Antonio J. Guevara Plaza, |
| data science en español: Introduction to Data Science Laura Igual, Santi Seguí, 2017-02-22 This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website. |
| data science en español: The Art of Data Science Roger D. Peng, Elizabeth Matsui, 2016-06-08 This book describes the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.--Leanpub.com. |
| data science en español: The Handbook of Spanish Second Language Acquisition Kimberly L. Geeslin, 2018-08-14 Bringing together a comprehensive collection of newly-commissioned articles, this Handbook covers the most recent developments across a range of sub-fields relevant to the study of second language Spanish. Provides a unique and much-needed collection of new research in this subject, compiled and written by experts in the field Offers a critical account of the most current, ground-breaking developments across key fields, each of which has seen innovative empirical research in the past decade Covers a broad range of issues including current theoretical approaches, alongside a variety of entries within such areas as the sound system, morphosyntax, individual and social factors, and instructed language learning Presents a variety of methodological approaches spanning the active areas of research in language acquisition |
| data science en español: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| data science en español: LinkedIn práctico y profesional Soraya Paniagua Amador, LinkedIn práctico y profesional es un tutorial práctico sobre la red social líder mundial en el ámbito profesional y de los recursos humanos. Este manual acompaña al usuario por todos los menús de navegación explicando paso a paso las diferentes funcionalidades y proponiendo numerosas actividades para realizar a medida que se avanza en el proceso de enseñanza-aprendizaje. Una vez completada la guía el usuario será competente para utilizar LinkedIn, en entorno web, de forma efectiva y eficiente tanto en el ámbito personal como profesional. Para conseguir los objetivos de aprendizaje es aconsejable seguir esta guía mientras se usa, a la vez, la herramienta LinkedIn. Tener un perfil en LinkedIn es, a día de hoy, imprescindible si aspiramos a un trabajo cualificado. Es el entorno preferido por los técnicos de selección de personal y head hunters, no en vano el 93% de los departamentos de recursos humanos usan LinkedIn para encontrar nuevos candidatos. Es, también, el lugar donde cimentar nuestra marca personal, no solo por el perfil digital y la amplia red de contactos que podemos construir, sino también por la plataforma de publicación y los grupos de interés que podemos crear y a los que nos podemos unir. Para las empresas, aparte de su utilidad como herramienta de reclutamiento, LinkedIn es una potente plataforma de comunicación y marketing, así como un gran recurso para los departamentos de ventas ya que posibilita establecer contactos con potenciales clientes. Este sencillo y útil tutorial es un excelente material para nuevos usuarios pero, sobre todo, para aquellos que conocen el funcionamiento y sin embargo no están explotando todo su potencial. Más de 350 millones de personas están en LinkedIn. Y tú, si aún no estás, ¿a qué esperas? Las redes sociales se caracterizan por estar continuamente actualizándose (nuevos menús o funcionalidades). No pasa nada ya que lo importante es conocer el funcionamiento general, el concepto y la forma de sacar el máximo provecho. Cuando se domina el sistema las actualizaciones apenas tienen impacto en el aprendizaje, se entienden de manera natural. |
| data science en español: The Routledge Handbook of Variationist Approaches to Spanish Manuel Díaz-Campos, 2021-10-12 The Routledge Handbook of Variationist Approaches to Spanish provides an up-to-date overview of the latest research examining sociolinguistic approaches to analyzing variation in Spanish. Divided into three sections, the book includes the most current research conducted in Spanish variationist sociolinguistics. This comprehensive volume covers phonological, morphosyntactic, social, and lexical variation in Spanish. Each section is further divided into subsections focusing on specific areas of language variation, highlighting the most salient and current developments in each subfield of Hispanic sociolinguistics. As such, this Handbook delves further into the details of topics relating to variation and change in Spanish than previous publications, with a focus on the symbolic sociolinguistic value of specific phenomena in the field. Encouraging readers to think critically about language variation, this book will be of interest to advanced undergraduate and graduate students, as well as researchers seeking to explore lesser-known areas of Hispanic sociolinguistics. The Routledge Handbook of Variationist Approaches to Spanish will be a welcome addition to specialists and students in the fields of linguistics, Hispanic linguistics, sociolinguistics, and linguistic anthropology. |
| data science en español: Introducción a la lingüística de corpus en español Guillermo Rojo, 2021-04-05 Introducción a la lingüística de corpus en español es la primera obra concebida desde la óptica del español para investigar los corpus textuales existentes en la actualidad. Destinada a conjugar armónicamente la exposición de cuestiones teóricas y metodológicas, proporciona información detallada sobre las tareas necesarias en el diseño, construcción y explotación de un corpus a partir de numerosos ejemplos de obtención de datos sobre diferentes cuestiones léxicas y gramaticales. Características principales: • Exposición de cuestiones teóricas y metodológicas combinada con el tratamiento de casos prácticos de extracción y análisis de datos procedentes de corpus textuales de español; • Análisis de fenómenos léxicos y gramaticales del español desde diferentes perspectivas y con atención a la variabilidad diacrónica, diatópica y diastrática; • Indicación detallada del modo de obtener los datos necesarios para la investigación en diferentes corpus del español; • Inclusión de un resumen inicial, actividades de investigación en cada capítulo y lecturas complementarias recomendadas; • Presentación de un capítulo final con herramientas informáticas útiles para el análisis de textos no incluidos en corpus textuales; • Recopilación de los principales términos usados en la lingüística de corpus en un glosario bilingüe (español e inglés). Introducción a la lingüística de corpus en español es una obra con un enfoque marcadamente didáctico, y dirigida fundamentalmente a estudiantes avanzados de grado y posgrado, profesores que necesiten hacer uso de corpus en sus clases, investigadores que precisen un conocimiento más profundo de la lingüística de corpus o expertos en otras disciplinas que deseen familiarizarse con una perspectiva técnica de los fenómenos lingüísticos. |
| data science en español: SQL for Data Scientists Renee M. P. Teate, 2021-08-17 Jump-start your career as a data scientist—learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that’s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data. This guide for data scientists differs from other instructional guides on the subject. It doesn’t cover SQL broadly. Instead, you’ll learn the subset of SQL skills that data analysts and data scientists use frequently. You’ll also gain practical advice and direction on how to think about constructing your dataset. Gain an understanding of relational database structure, query design, and SQL syntax Develop queries to construct datasets for use in applications like interactive reports and machine learning algorithms Review strategies and approaches so you can design analytical datasets Practice your techniques with the provided database and SQL code In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner’s perspective, moving your data scientist career forward! |
| data science en español: English Pronunciation for Speakers of Spanish María de los Ángeles Gómez González, Teresa Sánchez Roura, 2016-01-15 English Pronunciation for Speakers of Spanish fills a gaping hole in the market for books on English phonetics and pronunciation because it not only combines theoretical issues and applications to practice, but it also adopts a contrastive English-Spanish approach to better suit the needs of Spanish-speaking learners of English (SSLE), enabling them to build gradually on the knowledge gained in each chapter. The book covers the key concepts of English phonetics and phonology in seven chapters written in an accessible and engaging style: 1. Phonetics and Phonology 2. The Production and Classification of Speech Sounds 3. Vowels and Glides 4. Consonants 5. Segment Dynamics: Aspects of Connected Speech 6. Beyond the Segment: Stress and Intonation 7. Predicting Pronunciation from Spelling (and vice versa) Features: in-text audio illustrations, as well as over a hundred written and audio exercises with corresponding keys and different kinds of artwork (Tables, Figures, illustrations, spectrograms, etc.) classic readings in the discipline in the Further Reading section of each chapter highlights the phonetic contrasts and specific cues that are more important to aid comprehension in English and offers guidelines on correct pronunciation habits to help SSLE sound as close as possible to native English The book's companion website, EPSS Multimedia Lab, can be used on computers, smartphones and tablets, and is useful for the self-taught student and the busy lecturer alike. The website of the EPSS Multimedia lab can be accessed here: http://www.usc.gal/multimlab/ Features of the website: a complete sound bank defining and illustrating the sounds of English RP as compared with those of Peninsular Spanish written definitions and animated diagrams, videos and original recordings (by native speakers of English and Spanish) showing the articulation of each sound, alongside its most common spellings, as well as pronunciation practice for individual words and whole sentences a comprehensive selection of over a hundred written and audio exercises (with their keys) for practice both at home or in the language lab audio files corresponding to the audio illustrations given in the written book a repository of useful resources by topics and a list of online glossaries and pronunciation dictionaries |
| data science en español: Current Catalog National Library of Medicine (U.S.), 1967 Includes subject section, name section, and 1968-1970, technical reports. |
| data science en español: Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions Matt Taddy, 2019-08-23 Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling Understand how use ML tools in real world business problems, where causation matters more that correlation Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science. |
| data science en español: Data Analytics Herbert Jones, 2018-09-19 If you want to learn about data analytics and data mining then keep reading... 2 comprehensive manuscripts in 1 book Data Analytics: An Essential Beginner's Guide To Data Mining, Data Collection, Big Data Analytics For Business, And Business Intelligence Concepts Data Mining: The Data Mining Guide for Beginners, Including Applications for Business, Data Mining Techniques, Concepts, and More With this book, not only will you understand all the internal nitty-gritties about data analytics and data mining, you will also understand why data analytics and data mining is changing the business arena. You'll realize that the high-performance analytics will enable you to do stuff that you never thought about before probably because the volumes of data were just too big (among other reasons) and so much more. Here are just some of the topics that are discussed in the first part of this book: Overview Of Data Analytics: What Is Data Analytics (And Big Data Analytics)? Data Analytics And Business Intelligence Data Analysis And Data Analytics Data Mining Data Collection Types Of Data Analytics The Process: The Lifecycle Of Big Data Analytics Behavioral Analytics: Using Big Data Analytics To Find Hidden Customer Behavior Patterns Further Pattern Discovery In Advanced Analytics: Machine Learning And Much, Much More In part 2 of this book, you will learn the following: Model creation How to prepare your data How to clean your data Data Mining Similarity and distances of data The effect of data distribution Association pattern mining What is cluster analysis? What is an outlier in data mining? How to deal with outliers in data mining Methods of identifying outliers in data Applications of data mining in the business industry So if you are serious about becoming an expert in data analytics and data mining, start with this book by clicking add to cart! |
| data science en español: El léxico-gramática del español Alan V. Brown, Yanira B. Paz, Earl Kjar Brown, 2021-05-30 El léxico-gramática del español ofrece una aproximación alternativa al estudio de la gramática avanzada del español. Este libro brinda al estudiante un enfoque auténtico y contextualizado del uso del español, basándose en datos provenientes de corpus de español-L1 y L2 junto a la investigación lingüística a fin de describir las características léxico-gramaticales fundamentales de la lengua y su variación. Cada capítulo incluye actividades guiadas para que los estudiantes puedan realizar búsquedas en estos corpus con el propósito de llegar a conclusiones fundamentadas en evidencias empíricas sobre cómo los aprendices de varios niveles de competencia usan ciertos elementos léxico-gramaticales. Este libro representa un recurso ideal para los estudiantes de la gramática avanzada del español a nivel de pregrado y posgrado. El léxico-gramática del español provides an alternative approach to the study of advanced Spanish grammar. Drawing on L1 and L2 Spanish language corpora and linguistic research to describe key lexico-grammatical characteristics of the Spanish language, this book gives students insight into real, variable, and contextualized usage of Spanish. Each chapter includes guided exercises so that students can conduct their own searches of the corpus and draw evidence-based conclusions on how particular grammar structures are used by Spanish speakers at varying levels of proficiency. This is an ideal resource for advanced undergraduate and postgraduate students of Spanish language and linguistics. |
| data science en español: Learning Spark Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Lee, 2020-07-16 Data is bigger, arrives faster, and comes in a variety of formats—and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you’ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow |
| data science en español: IoT and Data Science in Engineering Management Fausto Pedro García Márquez, Isaac Segovia Ramírez, Pedro José Bernalte Sánchez, Alba Muñoz del Río, 2023-03-24 This book presents the selected research works from the 16th International Conference on Industrial Engineering and Industrial Management in 2022. The conference was promoted by ADINGOR (Asociación para el Desarrollo de la Ingeniería de Organización), organized by Ingenium Research Group at Universidad de Castilla-La Mancha, Spain, and it took place on July 7th and 8th, 2022, in Toledo, Spain. The book highlights some of the latest research advances and cutting-edge analyses of real-world case studies on Industrial Engineering and Industrial Management from a wide range of international contexts. It also identifies business applications and the latest findings and innovations in Operations Management and in Decision Sciences. |
| data science en español: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 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. |
| data science en español: Alumnado español de alto y bajo rendimiento en ciencias en PISA 2015: análisis del impacto de algunas variables de contexto Tourón, Javier, López-González, Emelina, Lizasoain Hernández, Luis, En línea con los resultados anteriores del programa PISA, la edición de 2015 evidencia que uno de los principales problemas del sistema educativo español es que casi el 20% del alumnado se sitúa en los dos niveles inferiores de desempeño, y sólo 5% del alumnado consigue alcanzar los niveles más altos de competencia en la materia de ciencias. En relación a estos datos, el objetivo de este trabajo es doble. En primer lugar, caracterizar los grupos extremos de rendimiento en ciencias en el alumnado evaluado en PISA 2015. En segundo lugar, identificar las variables que tienen un impacto significativo en el desempeño de estos grupos a fin de generar información que permita intervenciones por parte de autoridades educativas o centros. Con esta intención se realiza un análisis secundario sobre la base de datos de estudiantes españoles de PISA 2015. Las variables que se analizan son indicadores de diversos constructos medidos en los cuestionarios de contexto aplicados a estudiantes, docentes y directivos. Los resultados muestran que las variables que más diferencian entre los dos grupos extremos de estudiantes son las relacionadas con la autoeficacia percibida en ciencias, el interés y disfrute por las cuestiones científicas y las creencias epistemológicas, entre otras. En el ámbito de la escuela, el indicador con más peso es el relacionado con los comportamientos del alumnado que dificultan el aprendizaje. El conjunto de variables que compone este factor apunta a la importancia de un clima escolar que favorezca y potencie un adecuado ambiente de trabajo en el aula. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
