Data Science in Practice

This book approaches big data, artificial intelligence, machine learning, and business intelligence through the lens of Data Science. We have grown accustomed to seeing these terms mentioned time and time again in the mainstream media.

Author: Alan Said

Publisher: Springer

ISBN: 3319975560

Category: Technology & Engineering

Page: 195

View: 518

This book approaches big data, artificial intelligence, machine learning, and business intelligence through the lens of Data Science. We have grown accustomed to seeing these terms mentioned time and time again in the mainstream media. However, our understanding of what they actually mean often remains limited. This book provides a general overview of the terms and approaches used broadly in data science, and provides detailed information on the underlying theories, models, and application scenarios. Divided into three main parts, it addresses what data science is; how and where it is used; and how it can be implemented using modern open source software. The book offers an essential guide to modern data science for all students, practitioners, developers and managers seeking a deeper understanding of how various aspects of data science work, and of how they can be employed to gain a competitive advantage.

Data Science

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.

Author: Vijay Kotu

Publisher: Morgan Kaufmann

ISBN: 0128147628

Category: Computers

Page: 568

View: 844

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

Handbook of Research on Data Science for Effective Healthcare Practice and Administration

The Handbook of Research on Data Science for Effective Healthcare Practice and Administration is a critical reference source that overviews the state of data analysis as it relates to current practices in the health sciences field.

Author: Noughabi, Elham Akhond Zadeh

Publisher: IGI Global

ISBN: 1522525165

Category: Computers

Page: 545

View: 511

Data science has always been an effective way of extracting knowledge and insights from information in various forms. One industry that can utilize the benefits from the advances in data science is the healthcare field. The Handbook of Research on Data Science for Effective Healthcare Practice and Administration is a critical reference source that overviews the state of data analysis as it relates to current practices in the health sciences field. Covering innovative topics such as linear programming, simulation modeling, network theory, and predictive analytics, this publication is recommended for all healthcare professionals, graduate students, engineers, and researchers that are seeking to expand their knowledge of efficient techniques for information analysis in the healthcare professions.

Applied Data Science

The Twenty-First Century Virtuous RD&D Cycle is being used to design, develop,
and deliver data science tools and platforms. Data discovery and preparation,
and data science platforms are concrete examples of this cycle in practice.

Author: Martin Braschler

Publisher: Springer

ISBN: 3030118215

Category: Computers

Page: 465

View: 290

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

Envisioning the Data Science Discipline

As a result, data ethics take on an ever more prominent role in both data science
curricula and data science practice. The Hippocratic Oath, which details the ideal
conduct of physicians in terms of their treatment of patients and interactions with ...

Author: National Academies of Sciences, Engineering, and Medicine

Publisher: National Academies Press

ISBN: 0309465052

Category: Education

Page: 68

View: 797

The need to manage, analyze, and extract knowledge from data is pervasive across industry, government, and academia. Scientists, engineers, and executives routinely encounter enormous volumes of data, and new techniques and tools are emerging to create knowledge out of these data, some of them capable of working with real-time streams of data. The nation's ability to make use of these data depends on the availability of an educated workforce with necessary expertise. With these new capabilities have come novel ethical challenges regarding the effectiveness and appropriateness of broad applications of data analyses. The field of data science has emerged to address the proliferation of data and the need to manage and understand it. Data science is a hybrid of multiple disciplines and skill sets, draws on diverse fields (including computer science, statistics, and mathematics), encompasses topics in ethics and privacy, and depends on specifics of the domains to which it is applied. Fueled by the explosion of data, jobs that involve data science have proliferated and an array of data science programs at the undergraduate and graduate levels have been established. Nevertheless, data science is still in its infancy, which suggests the importance of envisioning what the field might look like in the future and what key steps can be taken now to move data science education in that direction. This study will set forth a vision for the emerging discipline of data science at the undergraduate level. This interim report lays out some of the information and comments that the committee has gathered and heard during the first half of its study, offers perspectives on the current state of data science education, and poses some questions that may shape the way data science education evolves in the future. The study will conclude in early 2018 with a final report that lays out a vision for future data science education.

Roundtable on Data Science Postsecondary Education

Ethics and Curriculum Development Lise Getoor, University of California, Santa
Cruz, asked how these Ph.D. programs integrate responsible data science
practice and data science ethics. Dhar said that CDS is collaborating with AI
Now8 on ...

Author: National Academies of Sciences, Engineering, and Medicine

Publisher: National Academies Press

ISBN: 030967770X

Category: Education

Page: 223

View: 306

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

Exam DP 100 Azure Data Scientist Associate 48 Test Prep Questions

This book is designed to be an ancillary to the classes, labs, and hands on practice that you have diligently worked on in preparing to obtain your DP-100: Azure Data Scientist Associate certification.

Author: Ger Arevalo

Publisher: Ger Arevalo

ISBN:

Category: Computers

Page:

View: 446

This book is designed to be an ancillary to the classes, labs, and hands on practice that you have diligently worked on in preparing to obtain your DP-100: Azure Data Scientist Associate certification. I won’t bother talking about the benefits of certifications. This book tries to reinforce the knowledge that you have gained in your process of studying. It is meant as one of the end steps in your preparation for the DP-100 exam. This book is short, but It will give you a good gauge of your readiness. Learning can be seen in 4 stages: 1. Unconscious Incompetence 2. Conscious Incompetence 3. Conscious Competence 4. Unconscious Competence This book will assume the reader has already gone through the needed classes, labs, and practice. It is meant to take the reader from stage 2, Conscious Incompetence, to stage 3 Conscious Competence. At stage 3, you should be ready to take the exam. Only real-world scenarios and work experience will take you to stage 4, Unconscious Competence. Before we get started, we all have doubts when preparing to take an exam. What is your reason and purpose for taking this exam? Remember your reason and purpose when you have some doubts. Obstacle is the way. Control your mind, attitude, and you can control the situation. Persistence leads to confidence. Confidence erases doubts.

Principles of Managerial Statistics and Data Science

Introduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business students Through a statistical perspective, this book introduces readers to the topic of data science, ...

Author: Roberto Rivera

Publisher: John Wiley & Sons

ISBN: 1119486416

Category: Mathematics

Page: 688

View: 493

Introduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business students Through a statistical perspective, this book introduces readers to the topic of data science, including Big Data, data analytics, and data wrangling. Chapters include multiple examples showing the application of the theoretical aspects presented. It features practice problems designed to ensure that readers understand the concepts and can apply them using real data. Over 100 open data sets used for examples and problems come from regions throughout the world, allowing the instructor to adapt the application to local data with which students can identify. Applications with these data sets include: Assessing if searches during a police stop in San Diego are dependent on driver’s race Visualizing the association between fat percentage and moisture percentage in Canadian cheese Modeling taxi fares in Chicago using data from millions of rides Analyzing mean sales per unit of legal marijuana products in Washington state Topics covered in Principles of Managerial Statistics and Data Science include:data visualization; descriptive measures; probability; probability distributions; mathematical expectation; confidence intervals; and hypothesis testing. Analysis of variance; simple linear regression; and multiple linear regression are also included. In addition, the book offers contingency tables, Chi-square tests, non-parametric methods, and time series methods. The textbook: Includes academic material usually covered in introductory Statistics courses, but with a data science twist, and less emphasis in the theory Relies on Minitab to present how to perform tasks with a computer Presents and motivates use of data that comes from open portals Focuses on developing an intuition on how the procedures work Exposes readers to the potential in Big Data and current failures of its use Supplementary material includes: a companion website that houses PowerPoint slides; an Instructor's Manual with tips, a syllabus model, and project ideas; R code to reproduce examples and case studies; and information about the open portal data Features an appendix with solutions to some practice problems Principles of Managerial Statistics and Data Science is a textbook for undergraduate and graduate students taking managerial Statistics courses, and a reference book for working business professionals.

The Data Science Handbook

This book provides a crash course in data science, combining all the necessary skills into a unified discipline.

Author: Field Cady

Publisher: John Wiley & Sons

ISBN: 1119092949

Category: Mathematics

Page: 416

View: 493

A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using Python™ along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon.

Analytics in Practice

This book provides an insight into the mechanics of Analytics and how to use it to identify and leverage the competitive advantage in the era of ever-growing data increment needs.

Author: Soumendra Mohanty

Publisher: Tata McGraw-Hill Education

ISBN: 1259007170

Category:

Page:

View: 784

This book provides an insight into the mechanics of Analytics and how to use it to identify and leverage the competitive advantage in the era of ever-growing data increment needs. This book discusses topics such as Foundations of Analytics, Business Analytics, Sentiment Analysis and Opinion Mining, methodology, and technical architecture. It also contains useful case studies, tips, techniques, and best practices. This book would be a hands-on reference for practitioners for dealing with the type of information often called for by practitioners, developers, and specialists working in the Data Mining and Analytics area. It would also be highly useful for the students of Computer Science, Information Technology and MBA-Information Technology at various universities.

Data Science For Dummies

From the modern business enterprise to the lifestyle choices of today's digital
citizen, data science insights are driving changes and ... Data science is simply
the practice of using a set of analytical techniques and methodologies to derive
and ...

Author: Lillian Pierson

Publisher: John Wiley & Sons

ISBN: 1118841557

Category: Computers

Page: 408

View: 943

"Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles in organizations. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization's massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you'll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization."--Provided by publisher.

Science Education Research and Practice in Europe

However as any “new instrument”, video has modified researchers' practice to the
extent that the nature of information given to researchers is different from that of
written data, direct observation and even audio recordings. In this chapter, our ...

Author: Doris Jorde

Publisher: Springer Science & Business Media

ISBN: 9460919006

Category: Education

Page: 394

View: 676

Each volume in the 7-volume series The World of Science Education reviews research in a key region of the world. These regions include North America, South and Latin America, Asia, Australia and New Zealand, Europe, Arab States, and Sub-Saharan Africa. The focus of this Handbook is on science education in Europe. In producing this volume the editors have invited a range of authors to describe their research in the context of developments in the continent and further afield. In reading this book you are invited to consider the historical, social and political contexts that have driven developments in science education research over the years. A unique feature of science education in Europe is the impact of the European Union on research and development over many years. A growing number of multi-national projects have contributed to the establishment of a community of researchers increasingly accepting of methodological diversity. That is not to say that Europe is moving towards homogeneity, as this volume clearly shows.

A Hands On Introduction to Data Science

An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.

Author: Chirag Shah

Publisher: Cambridge University Press

ISBN: 1108472443

Category: Business & Economics

Page: 400

View: 715

An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.

Data Science and Digital Business

This book combines the analytic principles of digital business and data science with business practice and big data.

Author: Fausto Pedro García Márquez

Publisher: Springer

ISBN: 9783319956503

Category: Business & Economics

Page: 321

View: 411

This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business.

Data Science for Librarians

This text serves as a tool for library and information science students and educators working on data science curriculum design.

Author: Yunfei Du

Publisher: ABC-CLIO

ISBN: 1440871221

Category: Language Arts & Disciplines

Page: 160

View: 415

This unique textbook intersects traditional library science with data science principles that readers will find useful in implementing or improving data services within their libraries. Data Science for Librarians introduces data science to students and practitioners in library services. Writing for academic, public, and school library managers; library science students; and library and information science educators, authors Yunfei Du and Hammad Rauf Khan provide a thorough overview of conceptual and practical tools for data librarian practice. Partially due to how quickly data science evolves, libraries have yet to recognize core competencies and skills required to perform the job duties of a data librarian. As society transitions from the information age into the era of big data, librarians and information professionals require new knowledge and skills to stay current and take on new job roles, such as data librarianship. Skills such as data curation, research data management, statistical analysis, business analytics, visualization, smart city data, and learning analytics are relevant in library services today and will become increasingly so in the near future. This text serves as a tool for library and information science students and educators working on data science curriculum design. Reviews fundamental concepts and principles of data science Offers a practical overview of tools and software Highlights skills and services needed in the 21st-century academic library Covers the entire research data life cycle and the librarian's role at each stage Provides insight into how library science and data science intersect

Data Science and Machine Learning with Python

Unlock your potential as an AI and ML professional! This book covers basic to advanced level topics required to master the Machine Learning concepts.

Author: Swapnil Saurav

Publisher:

ISBN: 9788194633495

Category:

Page: 386

View: 562

Unlock your potential as an AI and ML professional! This book covers basic to advanced level topics required to master the Machine Learning concepts. There are lot of programs implemented which goes with the explaination - thats why we call it Learn and Practice. Book uses Scikit-learn (formerly scikits.learn and also known as sklearn) is the most popular package and also a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.Happy Coding in Python

Modern Data Science with R

This book will help readers with some background in statistics and modest prior experience with coding develop and practice the appropriate skills to tackle complex data science projects.

Author: Benjamin S. Baumer

Publisher: CRC Press

ISBN: 1498724582

Category: Business & Economics

Page: 556

View: 794

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

Science and Practice of Pediatric Critical Care Medicine

Science and Practice of Pediatric Critical Care Medicine, DOI 10.1007/978-1-
84800-921-9_14, © Springer-Verlag London ... Current technology exists to have
alarm or other data transmitted remotely to a pager or other computer/ monitor ...

Author: Derek S. Wheeler

Publisher: Springer Science & Business Media

ISBN: 9781848009219

Category: Medical

Page: 199

View: 121

The ? eld of critical care medicine is in the midst of a dramatic change. Technological and s- enti? c advances during the last decade have resulted in a fundamental change in the way we view disease processes, such as sepsis, shock, acute lung injury, and traumatic brain injury. Pediatric intensivists have been both witness to and active participants in bringing about these changes. As the understanding of the pathogenesis of these diseases reaches the cellular and molecular levels, the gap between critical care medicine and molecular biology will disappear. It is imperative that all physicians caring for critically ill children in this new era have a th- ough understanding of the applicability of molecular biology to the care of these patients at the bedside in order to keep up with the rapidly evolving ? eld of critical care medicine. To the same extent, the practice of critical care medicine is in the midst of fundamental change. In keeping with the Institute of Medicine’s report “Crossing the Quality Chasm,” the care of critically ill and injured children needs to be safe, evidence-based, equitable, ef? cient, timely, and fami- centered [1,2]. In the following pages, these changes in our specialty are discussed in greater scope and detail, offering the reader fresh insight into not only where we came from, but also where we are going as a specialty.

Doing Data Science

spam, Click Models clear rates, Data Visualization at Square Cleveland, William,
The Current Landscape (with a Little ... OK Cupid's Attempt correcting for, in
practice, What Do People Do About Confounding Things in Practice?
stratification ...

Author: Cathy O'Neil

Publisher: "O'Reilly Media, Inc."

ISBN: 144936389X

Category: Computers

Page: 408

View: 217

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 Analytics for Traditional Chinese Medicine Research

Foreword. Facing the advances of recent science and technology developed in
modern medical practice, experts of traditional Chinese medicine (TCM) have
made efforts to published peer-reviewed papers to produce an enormous amount
of ...

Author: Josiah Poon

Publisher: Springer Science & Business Media

ISBN: 331903801X

Category: Computers

Page: 248

View: 267

This contributed volume explores how data mining, machine learning, and similar statistical techniques can analyze the types of problems arising from Traditional Chinese Medicine (TCM) research. The book focuses on the study of clinical data and the analysis of herbal data. Challenges addressed include diagnosis, prescription analysis, ingredient discoveries, network based mechanism deciphering, pattern-activity relationships, and medical informatics. Each author demonstrates how they made use of machine learning, data mining, statistics and other analytic techniques to resolve their research challenges, how successful if these techniques were applied, any insight noted and how these insights define the most appropriate future work to be carried out. Readers are given an opportunity to understand the complexity of diagnosis and treatment decision, the difficulty of modeling of efficacy in terms of herbs, the identification of constituent compounds in an herb, the relationship between these compounds and biological outcome so that evidence-based predictions can be made. Drawing on a wide range of experienced contributors, Data Analytics for Traditional Chinese Medicine Research is a valuable reference for professionals and researchers working in health informatics and data mining. The techniques are also useful for biostatisticians and health practitioners interested in traditional medicine and data analytics.