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| data science major declare: Topics in Biostatistics Walter T. Ambrosius, 2007-07-06 This book presents a multidisciplinary survey of biostatics methods, each illustrated with hands-on examples. It introduces advanced methods in statistics, including how to choose and work with statistical packages. Specific topics of interest include microarray analysis, missing data techniques, power and sample size, statistical methods in genetics. The book is an essential resource for researchers at every level of their career. |
| data science major declare: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 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. |
| data science major declare: Designing User Interfaces With a Data Science Approach Banubakode, Abhijit Narayanrao, Bhutkar, Ganesh Dattatray, Kurniawan, Yohannes, Gosavi, Chhaya Santosh, 2022-03-18 Data science has been playing a vital role in almost all major fields. Many researchers are interested in the development of IT applications, which are user-driven with a focus on issues. This can be addressed using data science. User-driven research and data science have gained much attention from many private, public, and government organizations and research institutions. Designing User Interfaces With a Data Science Approach promotes the inclusion of more diversified users for user-centered designs of applications across domains and analyzes user data with a data science approach for effective and user-friendly user interface designs. It introduces the foundations of advanced topics of human-computer interaction, particularly with user-centered designs and techniques. Covering topics such as artificial neural networks, natural dialog systems, and machine learning, this book is an essential resource for faculty, research scholars, industry professionals, students of higher education, mathematicians, data scientists, interaction designers, visual designers, software engineers, user experience researchers, accessibility engineers, cognitive system engineers, academicians, and libraries. |
| data science major declare: How to Be a High School Superstar Cal Newport, 2010-07-27 Do Less, Live More, Get Accepted What if getting into your reach schools didn’t require four years of excessive A.P. classes, overwhelming activity schedules, and constant stress? In How to Be a High School Superstar, Cal Newport explores the world of relaxed superstars—students who scored spots at the nation’s top colleges by leading uncluttered, low stress, and authentic lives. Drawing from extensive interviews and cutting-edge science, Newport explains the surprising truths behind these superstars’ mixture of happiness and admissions success, including: · Why doing less is the foundation for becoming more impressive. · Why demonstrating passion is meaningless, but being interesting is crucial. · Why accomplishments that are hard to explain are better than accomplishments that are hard to do. These insights are accompanied by step-by-step instructions to help any student adopt the relaxed superstar lifestyle—proving that getting into college doesn’t have to be a chore to survive, but instead can be the reward for living a genuinely interesting life. |
| data science major declare: Curriculum Handbook with General Information Concerning ... for the United States Air Force Academy United States Air Force Academy, 1993 |
| data science major declare: Academic Majors Handbook with General Information ... United States Air Force Academy United States Air Force Academy, 1988 |
| data science major declare: 50 Yale Admission Success Stories Yale Daily News Staff, 2020-03-17 From the students at the Yale Daily News, a book that highlights the essays that got students into Yale University, helping high school seniors get into the school of their choice The competition to get into a top-tier school becomes more and more fierce every year. Parents and students are searching for the best advice, and the final question they ask after joining clubs in high school and keeping the grades up is: How do I write a winning essay? 50 Yale Admission Success Stories and the Essays that Made Them Happen shows college applicants how to do exactly that, showcasing the Common App essays that got students into Yale, in addition to Yale-specific application essays and other supplemental aspects of the Yale application, like short statements and short answers. But this book does more than just show students what kind of essays got college students through the door; it profiles each student who contributed to the collection and puts those essays into context. We meet Edgar Avina, a political science major from Houston who worked odd jobs to support his family, who immigrated from Mexico. Madeleine Bender, a New York City native, is a jack of all trades who writes for the Daily News, plays clarinet for a concert band, and majors in both Classics and Ecology & Evolutionary Biology. These profiles set this book apart from other college essay books, reminding students that in order to write a strong essay, you must be yourself and understand how the university you're applying to will help you make your greatest dreams into a reality. |
| data science major declare: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science major declare: The Data Science Design Manual Steven S. Skiena, 2017-07-01 This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com) |
| data science major declare: Science & Engineering Indicators , 2004 |
| data science major declare: Recent Developments in Data Science and Intelligent Analysis of Information Oleg Chertov, Tymofiy Mylovanov, Yuriy Kondratenko, Janusz Kacprzyk, Vladik Kreinovich, Vadim Stefanuk, 2018-08-04 This book constitutes the proceedings of the XVIII International Conference on Data Science and Intelligent Analysis of Information (ICDSIAI'2018), held in Kiev, Ukraine on June 4-7, 2018. The conference series, which dates back to 2001 when it was known as the Workshop on Intelligent Analysis of Information, was renamed in 2008 to reflect the broadening of its scope and the composition of its organizers and participants. ICDSIAI'2018 brought together a large number of participants from numerous countries in Europe, Asia and the USA. The papers presented addressed novel theoretical developments in methods, algorithms and implementations for the broadly perceived areas of big data mining and intelligent analysis of data and information, representation and processing of uncertainty and fuzziness, including contributions on a range of applications in the fields of decision-making and decision support, economics, education, ecology, law, and various areas of technology. The book is dedicated to the memory of the conference founder, the late Professor Tetiana Taran, an outstanding scientist in the field of artificial intelligence whose research record, vision and personality have greatly contributed to the development of Ukrainian artificial intelligence and computer science. |
| data science major declare: Data-Driven Quality Improvement and Sustainability in Health Care Patricia L. Thomas, PhD, RN, FAAN, FNAP, FACHE, NEA-BC, ACNS-BC, CNL, James L. Harris, PhD, APRN-BC, MBA, CNL, FAAN, Brian J. Collins, BS, MA, 2020-11-19 Data-Driven Quality Improvement and Sustainability in Health Care: An Interprofessional Approach provides nurse leaders and healthcare administrators of all disciplines with a solid understanding of data and how to leverage data to improve outcomes, fuel innovation, and achieve sustained results. It sets the stage by examining the current state of the healthcare landscape; new imperatives to meet policy, regulatory, and consumer demands; and the role of data in administrative and clinical decision-making. It helps the professional identify the methods and tools that support thoughtful and thorough data analysis and offers practical application of data-driven processes that determine performance in healthcare operations, value- and performance-based contracts, and risk contracts. Misuse or inconsistent use of data leads to ineffective and errant decision-making. This text highlights common barriers and pitfalls related to data use and provide strategies for how to avoid these pitfalls. In addition, chapters feature key points, reflection questions, and real-life interprofessional case exemplars to help the professional draw distinctions and apply principles to their own practice. Key Features: Provides nurse leaders and other healthcare administrators with an understanding of the role of data in the current healthcare landscape and how to leverage data to drive innovative and sustainable change Offers frameworks, methodology, and tools to support quality improvement measures Demonstrates the application of data and how it shapes quality and safety initiatives through real-life case exemplars Highlights common barriers and pitfalls related to data use and provide strategies for how to avoid these pitfalls |
| data science major declare: Secure Data Science Bhavani Thuraisingham, Murat Kantarcioglu, Latifur Khan, 2022-04-27 Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area—including healthcare, finance, manufacturing, and marketing—could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science. After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. It also discusses such applications of data science as malware analysis and insider threat detection. Then this book addresses trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing, including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media. This book is a useful resource for researchers, software developers, educators, and managers who want to understand both the high level concepts and the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in secure data science. Furthermore, this book provides numerous references that would be helpful for the reader to get more details about secure data science. |
| data science major declare: Communicating with Data Deborah Nolan, Sara Stoudt, 2021-03-25 Communication is a critical yet often overlooked part of data science. Communicating with Data aims to help students and researchers write about their insights in a way that is both compelling and faithful to the data. General advice on science writing is also provided, including how to distill findings into a story and organize and revise the story, and how to write clearly, concisely, and precisely. This is an excellent resource for students who want to learn how to write about scientific findings, and for instructors who are teaching a science course in communication or a course with a writing component. Communicating with Data consists of five parts. Part I helps the novice learn to write by reading the work of others. Part II delves into the specifics of how to describe data at a level appropriate for publication, create informative and effective visualizations, and communicate an analysis pipeline through well-written, reproducible code. Part III demonstrates how to reduce a data analysis to a compelling story and organize and write the first draft of a technical paper. Part IV addresses revision; this includes advice on writing about statistical findings in a clear and accurate way, general writing advice, and strategies for proof reading and revising. Part V offers advice about communication strategies beyond the page, which include giving talks, building a professional network, and participating in online communities. This book also provides 22 portfolio prompts that extend the guidance and examples in the earlier parts of the book and help writers build their portfolio of data communication. |
| data science major declare: Data Science for COVID-19 Utku Kose, Deepak Gupta, Victor Hugo Costa de Albuquerque, Ashish Khanna, 2021-10-22 Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques for the detection, mitigation, treatment and elimination of the COVID-19 virus. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques, including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes is now possible. - Provides a leading-edge survey of Data Science techniques and methods for research, mitigation and the treatment of the COVID-19 virus - Integrates various Data Science techniques to provide a resource for COVID-19 researchers and clinicians around the world, including the wide variety of impacts the virus is having on societies and medical practice - Presents insights into innovative, data-oriented modeling and predictive techniques from COVID-19 researchers around the world, including geoprocessing and tracking, lab data analysis, and theoretical views on a variety of technical applications - Includes real-world feedback and user experiences from physicians and medical staff from around the world for medical treatment perspectives, public safety policies and impacts, sociological and psychological perspectives, the effects of COVID-19 in agriculture, economies, and education, and insights on future pandemics |
| data science major declare: Pro T-SQL 2012 Programmer's Guide Michael Coles, Scott Shaw, Jay Natarajan, Rudi Bruchez, 2012-11-29 Pro T–SQL 2012 Programmer’s Guide is every developer’s key to making full use of SQL Server 2012’s powerful, built–in Transact–SQL language. Discussing new and existing features, the book takes you on an expert guided tour of Transact–SQL functionality. Fully functioning examples and downloadable source code bring technically accurate and engaging treatment of Transact–SQL into your own hands. Step–by–step explanations ensure clarity, and an advocacy of best–practices will steer you down the road to success. Transact–SQL is the language developers and DBAs use to interact with SQL Server. It’s used for everything from querying data, to writing stored procedures, to managing the database. New features in T-SQL 2012 include full support for window functions, stored sequences, the ability to throw errors, data paging, and more. All these important new features are covered in this book. Developers and DBAs alike can benefit from the expressive power of Transact-SQL, and Pro T-SQL 2012 Programmer's Guide provides the gateway to success in applying this increasingly important database language to everyday business and technical tasks. |
| data science major declare: Making College Work Harry J. Holzer, Sandy Baum, 2017-08-15 Practical solutions for improving higher education opportunities for disadvantaged students Too many disadvantaged college students in America do not complete their coursework or receive any college credential, while others earn degrees or certificates with little labor market value. Large numbers of these students also struggle to pay for college, and some incur debts that they have difficulty repaying. The authors provide a new review of the causes of these problems and offer promising policy solutions. The circumstances affecting disadvantaged students stem both from issues on the individual side, such as weak academic preparation and financial pressures, and from institutional failures. Low-income students disproportionately attend schools that are underfunded and have weak performance incentives, contributing to unsatisfactory outcomes for many students. Some solutions, including better financial aid or academic supports, target individual students. Other solutions, such as stronger linkages between coursework and the labor market and more structured paths through the curriculum, are aimed at institutional reforms. All students, and particularly those from disadvantaged backgrounds, also need better and varied pathways both to college and directly to the job market, beginning in high school. We can improve college outcomes, but must also acknowledge that we must make hard choices and face difficult tradeoffs in the process. While no single policy is guaranteed to greatly improve college and career outcomes, implementing a number of evidence-based policies and programs together has the potential to improve these outcomes substantially. |
| data science major declare: Building Data Science Applications with FastAPI Francois Voron, 2021-10-08 Get well-versed with FastAPI features and best practices for testing, monitoring, and deployment to run high-quality and robust data science applications Key FeaturesCover the concepts of the FastAPI framework, including aspects relating to asynchronous programming, type hinting, and dependency injectionDevelop efficient RESTful APIs for data science with modern PythonBuild, test, and deploy high performing data science and machine learning systems with FastAPIBook Description FastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you'll be able to create fast and reliable data science API backends using practical examples. This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate with databases, implement authentication and integrate machine learning models. Later, you'll cover best practices relating to testing and deployment to run a high-quality and robust application. You'll also be introduced to the extensive ecosystem of Python data science packages. As you progress, you'll learn how to build data science applications in Python using FastAPI. The book also demonstrates how to develop fast and efficient machine learning prediction backends and test them to achieve the best performance. Finally, you'll see how to implement a real-time face detection system using WebSockets and a web browser as a client. By the end of this FastAPI book, you'll have not only learned how to implement Python in data science projects but also how to maintain and design them to meet high programming standards with the help of FastAPI. What you will learnExplore the basics of modern Python and async I/O programmingGet to grips with basic and advanced concepts of the FastAPI frameworkImplement a FastAPI dependency to efficiently run a machine learning modelIntegrate a simple face detection algorithm in a FastAPI backendIntegrate common Python data science libraries in a web backendDeploy a performant and reliable web backend for a data science applicationWho this book is for This Python data science book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python is recommended. |
| data science major declare: Modern Data Science with R Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, 2021-03-31 From a review of the first edition: Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice. |
| data science major declare: Population data science in cardiovascular medicine Victor Chien-Chia Wu, Yu-Sheng Lin, Yu-Tung Huang, Michael Wu, 2023-02-27 |
| data science major declare: Introduction to Scientific Programming with Python Joakim Sundnes, 2020 This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies. |
| data science major declare: Computational Intelligence in Data Science Mieczyslaw Lech Owoc, |
| data science major declare: Data Science and Simulation in Transportation Research Janssens, Davy, 2013-12-31 Given its effective techniques and theories from various sources and fields, data science is playing a vital role in transportation research and the consequences of the inevitable switch to electronic vehicles. This fundamental insight provides a step towards the solution of this important challenge. Data Science and Simulation in Transportation Research highlights entirely new and detailed spatial-temporal micro-simulation methodologies for human mobility and the emerging dynamics of our society. Bringing together novel ideas grounded in big data from various data mining and transportation science sources, this book is an essential tool for professionals, students, and researchers in the fields of transportation research and data mining. |
| data science major declare: Assuring the U.S. Department of Defense a Strong Science, Technology, Engineering, and Mathematics (STEM) Workforce National Research Council, National Academy of Engineering, Policy and Global Affairs, Board on Higher Education and Workforce, Division on Engineering and Physical Sciences, Committee on Science, Technology, Engineering, and Mathematics Workforce Needs for the U.S. Department of Defense and the U.S. Defense Industrial Base, 2012-10-31 The ability of the nation's military to prevail during future conflicts, and to fulfill its humanitarian and other missions, depends on continued advances in the nation's technology base. A workforce with robust Science, Technology, Engineering and Mathematics (STEM) capabilities is critical to sustaining U.S. preeminence. Today, however, the STEM activities of the Department of Defense (DOD) are a small and diminishing part of the nation's overall science and engineering enterprise. Assuring the U.S. Department of Defense a Strong Science, Technology, Engineering, and Mathematics (STEM) Workforce presents five principal recommendations for attracting, retaining, and managing highly qualified STEM talent within the department based on an examination of the current STEM workforce of DOD and the defense industrial base. As outlined in the report, DOD should focus its investments to ensure that STEM competencies in all potentially critical, emerging topical areas are maintained at least at a basic level within the department and its industrial and university bases. |
| data science major declare: City of Intellect Nicholas B. Dirks, 2023-10-31 During his four years as the tenth Chancellor of Berkeley (2013-17), Nicholas B. Dirks was confronted by crises arguably more challenging than those faced by any other college administrator in the contemporary period. This thoughtfully candid book, emerging from deep reflection on his turbulent time in office, offers not just a gripping insider's account of the febrile politics of his time as Berkeley's leader, but also decades of nuanced reflection on the university's true meaning (at its best, to be an aspirational 'city of intellect'). Dirks wrestles with some of the most urgent questions with which educational leaders are presently having to engage: including topics such as free speech and campus safe spaces, the humanities' contested future, and the real cost and value of liberal arts learning. His visionary intervention - part autobiography, part practical manifesto - is a passionate cri de cœur for structural changes in higher education that are both significant and profound. |
| data science major declare: Proceedings American Society for Engineering Education. Conference, 1994 |
| data science major declare: Guide to College Majors 2008 Princeton Review, Princeton Review Publishing Staff, 2005-02 Provides information on over three hundred common college majors, from accounting to zoology, including related fields, prior high school subjects, possible courses of study, and career and salary prospects for graduates. |
| data science major declare: Actuarial Mathematics Harry H. Panjer, American Mathematical Society, 1986 These lecture notes from the 1985 AMS Short Course examine a variety of topics from the contemporary theory of actuarial mathematics. Recent clarification in the concepts of probability and statistics has laid a much richer foundation for this theory. Other factors that have shaped the theory include the continuing advances in computer science, the flourishing mathematical theory of risk, developments in stochastic processes, and recent growth in the theory of finance. In turn, actuarial concepts have been applied to other areas such as biostatistics, demography, economic, and reliability engineering. |
| data science major declare: Crossing the Finish Line William G. Bowen, Matthew M. Chingos, Michael McPherson, 2009-09-08 Why so many of America's public university students are not graduating—and what to do about it The United States has long been a model for accessible, affordable education, as exemplified by the country's public universities. And yet less than 60 percent of the students entering American universities today are graduating. Why is this happening, and what can be done? Crossing the Finish Line provides the most detailed exploration ever of college completion at America's public universities. This groundbreaking book sheds light on such serious issues as dropout rates linked to race, gender, and socioeconomic status. Probing graduation rates at twenty-one flagship public universities and four statewide systems of public higher education, the authors focus on the progress of students in the entering class of 1999—from entry to graduation, transfer, or withdrawal. They examine the effects of parental education, family income, race and gender, high school grades, test scores, financial aid, and characteristics of universities attended (especially their selectivity). The conclusions are compelling: minority students and students from poor families have markedly lower graduation rates—and take longer to earn degrees—even when other variables are taken into account. Noting the strong performance of transfer students and the effects of financial constraints on student retention, the authors call for improved transfer and financial aid policies, and suggest ways of improving the sorting processes that match students to institutions. An outstanding combination of evidence and analysis, Crossing the Finish Line should be read by everyone who cares about the nation's higher education system. |
| data science major declare: Developing Analytic Talent Vincent Granville, 2014-03-24 Learn what it takes to succeed in the the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates. |
| data science major declare: 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 major declare: Barriers and Opportunities for 2-Year and 4-Year STEM Degrees National Academies of Sciences, Engineering, and Medicine, National Academy of Engineering, Policy and Global Affairs, Board on Higher Education and Workforce, Division of Behavioral and Social Sciences and Education, Board on Science Education, Committee on Barriers and Opportunities in Completing 2-Year and 4-Year STEM Degrees, 2016-05-18 Nearly 40 percent of the students entering 2- and 4-year postsecondary institutions indicated their intention to major in science, technology, engineering, and mathematics (STEM) in 2012. But the barriers to students realizing their ambitions are reflected in the fact that about half of those with the intention to earn a STEM bachelor's degree and more than two-thirds intending to earn a STEM associate's degree fail to earn these degrees 4 to 6 years after their initial enrollment. Many of those who do obtain a degree take longer than the advertised length of the programs, thus raising the cost of their education. Are the STEM educational pathways any less efficient than for other fields of study? How might the losses be stemmed and greater efficiencies realized? These questions and others are at the heart of this study. Barriers and Opportunities for 2-Year and 4-Year STEM Degrees reviews research on the roles that people, processes, and institutions play in 2-and 4-year STEM degree production. This study pays special attention to the factors that influence students' decisions to enter, stay in, or leave STEM majorsâ€quality of instruction, grading policies, course sequences, undergraduate learning environments, student supports, co-curricular activities, students' general academic preparedness and competence in science, family background, and governmental and institutional policies that affect STEM educational pathways. Because many students do not take the traditional 4-year path to a STEM undergraduate degree, Barriers and Opportunities describes several other common pathways and also reviews what happens to those who do not complete the journey to a degree. This book describes the major changes in student demographics; how students, view, value, and utilize programs of higher education; and how institutions can adapt to support successful student outcomes. In doing so, Barriers and Opportunities questions whether definitions and characteristics of what constitutes success in STEM should change. As this book explores these issues, it identifies where further research is needed to build a system that works for all students who aspire to STEM degrees. The conclusions of this report lay out the steps that faculty, STEM departments, colleges and universities, professional societies, and others can take to improve STEM education for all students interested in a STEM degree. |
| data science major declare: Successful STEM Mentoring Initiatives for Underrepresented Students Becky Wai-Ling Packard, 2023-07-03 Successful STEM Mentoring Initiatives for Underrepresented College Students is a step-by-step, research-based guide for higher education faculty and administrators who are charged with designing mentoring programs to recruit and retain students from underrepresented groups. Written by an acknowledged expert in the field of STEM mentoring, the book constitutes a virtual consultant that enables readers to diagnose the issues they face, identify priorities, and implement appropriate practices to achieve their goals.The book describes the real and perceived barriers that underrepresented students—to include women, students of color, transfer students, and first-generation college students—encounter when considering enrollment, or participating, in science courses; considers the issues they face at the various transitions in their education, from entering college to declaring a major and moving on to a profession; and sets out the range of mentoring options available to program designers.By posing key questions and using three running case illustrations of common dilemmas, the book walks readers through the process of matching the best design options with the particular needs and resources of their own department or campus. Intentionally brief and to the point, the book is nonetheless a comprehensive guide to the full range mentoring models and best practices, that also covers issues of institutional and departmental climate and teaching methods, and offers insider insights to help designers avoid pitfalls as they create effective, sustainable mentoring initiatives.This guide will assist administrators working on new initiatives to broaden access and improve persistence and graduation in their programs, as well as apply for research grants, by clarifying objectives and identifying the effective evidence-based practices to achieve them. It also provides common conversation-starters for departments to identify obstacles to enrollment and broaden participation. |
| data science major declare: Colleges Worth Your Money Andrew Belasco, Dave Bergman, Michael Trivette, 2024-06-01 Colleges Worth Your Money: A Guide to What America's Top Schools Can Do for You is an invaluable guide for students making the crucial decision of where to attend college when our thinking about higher education is radically changing. At a time when costs are soaring and competition for admission is higher than ever, the college-bound need to know how prospective schools will benefit them both as students and after graduation. Colleges Worth Your Moneyprovides the most up-to-date, accurate, and comprehensive information for gauging the ROI of America’s top schools, including: In-depth profiles of 200 of the top colleges and universities across the U.S.; Over 75 key statistics about each school that cover unique admissions-related data points such as gender-specific acceptance rates, early decision acceptance rates, and five-year admissions trends at each college. The solid facts on career outcomes, including the school’s connections with recruiters, the rate of employment post-graduation, where students land internships, the companies most likely to hire students from a particular school, and much more. Data and commentary on each college’s merit and need-based aid awards, average student debt, and starting salary outcomes. Top Colleges for America’s Top Majors lists highlighting schools that have the best programs in 40+ disciplines. Lists of the “Top Feeder” undergraduate colleges into medical school, law school, tech, journalism, Wall Street, engineering, and more. |
| data science major declare: COVID-19: Integrating Artificial Intelligence, Data Science, Mathematics, Medicine and Public Health, Epidemiology, Neuroscience, Neurorobotics, and Biomedical Science in Pandemic Management, volume II Atefeh Abedini, Reza Lashgari, 2024-02-29 |
| data science major declare: Data Science in Critical Care, An Issue of Critical Care Clinics, E-Book Rishikesan Kamaleswaran, Andre L. Holder, 2023-09-13 In this issue of Critical Care Clinics, guest editors Drs. Rishikesan Kamaleswaran and Andre L. Holder bring their considerable expertise to the topic of Data Science in Critical Care. Data science, the field of study dedicated to the principled extraction of knowledge from complex data, is particularly relevant in the critical care setting. In this issue, top experts in the field cover key topics such as refining our understanding and classification of critical illness using biomarker-based phenotyping; predictive modeling using AI/ML on EHR data; classification and prediction using waveform-based data; creating trustworthy and fair AI systems; and more. - Contains 15 relevant, practice-oriented topics including AI and the imaging revolution; designing living, breathing clinical trials: lessons learned from the COVID-19 pandemic; the patient or the population: knowing the limitations of our data to make smart clinical decisions; weighing the cost vs. benefit of AI in healthcare; and more. - Provides in-depth clinical reviews on data science in critical care, offering actionable insights for clinical practice. - Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize and distill the latest research and practice guidelines to create clinically significant, topic-based reviews. |
| data science major declare: The Best 390 Colleges, 2025 The Princeton Review, Robert Franek, 2024-08-27 NO ONE KNOWS COLLEGES LIKE THE PRINCETON REVIEW! This comprehensive guide to the nation's best colleges provides in-depth profiles on schools, best-of lists by interest, and tons of helpful student-driven details that will help you or your student choose their best-fit colleges! The Princeton Review's college rankings started in 1992 with surveys from 30,000 students. Over 30 years and more than a million student surveys later, we stand by our claim that there is no single “best” college, only the best college for you… and that this is the book that will help you find it! STRAIGHT FROM STUDENTS TO YOU · 390 in-depth school profiles based on candid feedback from 160,000+ students, covering academics, administration, campus life, and financial aid · Insights on unique college character, social scene, and more · Direct quotes from students about their school’s professors, campus culture, career services, and more RANKING LISTS & RATING SCORES · Lists of the top 25 colleges in 50 categories based on students' opinions of academics, campus life, facilities, and much more · Ratings for every school on Financial Aid, Selectivity, and Quality of Life DETAILED ADMISSIONS INFORMATION · The Inside Word on competitive applications, test scores, tuition, and average indebtedness · Comprehensive information on selectivity, freshman profiles, and application deadlines at each school Plus! Free access to 2 full-length practice tests online (1 SAT and 1 ACT) to help you prep for the important admissions-exams part of your admissions journey. |
| data science major declare: Junior-year Science and Mathematics Students by Major Field of Study M. Clemens Johnson, 1958 |
| data science major declare: The Best 385 Colleges, 2020 Edition The Princeton Review, Robert Franek, 2019-09-24 Make sure you’re preparing with the most up-to-date materials! Look for The Princeton Review’s newest edition of this book, The Best 386 Colleges, 2021 Edition (ISBN: 9780525569725, on-sale August 2020). Publisher's Note: Products purchased from third-party sellers are not guaranteed by the publisher for quality or authenticity, and may not include access to online tests or materials included with the original product. |
| data science major declare: The Best 378 Colleges Robert Franek, Princeton Review (Firm), 2013-08 A survey of life on the nation's campuses offers detailed profiles of the best colleges and rankings of colleges in sixty-two different categories, along with a wealth of information and applications tips. |
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 enable a …
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 to …
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 Transnationa…
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 …
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 …
Belmont Forum Adopts Open Data Principles for Environmental Chan…
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 …
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 …
