| data science masters gpa: Machine Learning Bookcamp Alexey Grigorev, 2021-11-23 The only way to learn is to practice! In Machine Learning Bookcamp, you''ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you''ve learned in previous chapters. By the end of the bookcamp, you''ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technology Machine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that''s exactly what you''ll be doing in Machine Learning Bookcamp. about the book In Machine Learning Bookcamp you''ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you''ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You''ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you''re done working through these fun and informative projects, you''ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems. what''s inside Code fundamental ML algorithms from scratch Collect and clean data for training models Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images and text Deploy ML models to a production-ready environment about the reader For readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning. |
| data science masters gpa: Enhancing Effective Instruction and Learning Using Assessment Data Hong Jiao, Robert W. Lissitz, 2021 This book introduces theories and practices for using assessment data to enhance learning and instruction. Topics include reshaping the homework review process, iterative learning engineering, learning progressions, learning maps, score report designing, the use of psychosocial data, and the combination of adaptive testing and adaptive learning. In addition, studies proposing new methods and strategies, technical details about the collection and maintenance of process data, and examples illustrating proposed methods and/or software are included. Chapter 1, 4, 6, 8, and 9 discuss how to make valid interpretations of results and/or achieve more efficient instructions from various sources of data. Chapter 3 and 7 propose and evaluate new methods to promote students' learning by using evidence-based iterative learning engineering and supporting the teachers' use of assessment data, respectively. Chapter 2 provides technical details on the collection, storage, and security protection of process data. Chapter 5 introduces software for automating some aspects of developmental education and the use of predictive modeling. Chapter 10 describes the barriers to using psychosocial data for formative assessment purposes. Chapter 11 describes a conceptual framework for adaptive learning and testing and gives an example of a functional learning and assessment system. In summary, the book includes comprehensive perspectives of the recent development and challenges of using test data for formative assessment purposes. The chapters provide innovative theoretical frameworks, new perspectives on the use of data with technology, and how to build new methods based on existing theories. This book is a useful resource to researchers who are interested in using data and technology to inform decision making, facilitate instructional utility, and achieve better learning outcomes-- |
| data science masters gpa: Statistics, Concepts and Controversies David S. Moore, 2012-11-09 No textbook communicates the basics of statistical analysis to liberal arts students as effectively as the bestselling Statistics: Concepts and Controversies (SCC). And no text makes it easier for these students to understand and talk about statistical claims they encounter in commercials, campaigns, the media, sports, and elsewhere in their lives. The new edition offers SCC’s signature combination of engaging cases, real-life examples and exercises, helpful pedagogy, rich full-color design, and innovative media learning tools, all significantly updated. |
| data science masters gpa: GMAT Foundations of Verbal Manhattan Prep, 2020-01-07 Developed for test-takers who need a refresher, Manhattan Prep's GMAT Foundations of Verbal provides a user-friendly review of basic verbal concepts crucial for GMAT success. Written by active instructors with 99th-percentile scores, GMAT Foundations of Verbal is designed to help students, particularly ESL students, who struggle with the basics of the verbal section of the GMAT. The book comes with robust online resources, including a practice test, a question bank and interactive lessons. Designed to be user-friendly for all students, GMAT Foundations of Verbal provides: Review of foundational grammar such as parts of speech and sentence structure Strategies for tackling the three verbal question types—Sentence Correction, Critical Reasoning, and Reading Comprehension Easy-to-follow examples and comprehensive explanations GMAT Foundations of Verbal is an invaluable resource for any student who wants to cement their understanding and build their basic verbal skills for the GMAT. |
| data science masters gpa: The Data Science Handbook Field Cady, 2017-02-28 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. |
| data science masters gpa: Reinventing Rural Gregory M. Fulkerson, Alexander R. Thomas, 2016-10-19 Reinventing Rural is a collection of original research papers that examine the ways in which rural people and places are changing in the context of an urbanizing world. This includes exploring the role of the environment, the economy, and related issues such as tourism. While traditionally relying on primary sector work in agriculture, mining, natural resources, and the like, rural areas are finding new ways to sustain themselves. This involves a new emphasis on environmental protection, as one important strategy has been to capitalize on natural amenities to attract residents and tourists. Beyond improvements to the economy are general improvements to the quality-of-life in rural communities. Consistent with this, the volume focuses on the two cornerstones of education and health, considering current challenges and offering ideas for reinventing rural quality-of-life. |
| data science masters gpa: 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 masters gpa: GMAT Official Guide Verbal Review 2022 GMAC (Graduate Management Admission Council), 2021-06-16 Add over 340 verbal practice questions to your prep. Designed by the makers of the GMAT™ exam. Your official source of real GMAT questions from past exams. Set yourself up for success with extra practice on the verbal section of the GMAT exam. Study with over 340 practice questions not included in GMAT™ Official Guide 2022: Book & Online Question Bank! Review answer explanations to help improve your performance. GMAT practice questions are organized by difficulty level: easy, medium and hard. Start at the beginning and work your way up to the hard questions as you build upon your knowledge. All practice questions are from past GMAT exams. The GMAT™ Official Guide Verbal Review 2022: Book + Online Question Bank provides 3 ways to study: Book: Know what to expect on the GMAT exam Learn the exam structure with an introductory review chapter followed by 25 practice questions. Review common formulas and concepts using quick reference sheets. Master reading comprehension and critical reasoning with over 340 practice questions from past GMAT exams, organized by difficulty level. GMAT Online Prep Tools: Focus your studying – Bonus: included with purchase! Practice online with the same questions from the book. Create custom practice sets by difficulty level and by fundamental skill. Track your progress using performance metrics. Prepare for exam day by timing your practice in exam mode. Test your knowledge of key concepts with flashcards. Prepare with the Online Question Bank, which includes online-exclusive questions filterable by difficulty level, question type, fundamental skills, and more. Study anytime, anywhere with the Mobile App: review and reattempt practice sets to improve performance in study or exam mode. Mobile App: Your GMAT prep on the go Study offline after downloading the question sets. Sync between devices. Start on your phone, finish on your computer. Add GMAT™ Official Guide Verbal Review 2022: Book + Online Question Bank to your GMAT prep; the official source of practice questions from past GMAT exams. This product includes a print book with a unique access code to the Online Question Bank and Mobile App. |
| data science masters gpa: Large-Scale Data Analytics Aris Gkoulalas-Divanis, Abderrahim Labbi, 2014-01-08 This edited book collects state-of-the-art research related to large-scale data analytics that has been accomplished over the last few years. This is among the first books devoted to this important area based on contributions from diverse scientific areas such as databases, data mining, supercomputing, hardware architecture, data visualization, statistics, and privacy. There is increasing need for new approaches and technologies that can analyze and synthesize very large amounts of data, in the order of petabytes, that are generated by massively distributed data sources. This requires new distributed architectures for data analysis. Additionally, the heterogeneity of such sources imposes significant challenges for the efficient analysis of the data under numerous constraints, including consistent data integration, data homogenization and scaling, privacy and security preservation. The authors also broaden reader understanding of emerging real-world applications in domains such as customer behavior modeling, graph mining, telecommunications, cyber-security, and social network analysis, all of which impose extra requirements for large-scale data analysis. Large-Scale Data Analytics is organized in 8 chapters, each providing a survey of an important direction of large-scale data analytics or individual results of the emerging research in the field. The book presents key recent research that will help shape the future of large-scale data analytics, leading the way to the design of new approaches and technologies that can analyze and synthesize very large amounts of heterogeneous data. Students, researchers, professionals and practitioners will find this book an authoritative and comprehensive resource. |
| data science masters gpa: 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 masters gpa: The Best Business Schools' Admissions Secrets Chioma Isiadinso M.Ed., 2014-08-05 The top secrets to getting into the best MBA programs, from a leading industry expert Top MBA programs reject more than 80 percent of their applicants, but author Chioma Isiadinso's admissions consulting firm has successfully guided 90 percent of her students into the best business schools around the world. As a former Admissions Board Member, Isiadinso offers insider tips and strategies to help applicants get into the school of their choice by building and promoting their personal brand. This revised and updated edition now offers: the do's and don'ts of social media networking sample admissions essays that worked an international perspective for global admissions appeal |
| data science masters gpa: Modern Data Analysis Robert L. Launer, Andrew F. Siegel, 2014-05-12 Modern Data Analysis contains the proceedings of a Workshop on Modern Data Analysis held in Raleigh, North Carolina, on June 2-4, 1980 under the auspices of the United States Army Research Office. The papers review theories and methods of data analysis and cover topics ranging from single and multiple quantile-quantile (Q-Q) plotting procedures to biplot display and pencil-and-paper exploratory data analysis methods. Projection pursuit methods for data analysis are also discussed. Comprised of nine chapters, this book begins with an introduction to styles of data analysis techniques, followed by an analysis of single and multiple Q-Q plotting procedures. Problems involving extreme-value data and the behavior of sample averages are considered. Subsequent chapters deal with the use of smelting in guiding re-expression; geometric data analysis; and influence functions and regression diagnostics. The final chapter examines the use and interpretation of robust analysis of variance for the general non-full-rank linear model. The procedures are described in terms of their mathematical structure, which leads to efficient computational algorithms. This monograph should be of interest to mathematicians and statisticians. |
| data science masters gpa: 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 masters gpa: Artificial Intelligence for Computer Games Pedro Antonio González-Calero, Marco Antonio Gómez-Martín, 2011-03-01 The book presents some of the most relevant results from academia in the area of Artificial Intelligence for games. It emphasizes well theoretically supported work supported by developed prototypes, which should lead into integration of academic AI techniques into current electronic entertainment games. The book elaborates on the main results produced in Academia within the last 10 years regarding all aspects of Artificial Intelligence for games, including pathfinding, decision making, and learning. A general theme of the book is the coverage of techniques for facilitating the construction of flexible not prescripted AI for agents in games. Regarding pathfinding, the book includes new techniques for implementing real-time search methods that improve the results obtained through AI, as well as techniques for learning pathfinding behavior by observing actual players. Regarding decision making, the book describes new techniques for authoring tools that facilitate the construction by game designers (typically nonprogrammers) of behavior controlling software, by reusing patterns or actual cases of past behavior. Additionally, the book will cover a number of approaches proposed for extending the essentially pre-scripted nature of current commercial videogames AI into a more interactive form of narrative, where the story emerges from the interaction with the player. Some of those approaches rely on a layered architecture for the character AI, including beliefs, intentions and emotions, taking ideas from research on agent systems. The book also includes chapters on techniques for automatically or semiautomatically learning complex behavior from recorded traces of human or automatic players using different combinations of reinforcement learning, case-based reasoning, neural networks and genetic algorithms. |
| data science masters gpa: Getting What You Came For Robert Peters, 2023-08-29 Is graduate school right for you? Should you get a master's or a Ph.D.? How can you choose the best possible school? This classic guide helps students answer these vital questions and much more. It will also help graduate students finish in less time, for less money, and with less trouble. Based on interviews with career counselors, graduate students, and professors, Getting What You Came For is packed with real-life experiences. It has all the advice a student will need not only to survive but to thrive in graduate school, including: instructions on applying to school and for financial aid; how to excel on qualifying exams; how to manage academic politics—including hostile professors; and how to write and defend a top-notch thesis. Most important, it shows you how to land a job when you graduate. |
| data science masters gpa: Real Education Charles Murray, 2009-08-25 The most talked-about education book this semester. —New York Times From the author of Coming Apart, and based on a series of controversial Wall Street Journal op-eds, this landmark manifesto gives voice to what everyone knows about talent, ability, and intelligence but no one wants to admit. With four truths as his framework, Charles Murray, the bestselling coauthor of The Bell Curve, sweeps away the hypocrisy, wishful thinking, and upside-down priorities that grip America’s educational establishment. •Ability varies. Children differ in their ability to learn, but America’s educational system does its best to ignore this. •Half of the children are below average. Many children cannot learn more than rudimentary reading and math. Yet decades of policies have required schools to divert resources to unattainable goals. •Too many people are going to college. Only a fraction of students struggling to get a degree can profit from education at the college level. •America’s future depends on how we educate the academically gifted. It is time to start thinking about the kind of education needed by the young people who will run the country. |
| data science masters gpa: Fundamentals of Statistical Inference , 1977 |
| data science masters gpa: Machine Learning: Theory and Applications , 2013-05-16 Statistical learning and analysis techniques have become extremely important today, given the tremendous growth in the size of heterogeneous data collections and the ability to process it even from physically distant locations. Recent advances made in the field of machine learning provide a strong framework for robust learning from the diverse corpora and continue to impact a variety of research problems across multiple scientific disciplines. The aim of this handbook is to familiarize beginners as well as experts with some of the recent techniques in this field.The Handbook is divided in two sections: Theory and Applications, covering machine learning, data analytics, biometrics, document recognition and security. - Very relevant to current research challenges faced in various fields - Self-contained reference to machine learning - Emphasis on applications-oriented techniques |
| data science masters gpa: Introduction to Mathematical Statistics and Its Applications Richard J. Larsen, Morris L. Marx, 2013-08-28 Noted for its integration of real-world data and case studies, this text offers sound coverage of the theoretical aspects of mathematical statistics. The authors demonstrate how and when to use statistical methods, while reinforcing the calculus that students have mastered in previous courses. Throughout the 5th Edition, the authors have added and updated examples and case studies, while also refining existing features that show a clear path from theory to practice. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed. |
| data science masters gpa: Museums and Digital Culture Tula Giannini, Jonathan P. Bowen, 2019-05-06 This book explores how digital culture is transforming museums in the 21st century. Offering a corpus of new evidence for readers to explore, the authors trace the digital evolution of the museum and that of their audiences, now fully immersed in digital life, from the Internet to home and work. In a world where life in code and digits has redefined human information behavior and dominates daily activity and communication, ubiquitous use of digital tools and technology is radically changing the social contexts and purposes of museum exhibitions and collections, the work of museum professionals and the expectations of visitors, real and virtual. Moving beyond their walls, with local and global communities, museums are evolving into highly dynamic, socially aware and relevant institutions as their connections to the global digital ecosystem are strengthened. As they adopt a visitor-centered model and design visitor experiences, their priorities shift to engage audiences, convey digital collections, and tell stories through exhibitions. This is all part of crafting a dynamic and innovative museum identity of the future, made whole by seamless integration with digital culture, digital thinking, aesthetics, seeing and hearing, where visitors are welcomed participants. The international and interdisciplinary chapter contributors include digital artists, academics, and museum professionals. In themed parts the chapters present varied evidence-based research and case studies on museum theory, philosophy, collections, exhibitions, libraries, digital art and digital future, to bring new insights and perspectives, designed to inspire readers. Enjoy the journey! |
| data science masters gpa: Reimagining Rural Gregory M. Fulkerson, Alexander R. Thomas, 2016-06-20 Reimagining Rural: Urbanormative Portrayals of Rural Life examines the ways in which rural people and places are being portrayed by popular television, reality television, film, literature, and news media in the United States. It is also an examination of the social processes that reinforce urbanormative standards that normalize urban life and render rural life as something unusual, exotic, or deviant. This includes exploring the role of the media as agenda setting agent, informing people what and how to think about rural life. Further it includes scrutinizing the institution of formal education that promotes a homogenous urban-oriented curriculum, while in the process, marginalizing the unique characteristics of local rural communities. These contributions are some of the only studies of their kind, investigating popular cultural representations of rural life, while providing powerful evidence and unique challenges for an urban society to rethink and reimagine rural life, while confronting the many stereotypes and myths that exist. |
| data science masters gpa: Mathematics for Machine Learning Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, 2020-04-23 The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. |
| data science masters gpa: Urbanormativity Gregory M. Fulkerson, Alexander R. Thomas, 2019-08-01 This book investigates urbanormativity—a concept that privileges urban normalcy and desirability over rural deviance and undesirability. The “reality” section outlines its foundations—urbanization, urban-rural systems, and urban dependency. The “representation” section explores urbanormative culture by considering cultural capital, media, and identity. The last section, “everyday life,” examines urban-rural disparities in law and politics and in life within different communities. It concludes by calling for a rural justice approach that will revalue the rural. |
| data science masters gpa: Studies in Urbanormativity Gregory M. Fulkerson, Alexander R. Thomas, 2013-12-19 The world has been witnessing a long unfolding process of urbanization that not only has altered the structural basis of society in terms of political economy, but has also symbolically relegated rural people and life to a secondary or deviant status through an ideology of urbanormativity. Both structural and cultural changes rooted in urbanization are connected in complex ways to spatial arrangements that can be described in terms of inequality and uneven development. Through a focus on localities, Studies in Urbanormativity: Rural Community in Urban Society examines the implications of urbanization and its corresponding ideology. Urbanormativity justifies rural domination by holding urban life as the standard against which rural forms are compared and deemed to be irregular, inferior, or deviant. Urban production, as conceptualized in this book, is inherently exploitative of rural resources—natural, social, cultural, and symbolic. As this exploitation advances, a wake of entropic conditions is left behind in the forms of degraded landscapes, broken social institutions, and denigrated communities, cultures and identities. Edited by Gregory M. Fulkerson and Alexander R. Thomas, Studies in Urbanormativity engages a topic on which scholars have been surprisingly silent. Designed for advancing theory and practice, the chapters provide new theoretical tools for understanding the complex relationship between the urban and rural. While primarily intended for scholars and practitioners interested in rural life, rural policy, and community development, the insights of this book will also be of interest to scholars studying various forms of cultural and social domination, as well as identity politics. |
| data science masters gpa: 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 masters gpa: Bayesian Methods for Hackers Cameron Davidson-Pilon, 2015-09-30 Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes • Learning the Bayesian “state of mind” and its practical implications • Understanding how computers perform Bayesian inference • Using the PyMC Python library to program Bayesian analyses • Building and debugging models with PyMC • Testing your model’s “goodness of fit” • Opening the “black box” of the Markov Chain Monte Carlo algorithm to see how and why it works • Leveraging the power of the “Law of Large Numbers” • Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning • Using loss functions to measure an estimate’s weaknesses based on your goals and desired outcomes • Selecting appropriate priors and understanding how their influence changes with dataset size • Overcoming the “exploration versus exploitation” dilemma: deciding when “pretty good” is good enough • Using Bayesian inference to improve A/B testing • Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify. |
| data science masters gpa: College Admissions Data Sourcebook Northeast Edition Bound 2010-11 , 2010-09 |
| data science masters gpa: Principles of Data Science Hamid R. Arabnia, Kevin Daimi, Robert Stahlbock, Cristina Soviany, Leonard Heilig, Kai Brüssau, 2020-07-08 This book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data science tools, deep learning, and various data science applications. Researchers can extract and conclude various future ideas and topics that could result in potential publications or thesis. Furthermore, this book contributes to Data Scientists’ preparation and to enhancing their knowledge of the field. The book provides a rich collection of manuscripts in highly regarded data science topics, edited by professors with long experience in the field of data science. Introduces various techniques, methods, and algorithms adopted by Data Science experts Provides a detailed explanation of data science perceptions, reinforced by practical examples Presents a road map of future trends suitable for innovative data science research and practice |
| data science masters gpa: Frontiers in Particle Physics Jean-Marc Gérard, Raymond Gastmans, Jean Iliopoulos, M. Lévy, 2013-12-01 Proceedings of a NATO ASI held in Cargese, France, August 1-13, 1994 |
| data science masters gpa: 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 masters gpa: Colleges That Create Futures Princeton Review, 2016-05-10 KICK-START YOUR CAREER WITH THE RIGHT ON-CAMPUS EXPERIENCE! When it comes to getting the most out of college, the experiences you have outside the classroom are just as important as what you study. Colleges That Create Futures looks beyond the usual “best of” college lists to highlight 50 schools that empower students to discover practical, real-world applications for their talents and interests. The schools in this book feature distinctive research, internship, and hands-on learning programs—all the info you need to help find a college where you can parlay your passion into a successful post-college career. Inside, You'll Find: • In-depth profiles covering career services, internship support, student group activity, alumni satisfaction, noteworthy facilities and programs, and more • Candid assessments of each school’s academics from students, current faculty, and alumni • Unique hands-on learning opportunities for students across majors • Testimonials on career prep from alumni in business, education, law, and much more *************************** What makes Colleges That Create Futures important? You've seen the headlines—lately the news has been full of horror stories about how the college educational system has failed many recent grads who leave school with huge debt, no job prospects, and no experience in the working world. Colleges That Create Futures identifies schools that don't fall into this trap but instead prepare students for successful careers! How are the colleges selected? Schools are selected based on survey results on career services, grad school matriculation, internship support, student group and government activity, alumni activity and salaries, and noteworthy facilities and programs. |
| data science masters gpa: College Admissions Data Sourcebook Northeast Edition Looseleaf 2010-11 , 2010-09 |
| data science masters gpa: Computing Skills for Biologists Stefano Allesina, Madlen Wilmes, 2019-01-15 A concise introduction to key computing skills for biologists While biological data continues to grow exponentially in size and quality, many of today’s biologists are not trained adequately in the computing skills necessary for leveraging this information deluge. In Computing Skills for Biologists, Stefano Allesina and Madlen Wilmes present a valuable toolbox for the effective analysis of biological data. Based on the authors’ experiences teaching scientific computing at the University of Chicago, this textbook emphasizes the automation of repetitive tasks and the construction of pipelines for data organization, analysis, visualization, and publication. Stressing practice rather than theory, the book’s examples and exercises are drawn from actual biological data and solve cogent problems spanning the entire breadth of biological disciplines, including ecology, genetics, microbiology, and molecular biology. Beginners will benefit from the many examples explained step-by-step, while more seasoned researchers will learn how to combine tools to make biological data analysis robust and reproducible. The book uses free software and code that can be run on any platform. Computing Skills for Biologists is ideal for scientists wanting to improve their technical skills and instructors looking to teach the main computing tools essential for biology research in the twenty-first century. Excellent resource for acquiring comprehensive computing skills Both novice and experienced scientists will increase efficiency by building automated and reproducible pipelines for biological data analysis Code examples based on published data spanning the breadth of biological disciplines Detailed solutions provided for exercises in each chapter Extensive companion website |
| data science masters gpa: Gap Year Joseph O'Shea, 2014 The idea of the gap year has taken hold in America. Since its development in Britain nearly fifty years ago, taking time off between secondary school and college has allowed students the opportunity to travel, develop crucial life skills, and grow up, all while doing volunteer work in much-needed parts of the developing world. |
| data science masters gpa: War, Peace, and Security Jacques Fontanel, Manas Chatterji, 2008-10-13 In the name of international and domestic security, billions of dollars are wasted on unproductive military spending in both developed and developing countries, when millions are starving and living without basic human needs. This book contains articles relating to military spending, military industrial establishments, and peace keeping. |
| data science masters gpa: 2012-2013 College Admissions Data Sourcebook Southeast Edition , |
| data science masters gpa: 2010-2011 College Admissions Data Sourcebook West Edition , 2010-09 |
| data science masters gpa: 2012-2013 College Admissions Data Sourcebook Northeast Edition , |
| data science masters gpa: Building Machine Learning Pipelines Hannes Hapke, Catherine Nelson, 2020-07-13 Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques |
| data science masters gpa: 2012-2013 College Admissions Data Sourcebook West Edition , |
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 minimum time …
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, released in …
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 from …
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 barriers …
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 collected, …
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 …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their …
