Advertisement
| data science masters salary: 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 salary: An Introduction to Data Science Jeffrey S. Saltz, Jeffrey M. Stanton, 2017-08-25 An Introduction to Data Science is an easy-to-read data science textbook for those with no prior coding knowledge. It features exercises at the end of each chapter, author-generated tables and visualizations, and R code examples throughout. |
| data science masters salary: Closing the Analytics Talent Gap Jennifer Priestley, Robert McGrath, 2021-05-03 How can we recruit out of your program? We have a project – how do we reach out to your students? If we do research together who owns it? We have employees who need to upskill in analytics – can you help me with that? How much does all of this cost? Managers and executives are increasingly asking university professors such questions as they deal with a critical shortage of skilled data analysts. At the same time, academics are asking such questions as: How can I bring a real analytical project in the classroom? How can I get real data to help my students develop the skills necessary to be a data scientist? Is what I am teaching in the classroom aligned with the demands of the market for analytical talent? After spending several years answering almost daily e-mails and telephone calls from business managers asking for staffing help and aiding fellow academics with their analytics teaching needs, Dr. Jennifer Priestley of Kennesaw State University and Dr. Robert McGrath of the University of New Hampshire wrote Closing the Analytics Talent Gap: An Executive’s Guide to Working with Universities. The book builds a bridge between university analytics programs and business organizations. It promotes a dialog that enables executives to learn how universities can help them find strategically important personnel and universities to learn how they can develop and educate this personnel. Organizations are facing previously unforeseen challenges related to the translation of massive amounts of data – structured and unstructured, static and in-motion, voice, text, and image – into information to solve current challenges and anticipate new ones. The advent of analytics and data science also presents universities with unforeseen challenges of providing learning through application. This book helps both organizations with finding data natives and universities with educating students to develop the facility to work in a multi-faceted and complex data environment. . |
| data science masters salary: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
| data science masters salary: Analytics and Knowledge Management Suliman Hawamdeh, Hsia-Ching Chang, 2018-08-06 The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms. |
| data science masters salary: Data Scientist Diploma (master's level) - City of London College of Economics - 6 months - 100% online / self-paced City of London College of Economics, Overview This diploma course covers all aspects you need to know to become a successful Data Scientist. Content - Getting Started with Data Science - Data Analytic Thinking - Business Problems and Data Science Solutions - Introduction to Predictive Modeling: From Correlation to Supervised Segmentation - Fitting a Model to Data - Overfitting and Its Avoidance - Similarity, Neighbors, and Clusters Decision Analytic Thinking I: What Is a Good Model? - Visualizing Model Performance - Evidence and Probabilities - Representing and Mining Text - Decision Analytic Thinking II: Toward Analytical Engineering - Other Data Science Tasks and Techniques - Data Science and Business Strategy - Machine Learning: Learning from Data with Your Machine. - And much more Duration 6 months Assessment The assessment will take place on the basis of one assignment at the end of the course. Tell us when you feel ready to take the exam and we’ll send you the assignment questions. Study material The study material will be provided in separate files by email / download link. |
| data science masters salary: Science & Engineering Indicators , 1991 |
| data science masters salary: Graduate STEM Education for the 21st Century National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Board on Higher Education and Workforce, Committee on Revitalizing Graduate STEM Education for the 21st Century, 2018-09-21 The U.S. system of graduate education in science, technology, engineering, and mathematics (STEM) has served the nation and its science and engineering enterprise extremely well. Over the course of their education, graduate students become involved in advancing the frontiers of discovery, as well as in making significant contributions to the growth of the U.S. economy, its national security, and the health and well-being of its people. However, continuous, dramatic innovations in research methods and technologies, changes in the nature and availability of work, shifts in demographics, and expansions in the scope of occupations needing STEM expertise raise questions about how well the current STEM graduate education system is meeting the full array of 21st century needs. Indeed, recent surveys of employers and graduates and studies of graduate education suggest that many graduate programs do not adequately prepare students to translate their knowledge into impact in multiple careers. Graduate STEM Education for the 21st Century examines the current state of U.S. graduate STEM education. This report explores how the system might best respond to ongoing developments in the conduct of research on evidence-based teaching practices and in the needs and interests of its students and the broader society it seeks to serve. This will be an essential resource for the primary stakeholders in the U.S. STEM enterprise, including federal and state policymakers, public and private funders, institutions of higher education, their administrators and faculty, leaders in business and industry, and the students the system is intended to educate. |
| data science masters salary: Data Science Jobs Ann Rajaram, 2019-05-20 Land a high-paying $$$ DataScience job in 90 days or less! This book is the perfect guide for you, if you fall into any of these categories: * Looking to start a career in data science, but unsure where to start. * Tired of applying to dozens of jobs without getting a positive response and/or final job offer .* You recently completed a masters degree or bootcamp and need to quickly find a job. * Are you an experienced tech professional, but looking to pivot into analytics to boost your salary potential. The book will teach you proven successful strategies on: * Winning Profiles Turbocharge your resume and LinkedIn profile and start receiving interview calls from hiring managers. Let JOBS CHASE YOU, instead of the other way around! * LinkedIn - A dedicated chapter on LinkedIn that teaches you some creative (and SECRET) ways to leverage the site and identify high-paying jobs with low competition. * Niche sites - A full list of niche job boards that other candidates have overlooked. These sites have high-$ jobs but lesser competition than the popular job search sites. Upwork - Contrary to popular opinion, Upwork can help you make $$$ in data science jobs. Learn proven techniques to help you bag contracts and start earning, as quickly as next week. * 100+ interview questions asked in real-life data scientist interviews. * Other learner resources and much more...Unlike most job search books that are written by recruiters or professors, this book is written by a senior data science professional, who rose quickly from analyst to managerial roles. She has attended interviews of her own, and knows clearly the frustrations (and at times, hopelessness) of the job search process. Author is a seasoned analytics professional who has worked in Top Firms like NASDAQ, BlackRock, etc. The systems in this book have successfully helped dozens of job seekers and will work effectively for you too! Read on to launch your dream career! Note, this book is deliberately kept short and precise, so you can quickly read through and start applying these principles, instead of sifting through 500 pages of fluff. |
| data science masters salary: Conceptual Statistics for Beginners Isadore Newman, Carole Newman, 1994 This new edition emphasizes and facilitates the conceptual understanding of statistics and statistical concepts for the purpose of reading and accurately interpreting research literature. The use of hand calculators is deemphasized. Instead, computer example setups are supplied for SPSS and SAS. |
| data science masters salary: Studies in Scientific and Engineering Manpower Melville S. Green, 1963 |
| data science masters salary: Principles of Financial Engineering Robert Kosowski, Salih N. Neftci, 2014-11-26 Principles of Financial Engineering, Third Edition, is a highly acclaimed text on the fast-paced and complex subject of financial engineering. This updated edition describes the engineering elements of financial engineering instead of the mathematics underlying it. It shows how to use financial tools to accomplish a goal rather than describing the tools themselves. It lays emphasis on the engineering aspects of derivatives (how to create them) rather than their pricing (how they act) in relation to other instruments, the financial markets, and financial market practices. This volume explains ways to create financial tools and how the tools work together to achieve specific goals. Applications are illustrated using real-world examples. It presents three new chapters on financial engineering in topics ranging from commodity markets to financial engineering applications in hedge fund strategies, correlation swaps, structural models of default, capital structure arbitrage, contingent convertibles, and how to incorporate counterparty risk into derivatives pricing. Poised midway between intuition, actual events, and financial mathematics, this book can be used to solve problems in risk management, taxation, regulation, and above all, pricing. A solutions manual enhances the text by presenting additional cases and solutions to exercises. This latest edition of Principles of Financial Engineering is ideal for financial engineers, quantitative analysts in banks and investment houses, and other financial industry professionals. It is also highly recommended to graduate students in financial engineering and financial mathematics programs. - The Third Edition presents three new chapters on financial engineering in commodity markets, financial engineering applications in hedge fund strategies, correlation swaps, structural models of default, capital structure arbitrage, contingent convertibles and how to incorporate counterparty risk into derivatives pricing, among other topics - Additions, clarifications, and illustrations throughout the volume show these instruments at work instead of explaining how they should act - The solutions manual enhances the text by presenting additional cases and solutions to exercises |
| data science masters salary: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| data science masters salary: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-24 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| data science masters salary: How Data Science Is Transforming Health Care Tim O'Reilly, Mike Loukides, Julie Steele, Colin Hill, 2012-08-24 In the early days of the 20th century, department store magnate JohnWanamaker famously said, I know that half of my advertising doesn'twork. The problem is that I don't know which half. That remainedbasically true until Google transformed advertising with AdSense basedon new uses of data and analysis. The same might be said about healthcare and it's poised to go through a similar transformation as newtools, techniques, and data sources come on line. Soon we'll makepolicy and resource decisions based on much better understanding ofwhat leads to the best outcomes, and we'll make medical decisionsbased on a patient's specific biology. The result will be betterhealth at less cost. This paper explores how data analysis will help us structure thebusiness of health care more effectively around outcomes, and how itwill transform the practice of medicine by personalizing for eachspecific patient. |
| data science masters salary: 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 salary: Public Service Marc Holzer, 2018-03-05 This volume includes perspectives on public service selected from six decades of major public administration journals. Recurring themes include: motivations to enter the public service, positive and negative images of public servants and of government, conflicts between loyalty to the organization and loyalty to the public, morale, burnout, and turnover. The volume also includes cross-national analyses of the public service in other systems, proposals for rethinking public service systems, and questions as to the future of the public service. It recaptures a long, continuing debate as to the health of the public service, and in so doing suggests agendas for university research and administrative action. |
| data science masters salary: Women, Minorities, and Persons with Disabilities in Science and Engineering , 1994 |
| data science masters salary: Analyzing the Analyzers Harlan Harris, Sean Murphy, Marck Vaisman, 2013-06-10 Despite the excitement around data science, big data, and analytics, the ambiguity of these terms has led to poor communication between data scientists and organizations seeking their help. In this report, authors Harlan Harris, Sean Murphy, and Marck Vaisman examine their survey of several hundred data science practitioners in mid-2012, when they asked respondents how they viewed their skills, careers, and experiences with prospective employers. The results are striking. Based on the survey data, the authors found that data scientists today can be clustered into four subgroups, each with a different mix of skillsets. Their purpose is to identify a new, more precise vocabulary for data science roles, teams, and career paths. This report describes: Four data scientist clusters: Data Businesspeople, Data Creatives, Data Developers, and Data Researchers Cases in miscommunication between data scientists and organizations looking to hire Why T-shaped data scientists have an advantage in breadth and depth of skills How organizations can apply the survey results to identify, train, integrate, team up, and promote data scientists |
| data science masters salary: 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 salary: Silicon Valley for Foreigners Reinaldo Normand, 2017-06-03 Written by a San Francisco based, foreign born entrepreneur, this book offers a unique perspective to decode the business etiquette and cultural traits of the Silicon Valley ecosystem. Recommended for entrepreneurs, executives, students and investors living outside the San Francisco Bay Area and interested in startups and innovation. |
| data science masters salary: Staff Report United States. Department of Commerce, 1963 |
| data science masters salary: Classified Pay Increases United States. Congress. House. Committee on Post Office and Civil Service, 1957 |
| data science masters salary: 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 salary: Learning How to Learn Barbara Oakley, PhD, Terrence Sejnowski, PhD, Alistair McConville, 2018-08-07 A surprisingly simple way for students to master any subject--based on one of the world's most popular online courses and the bestselling book A Mind for Numbers A Mind for Numbers and its wildly popular online companion course Learning How to Learn have empowered more than two million learners of all ages from around the world to master subjects that they once struggled with. Fans often wish they'd discovered these learning strategies earlier and ask how they can help their kids master these skills as well. Now in this new book for kids and teens, the authors reveal how to make the most of time spent studying. We all have the tools to learn what might not seem to come naturally to us at first--the secret is to understand how the brain works so we can unlock its power. This book explains: Why sometimes letting your mind wander is an important part of the learning process How to avoid rut think in order to think outside the box Why having a poor memory can be a good thing The value of metaphors in developing understanding A simple, yet powerful, way to stop procrastinating Filled with illustrations, application questions, and exercises, this book makes learning easy and fun. |
| data science masters salary: Virtual Machines James Edward Smith, Ravi Nair, 2005-06-03 In this text, Smith and Nair take a new approach by examining virtual machines as a unified discipline and pulling together cross-cutting technologies. Topics include instruction set emulation, dynamic program translation and optimization, high level virtual machines (including Java and CLI), and system virtual machines for both single-user systems and servers. |
| data science masters salary: Visualize This Nathan Yau, 2011-06-13 Practical data design tips from a data visualization expert of the modern age Data doesn't decrease; it is ever-increasing and can be overwhelming to organize in a way that makes sense to its intended audience. Wouldn't it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks to the creative genius of Nathan Yau, we can. With this full-color book, data visualization guru and author Nathan Yau uses step-by-step tutorials to show you how to visualize and tell stories with data. He explains how to gather, parse, and format data and then design high quality graphics that help you explore and present patterns, outliers, and relationships. Presents a unique approach to visualizing and telling stories with data, from a data visualization expert and the creator of flowingdata.com, Nathan Yau Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design to find meaning in the numbers Details tools that can be used to visualize data-native graphics for the Web, such as ActionScript, Flash libraries, PHP, and JavaScript and tools to design graphics for print, such as R and Illustrator Contains numerous examples and descriptions of patterns and outliers and explains how to show them Visualize This demonstrates how to explain data visually so that you can present your information in a way that is easy to understand and appealing. |
| data science masters salary: Data Mining and Predictive Analytics Daniel T. Larose, 2015-02-19 Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives. |
| data science masters salary: The Great Cloud Migration Michael C. Daconta, 2013 - Learn how to migrate your applications to the cloud! - Learn how to overcome your senior management's concerns about Cloud Security and Interoperability! - Learn how to explain cloud computing, big data and linked data to your organization! - Learn how to develop a robust Cloud Implementation Strategy! - Learn how a Technical Cloud Broker can ease your migration to the cloud! This book will answer the key questions that every organization is asking about emerging technologies like Cloud Computing, Big Data and Linked Data. Written by a seasoned expert and author/co-author of 11 other technical books, this book deftly guides you with real-world experience, case studies, illustrative diagrams and in-depth analysis. * How do you migrate your software applications to the cloud? This book is your definitive guide to migrating applications to the cloud! It explains all the options, tradeoffs, challenges and obstacles to the migration. It provides a migration lifecycle and process you can follow to migrate each application. It provides in-depth case studies: an Infrastructure-as-a-Service case study and a Platform-as-a-Service case study. It covers the difference between application migration and data migration to the cloud and walks you through how to do both well. It covers migration to all the major cloud providers to include Amazon Web Services (AWS), Google AppEngine and Microsoft Azure. * How do you develop a sound implementation strategy for the migration to the cloud? This book leverages Mr. Daconta's 25 years of leadership experience, from the Military to Corporate Executive teams to the Office of the CIO in the Department of Homeland Security, to guide you through the development of a practical and sound implementation strategy. The book's Triple-A Strategy: Assessment, Architecture then Action is must reading for every project lead and IT manager! * This book covers twenty migration scenarios! Application and data migration to the cloud |
| data science masters salary: Social Work Salaries Ralph Gibney Hurlin, 1926 |
| data science masters salary: Careers in Information Science Louise Schultz, 1963 Presents copy for use as a reference brochure and a giveaway sheet to be distributed to guidance counselors to help them direct young people into the growing field of Information Science. Sets forth that Information Science is concerned with the properties, behavior, and flow of information. Describes how it is used, both by individuals and in large systems. Discusses the opportunities in Information Science and outlines three relatively different career areas: (1) Special Librarianship; (2) Literature Analysis; and (3) Information System Design. Details an educational program appropriate for participation in these career areas. Concludes that Information Science is a new but rapidly growing field pushing the frontiers of human knowledge and, thus, contributing to human well-being and progress. (Author). |
| data science masters salary: 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 salary: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| data science masters salary: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| data science masters salary: Bioinformatics For Dummies Jean-Michel Claverie, Cedric Notredame, 2011-02-10 Were you always curious about biology but were afraid to sit through long hours of dense reading? Did you like the subject when you were in high school but had other plans after you graduated? Now you can explore the human genome and analyze DNA without ever leaving your desktop! Bioinformatics For Dummies is packed with valuable information that introduces you to this exciting new discipline. This easy-to-follow guide leads you step by step through every bioinformatics task that can be done over the Internet. Forget long equations, computer-geek gibberish, and installing bulky programs that slow down your computer. You’ll be amazed at all the things you can accomplish just by logging on and following these trusty directions. You get the tools you need to: Analyze all types of sequences Use all types of databases Work with DNA and protein sequences Conduct similarity searches Build a multiple sequence alignment Edit and publish alignments Visualize protein 3-D structures Construct phylogenetic trees This up-to-date second edition includes newly created and popular databases and Internet programs as well as multiple new genomes. It provides tips for using servers and places to seek resources to find out about what’s going on in the bioinformatics world. Bioinformatics For Dummies will show you how to get the most out of your PC and the right Web tools so you'll be searching databases and analyzing sequences like a pro! |
| data science masters salary: 101 Careers in Mathematics: Fourth Edition Deanna Haunsperger, Robert Thompson, 2019-09-24 What can you do with a degree in math? This book addresses this question with 125 career profiles written by people with degrees and backgrounds in mathematics. With job titles ranging from sports analyst to science writer to inventory specialist to CEO, the volume provides ample evidence that one really can do nearly anything with a degree in mathematics. These professionals share how their mathematical education shaped their career choices and how mathematics, or the skills acquired in a mathematics education, is used in their daily work. The degrees earned by the authors profiled here are a good mix of bachelors, masters, and PhDs. With 114 completely new profiles since the third edition, the careers featured within accurately reflect current trends in the job market. College mathematics faculty, high school teachers, and career counselors will all find this a useful resource. Career centers, mathematics departments, and student lounges should have a copy available for student browsing. In addition to the career profiles, the volume contains essays from career counseling professionals on the topics of job-searching, interviewing, and applying to graduate school. |
| data science masters salary: Introduction to Bioinformatics Arthur M. Lesk, 2019 Lesk provides an accessible and thorough introduction to a subject which is becoming a fundamental part of biological science today. The text generates an understanding of the biological background of bioinformatics. |
| data science masters salary: 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 salary: Science Indicators , 1978 |
| data science masters salary: 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 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 …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
