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| data science master columbia: 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 master columbia: Practical Python Data Wrangling and Data Quality Susan E. McGregor, 2021-12-03 The world around us is full of data that holds unique insights and valuable stories, and this book will help you uncover them. Whether you already work with data or want to learn more about its possibilities, the examples and techniques in this practical book will help you more easily clean, evaluate, and analyze data so that you can generate meaningful insights and compelling visualizations. Complementing foundational concepts with expert advice, author Susan E. McGregor provides the resources you need to extract, evaluate, and analyze a wide variety of data sources and formats, along with the tools to communicate your findings effectively. This book delivers a methodical, jargon-free way for data practitioners at any level, from true novices to seasoned professionals, to harness the power of data. Use Python 3.8+ to read, write, and transform data from a variety of sources Understand and use programming basics in Python to wrangle data at scale Organize, document, and structure your code using best practices Collect data from structured data files, web pages, and APIs Perform basic statistical analyses to make meaning from datasets Visualize and present data in clear and compelling ways |
| data science master columbia: Fundamentals of Statistical Inference , 1977 |
| data science master columbia: The Exposome Gary W. Miller, 2013-11-16 The Exposome: A Primer is the first book dedicated to exposomics, detailing the purpose and scope of this emerging field of study, its practical applications and how it complements a broad range of disciplines. Genetic causes account for up to a third of all complex diseases. (As genomic approaches improve, this is likely to rise.) Environmental factors also influence human disease but, unlike with genetics, there is no standard or systematic way to measure the influence of environmental exposures. The exposome is an emerging concept that hopes to address this, measuring the effects of life-long environmental exposures on health and how these exposures can influence disease. This systematic introduction considers topics of managing and integrating exposome data (including maps, models, computation, and systems biology), -omics-based technologies, and more. Both students and scientists in disciplines including toxicology, environmental health, epidemiology, and public health will benefit from this rigorous yet readable overview. |
| data science master columbia: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data science master columbia: Data Science Tiffany Timbers, Trevor Campbell, Melissa Lee, 2022-07-15 Data Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows. Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for data science projects. The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia’s DSCI100: Introduction to Data Science course. |
| data science master columbia: The R Inferno Patrick Burns, 2011 An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. R is free, open-source, and has thousands of contributed packages. It is used in such diverse fields as ecology, finance, genomics and music. If you are using spreadsheets to understand data, switch to R. You will have safer -- and ultimately, more convenient -- computations. |
| data science master columbia: Financial Risk Management Allan M. Malz, 2011-09-13 Financial risk has become a focus of financial and nonfinancial firms, individuals, and policy makers. But the study of risk remains a relatively new discipline in finance and continues to be refined. The financial market crisis that began in 2007 has highlighted the challenges of managing financial risk. Now, in Financial Risk Management, author Allan Malz addresses the essential issues surrounding this discipline, sharing his extensive career experiences as a risk researcher, risk manager, and central banker. The book includes standard risk measurement models as well as alternative models that address options, structured credit risks, and the real-world complexities or risk modeling, and provides the institutional and historical background on financial innovation, liquidity, leverage, and financial crises that is crucial to practitioners and students of finance for understanding the world today. Financial Risk Management is equally suitable for firm risk managers, economists, and policy makers seeking grounding in the subject. This timely guide skillfully surveys the landscape of financial risk and the financial developments of recent decades that culminated in the crisis. The book provides a comprehensive overview of the different types of financial risk we face, as well as the techniques used to measure and manage them. Topics covered include: Market risk, from Value-at-Risk (VaR) to risk models for options Credit risk, from portfolio credit risk to structured credit products Model risk and validation Risk capital and stress testing Liquidity risk, leverage, systemic risk, and the forms they take Financial crises, historical and current, their causes and characteristics Financial regulation and its evolution in the wake of the global crisis And much more Combining the more model-oriented approach of risk management-as it has evolved over the past two decades-with an economist's approach to the same issues, Financial Risk Management is the essential guide to the subject for today's complex world. |
| data science master columbia: Information Security Essentials Susan E. McGregor, 2021-06-01 As technological and legal changes have hollowed out the protections that reporters and news organizations have depended upon for decades, information security concerns facing journalists as they report, produce, and disseminate the news have only intensified. From source prosecutions to physical attacks and online harassment, the last two decades have seen a dramatic increase in the risks faced by journalists at all levels even as the media industry confronts drastic cutbacks in budgets and staff. As a result, few professional or aspiring journalists have a comprehensive understanding of what is required to keep their sources, stories, colleagues, and reputations safe. This book is an essential guide to protecting news writers, sources, and organizations in the digital era. Susan E. McGregor provides a systematic understanding of the key technical, legal, and conceptual issues that anyone teaching, studying, or practicing journalism should know. Bringing together expert insights from both leading academics and security professionals who work at and with news organizations from BuzzFeed to the Associated Press, she lays out key principles and approaches for building information security into journalistic practice. McGregor draws on firsthand experience as a Wall Street Journal staffer, followed by a decade of researching, testing, and developing information security tools and practices. Filled with practical but evergreen advice that can enhance the security and efficacy of everything from daily beat reporting to long-term investigative projects, Information Security Essentials is a vital tool for journalists at all levels. * Please note that older print versions of this book refer to Reuters' Gina Chua by her previous name. This is being corrected in forthcoming print and digital editions. |
| data science master columbia: Climate Change Jason Smerdon, 2009-04-25 Climate Change is geared toward a variety of students and general readers who seek the real science behind global warming. Exquisitely illustrated, the text introduces the basic science underlying both the natural progress of climate change and the effect of human activity on the deteriorating health of our planet. Noted expert and author Edmond A. Mathez synthesizes the work of leading scholars in climatology and related fields, and he concludes with an extensive chapter on energy production, anchoring this volume in economic and technological realities and suggesting ways to reduce greenhouse-gas emissions. Climate Change opens with the climate system fundamentals: the workings of the atmosphere and ocean, their chemical interactions via the carbon cycle, and the scientific framework for understanding climate change. Mathez then brings the climate of the past to bear on our present predicament, highlighting the importance of paleoclimatology in understanding the current climate system. Subsequent chapters explore the changes already occurring around us and their implications for the future. In a special feature, Jason E. Smerdon, associate research scientist at Lamont-Doherty Earth Observatory of Columbia University, provides an innovative appendix for students. |
| data science master columbia: Solid States Michael Bell, Craig Buckley, 2010-12 DVD features highlights from the conference held at Columbia University. |
| data science master columbia: Data Analysis and Graphics Using R John Maindonald, John Braun, 2006-12-26 Join the revolution ignited by the ground-breaking R system! Starting with an introduction to R, covering standard regression methods, then presenting more advanced topics, this book guides users through the practical and powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display and interpretation of data. The many worked examples, taken from real-world research, are accompanied by commentary on what is done and why. A website provides computer code and data sets, allowing readers to reproduce all analyses. Updates and solutions to selected exercises are also available. Assuming only basic statistical knowledge, the book is ideal for research scientists, final-year undergraduate or graduate level students of applied statistics, and practising statisticians. It is both for learning and for reference. This revised edition reflects changes in R since 2003 and has new material on survival analysis, random coefficient models, and the handling of high-dimensional data. |
| data science master columbia: Data Science Careers, Training, and Hiring Renata Rawlings-Goss, 2019-08-02 This book is an information packed overview of how to structure a data science career, a data science degree program, and how to hire a data science team, including resources and insights from the authors experience with national and international large-scale data projects as well as industry, academic and government partnerships, education, and workforce. Outlined here are tips and insights into navigating the data ecosystem as it currently stands, including career skills, current training programs, as well as practical hiring help and resources. Also, threaded through the book is the outline of a data ecosystem, as it could ultimately emerge, and how career seekers, training programs, and hiring managers can steer their careers, degree programs, and organizations to align with the broader future of data science. Instead of riding the current wave, the author ultimately seeks to help professionals, programs, and organizations alike prepare a sustainable plan for growth in this ever-changing world of data. The book is divided into three sections, the first “Building Data Careers”, is from the perspective of a potential career seeker interested in a career in data, the second “Building Data Programs” is from the perspective of a newly forming data science degree or training program, and the third “Building Data Talent and Workforce” is from the perspective of a Data and Analytics Hiring Manager. Each is a detailed introduction to the topic with practical steps and professional recommendations. The reason for presenting the book from different points of view is that, in the fast-paced data landscape, it is helpful to each group to more thoroughly understand the desires and challenges of the other. It will, for example, help the career seekers to understand best practices for hiring managers to better position themselves for jobs. It will be invaluable for data training programs to gain the perspective of career seekers, who they want to help and attract as students. Also, hiring managers will not only need data talent to hire, but workforce pipelines that can only come from partnerships with universities, data training programs, and educational experts. The interplay gives a broader perspective from which to build. |
| data science master columbia: Data Science for Business and Decision Making Luiz Paulo Favero, Patricia Belfiore, 2019-04-11 Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®. - Combines statistics and operations research modeling to teach the principles of business analytics - Written for students who want to apply statistics, optimization and multivariate modeling to gain competitive advantages in business - Shows how powerful software packages, such as SPSS and Stata, can create graphical and numerical outputs |
| data science master columbia: How Much Inequality Is Fair? Venkat Venkatasubramanian, 2017-08-08 Many in the United States feel that the nation’s current level of economic inequality is unfair and that capitalism is not working for 90% of the population. Yet some inequality is inevitable. The question is: What level of inequality is fair? Mainstream economics has offered little guidance on fairness and the ideal distribution of income. Political philosophy, meanwhile, has much to say about fairness yet relies on qualitative theories that cannot be verified by empirical data. To address inequality, we need to know what the goal is—and for this, we need a quantitative, testable theory of fairness for free-market capitalism. How Much Inequality Is Fair? synthesizes concepts from economics, political philosophy, game theory, information theory, statistical mechanics, and systems engineering into a mathematical framework for a fair free-market society. The key to this framework is the insight that maximizing fairness means maximizing entropy, which makes it possible to determine the fairest possible level of pay inequality. The framework therefore provides a moral justification for capitalism in mathematical terms. Venkat Venkatasubramanian also compares his theory’s predictions to actual inequality data from various countries—showing, for instance, that Scandinavia has near-ideal fairness, while the United States is markedly unfair—and discusses the theory’s implications for tax policy, social programs, and executive compensation. |
| data science master columbia: Machine Learning in Production Andrew Kelleher, Adam Kelleher, 2019-02-27 Foundational Hands-On Skills for Succeeding with Real Data Science Projects This pragmatic book introduces both machine learning and data science, bridging gaps between data scientist and engineer, and helping you bring these techniques into production. It helps ensure that your efforts actually solve your problem, and offers unique coverage of real-world optimization in production settings. –From the Foreword by Paul Dix, series editor Machine Learning in Production is a crash course in data science and machine learning for people who need to solve real-world problems in production environments. Written for technically competent “accidental data scientists” with more curiosity and ambition than formal training, this complete and rigorous introduction stresses practice, not theory. Building on agile principles, Andrew and Adam Kelleher show how to quickly deliver significant value in production, resisting overhyped tools and unnecessary complexity. Drawing on their extensive experience, they help you ask useful questions and then execute production projects from start to finish. The authors show just how much information you can glean with straightforward queries, aggregations, and visualizations, and they teach indispensable error analysis methods to avoid costly mistakes. They turn to workhorse machine learning techniques such as linear regression, classification, clustering, and Bayesian inference, helping you choose the right algorithm for each production problem. Their concluding section on hardware, infrastructure, and distributed systems offers unique and invaluable guidance on optimization in production environments. Andrew and Adam always focus on what matters in production: solving the problems that offer the highest return on investment, using the simplest, lowest-risk approaches that work. Leverage agile principles to maximize development efficiency in production projects Learn from practical Python code examples and visualizations that bring essential algorithmic concepts to life Start with simple heuristics and improve them as your data pipeline matures Avoid bad conclusions by implementing foundational error analysis techniques Communicate your results with basic data visualization techniques Master basic machine learning techniques, starting with linear regression and random forests Perform classification and clustering on both vector and graph data Learn the basics of graphical models and Bayesian inference Understand correlation and causation in machine learning models Explore overfitting, model capacity, and other advanced machine learning techniques Make informed architectural decisions about storage, data transfer, computation, and communication Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| data science master columbia: The Principles and Practice of Narrative Medicine Rita Charon, 2017 The Principles and Practice of Narrative Medicine articulates the ideas, methods, and practices of narrative medicine. Written by the originators of the field, this book provides the authoritative starting place for any clinicians or scholars committed to learning of and eventually teaching or practicing narrative medicine. |
| data science master columbia: Quantitative Corporate Finance John B. Guerard, Jr., Eli Schwartz, 2007-11-19 The book addresses several problems in contemporary corporate finance: optimal capital structure, both in the US and in the G7 economies; the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Model (APT) and the implications for the cost of capital; dividend policy; sales forecasting and pro forma statement analysis; leverage and bankruptcy; and mergers and acquisitions. It is designed to be used as an advanced graduate corporate financial management textbook. |
| data science master columbia: Using Data in Schools to Inform Leadership and Decision Making Alex J. Bowers, Alan R. Shoho, Bruce G. Barnett, 2014-11-01 Our fifth book in the International Research on School Leadership series focuses on the use of data in schools and districts as useful information for leadership and decision making. Schools are awash in data and information, from test scores, to grades, to discipline reports, and attendance as just a short list of student information sources, while additional streams of data feed into schools and districts from teachers and parents as well as local, regional and national policy levels. To deal with the data, schools have implemented a variety of data practices, from data rooms, to data days, data walks, and data protocols. However, despite the flood of data, successful school leaders are leveraging an analysis of their school’s data as a means to bring about continuous improvement in an effort to improve instruction for all students. Nevertheless, some drown, some swim, while others find success. Our goal in this book volume is to bring together a set of chapters by authors who examine successful data use as it relates to leadership and school improvement. In particular, the chapters in this volume consider important issues in this domain, including: • How educational leaders use data to inform their practice. • What types of data and data analysis are most useful to successful school leaders. • To what extent are data driven and data informed practices helping school leaders positively change instructional practice? • In what ways does good data collection and analysis feed into successful continuous improvement and holistic systems thinking? • How have school leadership practices changed as more data and data analysis techniques have become available? • What are the major obstacles facing school leaders when using data for decision making and how do they overcome them? |
| data science master columbia: America's Energy Future National Research Council, National Academy of Engineering, National Academy of Sciences, Division on Engineering and Physical Sciences, Committee on America's Energy Future, 2009-12-15 For multi-user PDF licensing, please contact customer service. Energy touches our lives in countless ways and its costs are felt when we fill up at the gas pump, pay our home heating bills, and keep businesses both large and small running. There are long-term costs as well: to the environment, as natural resources are depleted and pollution contributes to global climate change, and to national security and independence, as many of the world's current energy sources are increasingly concentrated in geopolitically unstable regions. The country's challenge is to develop an energy portfolio that addresses these concerns while still providing sufficient, affordable energy reserves for the nation. The United States has enormous resources to put behind solutions to this energy challenge; the dilemma is to identify which solutions are the right ones. Before deciding which energy technologies to develop, and on what timeline, we need to understand them better. America's Energy Future analyzes the potential of a wide range of technologies for generation, distribution, and conservation of energy. This book considers technologies to increase energy efficiency, coal-fired power generation, nuclear power, renewable energy, oil and natural gas, and alternative transportation fuels. It offers a detailed assessment of the associated impacts and projected costs of implementing each technology and categorizes them into three time frames for implementation. |
| data science master columbia: An Epidemic of Absence Moises Velasquez-Manoff, 2013-09-17 A controversial, revisionist approach to autoimmune and allergic disorders considers the perspective that the human immune system has been disabled by twentieth-century hygiene and medical practices. |
| data science master columbia: 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 master columbia: Machine Learning Kevin P. Murphy, 2012-08-24 A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students. |
| data science master columbia: Crush It on LinkedIn Visthruth G, Ishan Sharma, 2020-07-11 LinkedIn is one of the fastest growing social media and it is THE place for professionals and people looking to advance in their career. Crush It on LinkedIn is your guide on how to use LinkedIn effectively to build your brand, get a job, or expand your business.Here's what you'll learn from this book: How to make a stunning LinkedIn Profile that gets viewed by people on the platformHow to grow your LinkedIn profile and get noticed by people in your niche.How to create content on LinkedIn that helps you build your brand.How to talk to people effectively using the private messagingMistakes you are doing on LinkedIn that is affecting your profileAn overview of LinkedIn Advertising, Lead generation and which Businesses should use itRecent additions in 2020 and the future of this platformSuccess Stories of People who used LinkedIn to build a brand.and a lot more in this short and concise book.You'll learn these topics with multiple examples.This is a MUST have book for students in college who want to get their first internship or job. The book explains everything from the ground up.The author, Ishan Sharma is a 19 year old student at BITS Goa. He has his own YouTube Channel and a podcast with over 130k views and he helps create content for startups on social media platforms like Instagram and LinkedIn.With this book, Ishan aims to share his experiences of using LinkedIn to get new opportunities and from his talks with people who've been using LinkedIn from the last 5-7 years |
| data science master columbia: Ways of Knowing Cities Laura Kurgan, Dare Brawley, 2019 Ways of Knowing Cities considers the role of technology in generating, materializing, and contesting urban epistemologies--from ubiquitous sites of smart urbanism to discrete struggles over infrastructural governance to forgotten histories of segregation now naturalized in urban algorithms to exceptional territories of border policing. |
| data science master columbia: Probabilistic Machine Learning Kevin P. Murphy, 2022-03-01 A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach. |
| data science master columbia: Bringing Columbia Home Michael D. Leinbach, Jonathan H. Ward, 2018-01-23 Voted the Best Space Book of 2018 by the Space Hipsters The dramatic inside story of the epic search and recovery operation after the Columbia space shuttle disaster. On February 1, 2003, Columbia disintegrated on reentry before the nation’s eyes, and all seven astronauts aboard were lost. Author Mike Leinbach, Launch Director of the space shuttle program at NASA’s John F. Kennedy Space Center was a key leader in the search and recovery effort as NASA, FEMA, the FBI, the US Forest Service, and dozens more federal, state, and local agencies combed an area of rural east Texas the size of Rhode Island for every piece of the shuttle and her crew they could find. Assisted by hundreds of volunteers, it would become the largest ground search operation in US history. This comprehensive account is told in four parts: Parallel Confusion Courage, Compassion, and Commitment Picking Up the Pieces A Bittersweet Victory For the first time, here is the definitive inside story of the Columbia disaster and recovery and the inspiring message it ultimately holds. In the aftermath of tragedy, people and communities came together to help bring home the remains of the crew and nearly 40 percent of shuttle, an effort that was instrumental in piecing together what happened so the shuttle program could return to flight and complete the International Space Station. Bringing Columbia Home shares the deeply personal stories that emerged as NASA employees looked for lost colleagues and searchers overcame immense physical, logistical, and emotional challenges and worked together to accomplish the impossible. Featuring a foreword and epilogue by astronauts Robert Crippen and Eileen Collins, and dedicated to the astronauts and recovery search persons who lost their lives, this is an incredible, compelling narrative about the best of humanity in the darkest of times and about how a failure at the pinnacle of human achievement became a story of cooperation and hope. |
| data science master columbia: 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 master columbia: Columbia Crew Survival Investigation Report Nasa, 2009 NASA commissioned the Columbia Accident Investigation Board (CAIB) to conduct a thorough review of both the technical and the organizational causes of the loss of the Space Shuttle Columbia and her crew on February 1, 2003. The accident investigation that followed determined that a large piece of insulating foam from Columbia's external tank (ET) had come off during ascent and struck the leading edge of the left wing, causing critical damage. The damage was undetected during the mission. The Columbia accident was not survivable. After the Columbia Accident Investigation Board (CAIB) investigation regarding the cause of the accident was completed, further consideration produced the question of whether there were lessons to be learned about how to improve crew survival in the future. This investigation was performed with the belief that a comprehensive, respectful investigation could provide knowledge that can protect future crews in the worldwide community of human space flight. Additionally, in the course of the investigation, several areas of research were identified that could improve our understanding of both nominal space flight and future spacecraft accidents. This report is the first comprehensive, publicly available accident investigation report addressing crew survival for a human spacecraft mishap, and it provides key information for future crew survival investigations. The results of this investigation are intended to add meaning to the sacrifice of the crew's lives by making space flight safer for all future generations. |
| data science master columbia: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| data science master columbia: Pattern Recognition and Machine Learning Christopher M. Bishop, 2016-08-23 This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. |
| data science master columbia: Recent Advances in Information Systems and Technologies Álvaro Rocha, Ana Maria Correia, Hojjat Adeli, Luís Paulo Reis, Sandra Costanzo, 2017-03-28 This book presents a selection of papers from the 2017 World Conference on Information Systems and Technologies (WorldCIST'17), held between the 11st and 13th of April 2017 at Porto Santo Island, Madeira, Portugal. WorldCIST is a global forum for researchers and practitioners to present and discuss recent results and innovations, current trends, professional experiences and challenges involved in modern Information Systems and Technologies research, together with technological developments and applications. The main topics covered are: Information and Knowledge Management; Organizational Models and Information Systems; Software and Systems Modeling; Software Systems, Architectures, Applications and Tools; Multimedia Systems and Applications; Computer Networks, Mobility and Pervasive Systems; Intelligent and Decision Support Systems; Big Data Analytics and Applications; Human–Computer Interaction; Ethics, Computers & Security; Health Informatics; Information Technologies in Education; and Information Technologies in Radiocommunications. |
| data science master columbia: Environmental Conflict Resolution Christopher Napier, 1998 |
| data science master columbia: 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 master columbia: The Women Who Changed Architecture Jan Cigliano Hartman, 2022-03-29 A visual and global chronicle of the triumphs, challenges, and impact of over 100 women in architecture, from early practitioners to contemporary leaders. Marion Mahony Griffin passed the architectural licensure exam in 1898 and created exquisite drawings that buoyed the reputation of Frank Lloyd Wright. Her story is one of the many told in The Women Who Changed Architecture, which sets the record straight on the transformative impact women have made on architecture. With in-depth profiles and stunning images, this is the most comprehensive look at women in architecture around the world, from the nineteenth century to today. Discover contemporary leaders, like MacArthur Fellow Jeanne Gang, spearheading sustainable design initiatives, reimagining cities as equitable spaces, and directing architecture schools. An essential read for architecture students, architects, and anyone interested in how buildings are created and the history behind them. |
| data science master columbia: Earth Repair Leila Darwish, 2013-06-01 Millions of acres of land have been contaminated by pesticides, improperly handled chemicals, dirty energy projects, toxic waste, and other pollutants in the United States alone. This toxic legacy impacts the environment, our health, our watersheds, and land that could otherwise be used to grow healthy local food and medicines. Conventional clean-up techniques employed by government and industry are tremendously expensive and resource-intensive and can cause further damage. More and more communities find themselves increasingly unable to rely on those companies and governments who created the problems to step in and provide solutions. Earth Repair describes a host of powerful grassroots bioremediation techniques, including: Microbial remediation—using microorganisms to break down and bind contaminants Phytoremediation—using plants to extract, bind, and transform toxins Mycoremediation—using fungi to clean up contaminated soil and water Packed with valuable, firsthand information from visionaries in the field, Earth Repair empowers communities and individuals to take action and heal contaminated and damaged land. Encompassing everything from remediating and regenerating abandoned city lots for urban farmers and gardeners to recovering from environmental disasters and industrial catastrophes such as oil spills and nuclear fallout, this fertile toolbox is essential reading for anyone who wishes to transform environmental despair into constructive action. Leila Darwish is a community organizer, urban gardener, and permaculture designer with a focus on using grassroots bioremediation to address environmental justice issues in communities struggling with toxic contamination of their land and drinking water. |
| data science master columbia: Continuous-Time Asset Pricing Theory Robert A. Jarrow, 2021-07-30 Asset pricing theory yields deep insights into crucial market phenomena such as stock market bubbles. Now in a newly revised and updated edition, this textbook guides the reader through this theory and its applications to markets. The new edition features new results on state dependent preferences, a characterization of market efficiency and a more general presentation of multiple-factor models using only the assumptions of no arbitrage and no dominance. Taking an innovative approach based on martingales, the book presents advanced techniques of mathematical finance in a business and economics context, covering a range of relevant topics such as derivatives pricing and hedging, systematic risk, portfolio optimization, market efficiency, and equilibrium pricing models. For applications to high dimensional statistics and machine learning, new multi-factor models are given. This new edition integrates suicide trading strategies into the understanding of asset price bubbles, greatly enriching the overall presentation and further strengthening the book’s underlying theme of economic bubbles. Written by a leading expert in risk management, Continuous-Time Asset Pricing Theory is the first textbook on asset pricing theory with a martingale approach. Based on the author’s extensive teaching and research experience on the topic, it is particularly well suited for graduate students in business and economics with a strong mathematical background. |
| data science master columbia: Data Science & Business Analytics Sneha Kumari, K. K. Tripathy, Vidya Kumbhar, 2020-12-04 Data Science & Business Analytics explores the application of big data and business analytics by academics, researchers, industrial experts, policy makers and practitioners, helping the reader to understand how big data can be efficiently utilized in better managerial applications. |
| data science master columbia: 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 master columbia: School and District Leadership in an Era of Accountability Bruce G. Barnett, Alan R. Shoho, Alex J. Bowers, 2013-09-01 Our fourth book in the International Research on School Leadership series focuses on school leadership in an era of high stakes accountability. Fueled by sweeping federal education accountability reforms, such as the United States’ No Child Left Behind (NCLB) and Race to the Top (R2T) and Australia’s Performance Measurement and Reporting Taskforce, school systems around the world are being forced to increase academic standards, participate in high-stakes testing, and raise evaluation standards for teachers and principals. These results-driven reforms are intended to hold educators “accountable for student learning and accountable to the public” (Anderson, 2005, p. 2, emphasis in original). While policymakers and the public debate the merits of student achievement accountability measures, P-12 educational leaders do not have the luxury to wait for clear guidance and resources to improve their schools and operating systems. Instead, successful leaders must balance the need to create learning communities, manage the organizational climate, and encourage community involvement with the consequences testing has on teacher morale and public scrutiny. The chapters in this volume clearly indicate that as school leaders attend to these potentially competing forces, this affects their problem-solving strategies, ability to facilitate change, and encourage community involvement. We were delighted with the responses from colleagues around the world who were eager to share their research dealing with how leaders are functioning effectively within a high-accountability environment. The nine chapters in this volume provide empirical evidence of the strategies school leaders use to cope with problems and negotiate external demands while improving student performance. In particular, the voices and actions of principals, superintendents, and school board members are captured in a blend of quantitative and qualitative studies. The breadth of studies is impressive, ranging from case studies of individual principals to cross-district comparisons to national data from the National Center for Education Statistics. To highlight important findings, we have organized the book into five sections. The first section (Chapters 2, 3, and 4) highlights the problem-solving strategies used by principals and superintendents when pressured to turn around low-performing schools. In the second section (Chapters 5 and 6), attention is devoted to ways in which school leaders act as “buffers” by reducing the impact of external demands within their local school contexts. Next, Chapters 7 and 8 explore creative ways in which financial analyses can be used to assess the cost effectiveness of programs and services. Chapters 9 and 10 examine how principals enact their instructional leadership roles in managing curriculum reforms and evaluating teachers. Finally, in the last section (Chapter 11), Kenneth Leithwood synthesizes the major themes and ideas emerging across these chapters, paying particular attention to practical issues influencing school leaders in this era of school reform and accountability as well as promising areas for future research. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
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