| data science masters 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 masters columbia: Fundamentals of Statistical Inference , 1977 |
| data science masters 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 masters 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 masters 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 masters columbia: R for Everyone Jared P. Lander, 2017-06-13 Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includes Explore R, RStudio, and R packages Use R for math: variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using R’s facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalized linear, and nonlinear models Program basic statistics: mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyze univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp Register your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available. |
| data science masters columbia: Practical Data Science Cookbook Prabhanjan Tattar, Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta, 2017-06-29 Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Python Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization |
| data science masters 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 masters columbia: The Nature of Statistical Learning Theory Vladimir Vapnik, 2013-06-29 The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists. |
| data science masters columbia: Mastering 'Metrics Joshua D. Angrist, Jörn-Steffen Pischke, 2014-12-21 From Joshua Angrist, winner of the Nobel Prize in Economics, and Jörn-Steffen Pischke, an accessible and fun guide to the essential tools of econometric research Applied econometrics, known to aficionados as 'metrics, is the original data science. 'Metrics encompasses the statistical methods economists use to untangle cause and effect in human affairs. Through accessible discussion and with a dose of kung fu–themed humor, Mastering 'Metrics presents the essential tools of econometric research and demonstrates why econometrics is exciting and useful. The five most valuable econometric methods, or what the authors call the Furious Five—random assignment, regression, instrumental variables, regression discontinuity designs, and differences in differences—are illustrated through well-crafted real-world examples (vetted for awesomeness by Kung Fu Panda's Jade Palace). Does health insurance make you healthier? Randomized experiments provide answers. Are expensive private colleges and selective public high schools better than more pedestrian institutions? Regression analysis and a regression discontinuity design reveal the surprising truth. When private banks teeter, and depositors take their money and run, should central banks step in to save them? Differences-in-differences analysis of a Depression-era banking crisis offers a response. Could arresting O. J. Simpson have saved his ex-wife's life? Instrumental variables methods instruct law enforcement authorities in how best to respond to domestic abuse. Wielding econometric tools with skill and confidence, Mastering 'Metrics uses data and statistics to illuminate the path from cause to effect. Shows why econometrics is important Explains econometric research through humorous and accessible discussion Outlines empirical methods central to modern econometric practice Works through interesting and relevant real-world examples |
| data science masters 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 masters 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 masters 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 masters 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 masters 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 masters columbia: Ultimate Price Howard Steven Friedman, 2021-05-05 How much is a human life worth? Individuals, families, companies, and governments routinely place a price on human life. The calculations that underlie these price tags are often buried in technical language, yet they influence our economy, laws, behaviors, policies, health, and safety. These price tags are often unfair, infused as they are with gender, racial, national, and cultural biases that often result in valuing the lives of the young more than the old, the rich more than the poor, whites more than blacks, Americans more than foreigners, and relatives more than strangers. This is critical since undervalued lives are left less-protected and more exposed to risk. Howard Steven Friedman explains in simple terms how economists and data scientists at corporations, regulatory agencies, and insurance companies develop and use these price tags and points a spotlight at their logical flaws and limitations. He then forcefully argues against the rampant unfairness in the system. Readers will be enlightened, shocked, and, ultimately, empowered to confront the price tags we assign to human lives and understand why such calculations matter. |
| data science masters 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 masters 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 masters 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 masters 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 masters 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 masters 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 masters columbia: Twenty Lectures on Algorithmic Game Theory Tim Roughgarden, 2016-08-30 Computer science and economics have engaged in a lively interaction over the past fifteen years, resulting in the new field of algorithmic game theory. Many problems that are central to modern computer science, ranging from resource allocation in large networks to online advertising, involve interactions between multiple self-interested parties. Economics and game theory offer a host of useful models and definitions to reason about such problems. The flow of ideas also travels in the other direction, and concepts from computer science are increasingly important in economics. This book grew out of the author's Stanford University course on algorithmic game theory, and aims to give students and other newcomers a quick and accessible introduction to many of the most important concepts in the field. The book also includes case studies on online advertising, wireless spectrum auctions, kidney exchange, and network management. |
| data science masters 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 masters 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 masters 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 masters columbia: The Measure of a Nation Howard Steven Friedman, 2012 Compares the United States with other affluent democracies in such areas as health, crime and violence, education, democracy, and equality, and suggests ways the country might improve its standing in these areas. |
| data science masters columbia: Environmental Conflict Resolution Christopher Napier, 1998 |
| data science masters columbia: Medicine and Western Civilization David J. Rothman, Steven Marcus, Stephanie A. Kiceluk, 1995 This fabulous anthology is sure to be a core text for history of medicine and social science classes in colleges across the country. In order to demonstrate how medical research has influenced Western cultural perspectives, the editors have collected original works from 61 different authors around nine major themes (among them Anatomy and Destiny, Psyche and Soma, and The Construction of Pain, Suffering, and Death). The authors range from Aristotle, the Bible, and Louis Pasteur, to Masters and Johnson, Ernest Hemingway, and Simone de Beauvoir. The primary sources selected to illustrate the themes are well chosen and contrast with each other nicely. However, the brief background material for the selections center around the authors and offer little or no discussion about the selections' relevance to the topics at hand. This book would be best read in a class or group where the texts' meaning in relation to each other can be discussed, but the book can stand alone if the reader is prepared to do some critical thinking. |
| data science masters 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 masters 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 masters columbia: Game Theory, Alive Anna R. Karlin, Yuval Peres, 2017-04-27 We live in a highly connected world with multiple self-interested agents interacting and myriad opportunities for conflict and cooperation. The goal of game theory is to understand these opportunities. This book presents a rigorous introduction to the mathematics of game theory without losing sight of the joy of the subject. This is done by focusing on theoretical highlights (e.g., at least six Nobel Prize winning results are developed from scratch) and by presenting exciting connections of game theory to other fields such as computer science (algorithmic game theory), economics (auctions and matching markets), social choice (voting theory), biology (signaling and evolutionary stability), and learning theory. Both classical topics, such as zero-sum games, and modern topics, such as sponsored search auctions, are covered. Along the way, beautiful mathematical tools used in game theory are introduced, including convexity, fixed-point theorems, and probabilistic arguments. The book is appropriate for a first course in game theory at either the undergraduate or graduate level, whether in mathematics, economics, computer science, or statistics. The importance of game-theoretic thinking transcends the academic setting—for every action we take, we must consider not only its direct effects, but also how it influences the incentives of others. |
| data science masters 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 masters columbia: The For the War Yet to Come Hiba Bou Akar, 2018-09-11 “Through elegant ethnography and nuanced theorization . . . gives us a new way of thinking about violence, development, modernity, and ultimately, the city.” —Ananya Roy, University of California, Los Angeles Beirut is a city divided. Following the Green Line of the civil war, dividing the Christian east and the Muslim west, today hundreds of such lines dissect the city. For the residents of Beirut, urban planning could hold promise: a new spatial order could bring a peaceful future. But with unclear state structures and outsourced public processes, urban planning has instead become a contest between religious-political organizations and profit-seeking developers. Neighborhoods reproduce poverty, displacement, and urban violence. For the War Yet to Come examines urban planning in three neighborhoods of Beirut’s southeastern peripheries, revealing how these areas have been developed into frontiers of a continuing sectarian order. Hiba Bou Akar argues these neighborhoods are arranged, not in the expectation of a bright future, but according to the logic of “the war yet to come”: urban planning plays on fears and differences, rumors of war, and paramilitary strategies to organize everyday life. As she shows, war in times of peace is not fought with tanks, artillery, and rifles, but involves a more mundane territorial contest for land and apartment sales, zoning and planning regulations, and infrastructure projects. Winner of the Anthony Leeds Prize “Upends our conventional notions of center and periphery, of local and transnational, even of war and peace.” —AbdouMaliq Simone, Max Planck Institute for the Study of Religious and Ethnic Diversity “Fascinating, theoretically astute, and empirically rich.” —Asef Bayat, University of Illinois — Urbana-Champaign “An important contribution.” —Christine Mady, International Journal of Middle East Studies |
| data science masters columbia: Nature Engaged M. Biagioli, J. Riskin, 2012-12-10 This volume gathers essays that focus on the worldliness of science, its inseparable engagement in the major institutional bases of social life: law, market, church, school, and nation. With a chronological span reaching from the Renaissance to Big Science, its topics range from sundials to genetic sequences, from calculating instruments to devices that simulate human behavior, from early cartography to techniques for tracing radioactive fallout on a global scale. The book aims to show readers, with episodes drawn from the span of their modern history, the sciences in action throughout human society. |
| data science masters columbia: The Urge Carl Erik Fisher, 2022-01-25 Named a Best Book of the Year by The New Yorker and The Boston Globe An authoritative, illuminating, and deeply humane history of addiction—a phenomenon that remains baffling and deeply misunderstood despite having touched countless lives—by an addiction psychiatrist striving to understand his own family and himself “Carl Erik Fisher’s The Urge is the best-written and most incisive book I’ve read on the history of addiction. In the midst of an overdose crisis that grows worse by the hour and has vexed America for centuries, Fisher has given us the best prescription of all: understanding. He seamlessly blends a gripping historical narrative with memoir that doesn’t self-aggrandize; the result is a full-throated argument against blaming people with substance use disorder. The Urge is a propulsive tour de force that is as healing as it is enjoyable to read.” —Beth Macy, author of Dopesick Even after a decades-long opioid overdose crisis, intense controversy still rages over the fundamental nature of addiction and the best way to treat it. With uncommon empathy and erudition, Carl Erik Fisher draws on his own experience as a clinician, researcher, and alcoholic in recovery as he traces the history of a phenomenon that, centuries on, we hardly appear closer to understanding—let alone addressing effectively. As a psychiatrist-in-training fresh from medical school, Fisher was soon face-to-face with his own addiction crisis, one that nearly cost him everything. Desperate to make sense of the condition that had plagued his family for generations, he turned to the history of addiction, learning that the current quagmire is only the latest iteration of a centuries-old story: humans have struggled to define, treat, and control addictive behavior for most of recorded history, including well before the advent of modern science and medicine. A rich, sweeping account that probes not only medicine and science but also literature, religion, philosophy, and public policy, The Urge illuminates the extent to which the story of addiction has persistently reflected broader questions of what it means to be human and care for one another. Fisher introduces us to the people who have endeavored to address this complex condition through the ages: physicians and politicians, activists and artists, researchers and writers, and of course the legions of people who have struggled with their own addictions. He also examines the treatments and strategies that have produced hope and relief for many people with addiction, himself included. Only by reckoning with our history of addiction, he argues—our successes and our failures—can we light the way forward for those whose lives remain threatened by its hold. The Urge is at once an eye-opening history of ideas, a riveting personal story of addiction and recovery, and a clinician’s urgent call for a more expansive, nuanced, and compassionate view of one of society’s most intractable challenges. |
| data science masters columbia: Dental Digital Photography Feng Liu, 2019-04-23 This book provides comprehensive and updated knowledge about dental digital photography. The first part of this book focuses on the fundamental theory of photography, how to select photographic equipment and the basic applications of digital photography in various aspect in dentistry. It is not only helpful to get more clearly understanding of the concept and methods of dental digital photography, but also instructive for dentist to apply photography during their daily treatment. The second part of the book summaries the different category of digital images. It describes the post-processing methods of the images and the digital smile design(DSD) basic process, which highlight the instructive aspects for clinical work. In the Chapter named ‘cosmetic dental treatment based on digital photography’, comprehensive cosmetic cases are provided; Appendix contains a variety of dental photography standards. |
| data science masters columbia: Reckoning with Matter Matthew L. Jones, 2016-11-29 From Blaise Pascal in the 1600s to Charles Babbage in the first half of the nineteenth century, inventors struggled to create the first calculating machines. All failed—but that does not mean we cannot learn from the trail of ideas, correspondence, machines, and arguments they left behind. In Reckoning with Matter, Matthew L. Jones draws on the remarkably extensive and well-preserved records of the quest to explore the concrete processes involved in imagining, elaborating, testing, and building calculating machines. He explores the writings of philosophers, engineers, and craftspeople, showing how they thought about technical novelty, their distinctive areas of expertise, and ways they could coordinate their efforts. In doing so, Jones argues that the conceptions of creativity and making they exhibited are often more incisive—and more honest—than those that dominate our current legal, political, and aesthetic culture. |
| data science masters columbia: Structural Interfaces and Attachments in Biology Stavros Thomopoulos, Victor Birman, Guy M. Genin, 2012-10-05 Attachment of dissimilar materials in engineering and surgical practice is a perennial challenge. Bimaterial attachment sites are common locations for injury, repeated injury, and mechanical failure. Nature presents several highly effective solutions to the challenge of bimaterial attachment that differ from those found in engineering practice. Structural Interfaces and Attachments in Biology describes the attachment of dissimilar materials from multiple perspectives. The text will simultaneously elucidate natural bimaterial attachments and outline engineering principles underlying successful attachments to the communities of tissue engineers and surgeons. Included an in-depth analysis of the biology of attachments in the body and mechanisms by which robust attachments are formed, a review of current concepts of attaching dissimilar materials in surgical practice and a discussion of bioengineering approaches that are currently being developed. |
| data science masters columbia: Regulation and Planning Yvonne Rydin, Robert Beauregard, Marco Cremaschi, Laura Lieto, 2021-09-30 In Regulation and Planning, planning scholars from the United Kingdom, France, Italy, Sweden, Canada, Australia, and the United States explore how planning regulations are negotiated amid layers of normative considerations. It treats regulation not simply as a set of legal guidelines to be compared against proposed actions, but as a social practice in which issues of governmental legitimacy, cultural understandings, materiality, and power are contested. Each chapter addresses an actual instance of planning regulation including, among others, a dispute about a proposed Apple store in a public park in Stockholm, the procedures by which building codes are managed by planners in Napoli, the role that design plays in regulating the use of public space in a new Paris neighbourhood, and the influence of plans on the regulation of development in Malmö and Cambridge. Collectively, the volume probes the institutions and practices that give meaning and consequence to planning regulations. For planning students learning about what it means to plan, planning researchers striving to understand the influence of planners on urban development, and planning practitioners interested in reflecting on practices that occupy a great deal of their time, this is an indispensable book. |
Data Science - engineering.columbia.edu
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Students in the Master of Science in Learning Analytics program work with real-world data collected from online and digital learning environments in the K-12 and post-secondary sectors. …
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1) Introduction to Computer Science: COMS 1004, COMS 1005, ENGI 1006, or COMS 1007 2) Data Structures: COMS 3134, COMS 3136, or COMS 3137 3) Discrete Math: COMS 3203
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Economics student and data science enthusiast, dedicated to transforming rapidly evolving markets from within. Experienced in product development, leading teams and delivering results …
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leverage advanced quantitative models, algorithms, and data for making decisions to improve business operations. Students pursuing this 36-point degree program are provided with a …
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Be the master of your future in the Chemical Process Industry with a Master’s Degree in Chemical Engineering. No One Can Engineer Like a Columbian!
Political Analytics Master of Science - Columbia University …
sciences—particularly political science, statistics, mathematical modeling, and applied analytics—this program meets the needs of learners who aspire to a career in political …
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