Advertisement
| columbia university masters data science: 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. |
| columbia university masters data science: 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. |
| columbia university masters data science: 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. |
| columbia university masters data science: Computational Statistical Methodologies and Modeling for Artificial Intelligence Priyanka Harjule, Azizur Rahman, Basant Agarwal, Vinita Tiwari, 2023-03-31 This book covers computational statistics-based approaches for Artificial Intelligence. The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems. The primary users of this book will include researchers, academicians, postgraduate students, and specialists in the areas of data science, mathematical modelling, and Artificial Intelligence. It will also serve as a valuable resource for many others in the fields of electrical, computer, and optical engineering. The key features of this book are: Presents development of several real-world problem applications and experimental research in the field of computational statistics and mathematical modelling for Artificial Intelligence Examines the evolution of fundamental research into industrialized research and the transformation of applied investigation into real-time applications Examines the applications involving analytical and statistical solutions, and provides foundational and advanced concepts for beginners and industry professionals Provides a dynamic perspective to the concept of computational statistics for analysis of data and applications in intelligent systems with an objective of ensuring sustainability issues for ease of different stakeholders in various fields Integrates recent methodologies and challenges by employing mathematical modeling and statistical techniques for Artificial Intelligence |
| columbia university masters data science: 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. |
| columbia university masters data science: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning Segall, Richard S., Niu, Gao, 2022-01-07 During these uncertain and turbulent times, intelligent technologies including artificial neural networks (ANN) and machine learning (ML) have played an incredible role in being able to predict, analyze, and navigate unprecedented circumstances across a number of industries, ranging from healthcare to hospitality. Multi-factor prediction in particular has been especially helpful in dealing with the most current pressing issues such as COVID-19 prediction, pneumonia detection, cardiovascular diagnosis and disease management, automobile accident prediction, and vacation rental listing analysis. To date, there has not been much research content readily available in these areas, especially content written extensively from a user perspective. Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning is designed to cover a brief and focused range of essential topics in the field with perspectives, models, and first-hand experiences shared by prominent researchers, discussing applications of artificial neural networks (ANN) and machine learning (ML) for biomedical and business applications and a listing of current open-source software for neural networks, machine learning, and artificial intelligence. It also presents summaries of currently available open source software that utilize neural networks and machine learning. The book is ideal for professionals, researchers, students, and practitioners who want to more fully understand in a brief and concise format the realm and technologies of artificial neural networks (ANN) and machine learning (ML) and how they have been used for prediction of multi-disciplinary research problems in a multitude of disciplines. |
| columbia university masters data science: 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. |
| columbia university masters data science: Fundamentals of Statistical Inference , 1977 |
| columbia university masters data science: 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. |
| columbia university masters data science: 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. |
| columbia university masters data science: 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. |
| columbia university masters data science: Python Machine Learning By Example Yuxi (Hayden) Liu, 2024-07-31 Author Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandas Key Features Discover new and updated content on NLP transformers, PyTorch, and computer vision modeling Includes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutions Implement ML models, such as neural networks and linear and logistic regression, from scratch Purchase of the print or Kindle book includes a free PDF copy Book DescriptionThe fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.What you will learn Follow machine learning best practices throughout data preparation and model development Build and improve image classifiers using convolutional neural networks (CNNs) and transfer learning Develop and fine-tune neural networks using TensorFlow and PyTorch Analyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIP Build classifiers using support vector machines (SVMs) and boost performance with PCA Avoid overfitting using regularization, feature selection, and more Who this book is for This expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project. |
| columbia university masters data science: The Grants Register 2025 Palgrave Macmillan, |
| columbia university masters data science: 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. |
| columbia university masters data science: Maker Education Meets Technology Education , 2023-09-04 In this book two fields meet, Technology Education with its long history, and Maker Education, a relative new shoot in the educational field. Both focus on learning through making and both value agency and motivation of learners. The purpose of this book is to understand and analyze the kind of informal and formal educational activities that take place under the umbrella of the Maker Movement and then relate this to the field of Technology Education to uncover what researchers, innovators and teachers in this field can learn from the principles, ideas and practices that are central to the Maker Movement and vice versa. The book contains two types of chapters. The first type is case study chapters that span from Mexico, China, Korea, Denmark, the Netherlands to Kenya and from primary to tertiary level, showing a variety of good practices in maker education including both formal and informal contexts. In the subsequent thematic chapters, dedicated authors have used the case studies to reflect on themes such as curriculum reform, social learning, materiality, spatial thinking, informal versus formal learning as well as the sustainability of learning and relate what is happening in Maker Education with Technology Education to imagine possible futures for Maker Education. |
| columbia university masters data science: Bulletin United States. Office of Education, 1933 |
| columbia university masters data science: The Case for International Sharing of Scientific Data National Research Council, Policy and Global Affairs, Board on Research Data and Information, Board on International Scientific Organizations, Committee on the Case of International Sharing of Scientific Data: A Focus on Developing Countries, 2013-01-11 The theme of this international symposium is the promotion of greater sharing of scientific data for the benefit of research and broader development, particularly in the developing world. This is an extraordinarily important topic. Indeed, I have devoted much of my own career to matters related to the concept of openness. I had the opportunity to promote and help build the open courseware program at the Massachusetts Institute of Technology (MIT). This program has made the teaching materials for all 2,000 subjects taught at MIT available on the Web for anyone, anywhere, to use anytime at no cost. In countries where basic broadband was not available, we shipped it in on hard drives and compact disks. Its impact has been worldwide, but it has surely had the greatest impact on the developing world. I am also a trustee of a nonprofit organization named Ithaca that operates Journal Storage (JSTOR) and other entities that make scholarly information available at very low cost. The culture of science has been international and open for centuries. Indeed, the scientific enterprise can only work when all information is open and accessible, because science works through critical analysis and replication of results. In recent years, as some scientific data, and especially technological data, have increased in economic value frequently has caused us to be far less open with information than business and free enterprise require us to be. Indeed, the worldwide shift to what is known as open innovation is strengthening every day. Finally, since the end of World War II, the realities of modern military conflict and now terrorism have led governments to restrict information through classification. This is important, but I believe that we classify far too much information. The last thing we need today, at the beginning of the twenty-first century, is further arbitrary limitations on the free flow of scientific information, whether by policies established by governments and businesses, or by lack of information infrastructure. For all these reasons, the international sharing of scientific data is one of the topics of great interest here at the National Academies and has been the subject of many of our past reports. This is the primary reason why this symposium has been co-organized by the NRC's Policy and Global Affairs Division-the Board on International Scientific Organizations (BISO) and the Board on Research Data and Information (BRDI). The Case for International Sharing of Scientific Data: A Focus on Developing Countries: Proceedings of a Symposium summarizes the symposium. |
| columbia university masters data science: 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. |
| columbia university masters data science: Bayesian Data Analysis, Third Edition Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin, 2013-11-01 Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page. |
| columbia university masters data science: The Wiley Handbook of Cognition and Assessment Andre A. Rupp, Jacqueline P. Leighton, 2016-11-14 This state-of-the-art resource brings together the most innovative scholars and thinkers in the field of testing to capture the changing conceptual, methodological, and applied landscape of cognitively-grounded educational assessments. Offers a methodologically-rigorous review of cognitive and learning sciences models for testing purposes, as well as the latest statistical and technological know-how for designing, scoring, and interpreting results Written by an international team of contributors at the cutting-edge of cognitive psychology and educational measurement under the editorship of a research director at the Educational Testing Service and an esteemed professor of educational psychology at the University of Alberta as well as supported by an expert advisory board Covers conceptual frameworks, modern methodologies, and applied topics, in a style and at a level of technical detail that will appeal to a wide range of readers from both applied and scientific backgrounds Considers emerging topics in cognitively-grounded assessment, including applications of emerging socio-cognitive models, cognitive models for human and automated scoring, and various innovative virtual performance assessments |
| columbia university masters data science: Proceedings of the 3rd Annual Generalized Intelligent Framework for Tutoring (GIFT) Users Symposium (GIFTSym3) Robert A. Sottilare, Anne M. Sinatra, 2015-08-01 GIFT, the Generalized Intelligent Framework for Tutoring, is a modular, service-oriented architecture developed to lower the skills and time needed to author effective adaptive instruction. Design goals for GIFT also include capturing best instructional practices, promoting standardization and reuse for adaptive instructional content and methods, and methods for evaluating the effectiveness of tutoring technologies. Truly adaptive systems make intelligent (optimal) decisions about tailoring instruction in real-time and make these decisions based on information about the learner and conditions in the instructional environment. The GIFT Users Symposia were started in 2013 to capture successful implementations of GIFT from the user community and to share recommendations leading to more useful capabilities for GIFT authors, researchers, and learners. |
| columbia university masters data science: So You Want to Be a Neuroscientist? Ashley Juavinett, 2020-12-08 The pursuit to understand the human brain in all its intricacy is a fascinatingly complex challenge and neuroscience is one of the fastest-growing scientific fields worldwide. There is a wide range of career options open to those who wish to pursue a career in neuroscience, yet there are few resources that provide students with inside advice on how to go about it. So You Want to Be a Neuroscientist? is a contemporary and engaging guide for aspiring neuroscientists of diverse backgrounds and interests. Fresh with the experience of having recently launched her own career, Ashley Juavinett provides a candid look at the field, offering practical guidance that explores everything from programming to personal stories. Juavinett begins with a look at the field and its history, exploring our evolving understanding of how the brain works. She then tackles the nitty-gritty: how to apply to a PhD program, the daily life of a graduate student, the art of finding mentors and collaborators, and what to expect when working in a lab. Finally, she introduces readers to diverse young scientists whose career paths illustrate what you can do with a neuroscience degree. For anyone intrigued by the brain or seeking advice on how to further their ambitions of studying it, So You Want to Be a Neuroscientist? is a practical and timely overview of how to learn and thrive in this exciting field. |
| columbia university masters data science: ACM ... Administrative Directory of College and University Computer Science/data Processing Programs and Computer Facilities , 1988 |
| columbia university masters data science: Computer Age Statistical Inference Bradley Efron, Trevor Hastie, 2016-07-21 Take an exhilarating journey through the modern revolution in statistics with two of the ringleaders. |
| columbia university masters data science: 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 |
| columbia university masters data science: Practical Python Data Wrangling and Data Quality Susan E. McGregor, 2021-12-03 There are awesome discoveries to be made and valuable stories to be told in datasets--and this book will help you uncover them. Whether you already work with data or just want to understand its possibilities, the techniques and advice in this practical book will help you learn how to better clean, evaluate, and analyze data to generate meaningful insights and compelling visualizations. Through foundational concepts and worked examples, author Susan McGregor provides the concepts and tools you need to evaluate and analyze all kinds of data and communicate your findings effectively. This book provides a methodical, jargon-free way for practitioners of all levels 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 Complete exercises either on your own machine or on the web Collect data from structured data files, web pages, and APIs Perform basic statistical analysis to make meaning from data sets Visualize and present data in clear and compelling ways. |
| columbia university masters data science: Computer Age Statistical Inference, Student Edition Bradley Efron, Trevor Hastie, 2021-06-17 Now in paperback and fortified with exercises, this brilliant, enjoyable text demystifies data science, statistics and machine learning. |
| columbia university masters data science: Diversity in Visualization Ron Metoyer, Kelly Gaither, 2022-06-01 At the 2016 IEEE VIS Conference in Baltimore, Maryland, a panel of experts from the Scientific Visualization (SciVis) community gathered to discuss why the SciVis component of the conference had been shrinking significantly for over a decade. As the panelists concluded and opened the session to questions from the audience, Annie Preston, a Ph.D. student at the University of California, Davis, asked whether the panelists thought diversity or, more specifically, the lack of diversity was a factor. This comment ignited a lively discussion of diversity: not only its impact on Scientific Visualization, but also its role in the visualization community at large. The goal of this book is to expand and organize the conversation. In particular, this book seeks to frame the diversity and inclusion topic within the Visualization community, illuminate the issues, and serve as a starting point to address how to make this community more diverse and inclusive. This book acknowledges that diversity is a broad topic with many possible meanings. Expanded definitions of diversity that are relevant to the Visualization community and to computing at large are considered. The broader conversation of inclusion and diversity is framed within the broader sociological context in which it must be considered. Solutions to recruit and retain a diverse research community and strategies for supporting inclusion efforts are presented. Additionally, community members present short stories detailing their non-inclusive experiences in an effort to facilitate a community-wide conversation surrounding very difficult situations. It is important to note that this is by no means intended to be a comprehensive, authoritative statement on the topic. Rather, this book is intended to open the conversation and begin to build a framework for diversity and inclusion in this specific research community. While intended for the Visualization community, ideally, this book will provide guidance for any computing community struggling with similar issues and looking for solutions. |
| columbia university masters data science: From Solidarity to Geopolitics Tsveta Petrova, 2014-09-22 This book theorizes a mechanism underlying regime-change waves, the deliberate efforts of diffusion entrepreneurs to spread a particular regime and regime-change model across state borders. Why do only certain states and nonstate actors emerge as such entrepreneurs? Why, how, and how effectively do they support regime change abroad? To answer these questions, the book studies the entrepreneurs behind the third wave of democratization, with a focus on the new eastern European democracies - members of the European Union. The study finds that it is not the strongest democracies nor the democracies trying to ensure their survival in a neighborhood of nondemocracies that become the most active diffusion entrepreneurs. It is, instead, the countries where the organizers of the domestic democratic transitions build strong solidarity movements supporting the spread of democracy abroad that do. The book also draws parallels between their activism abroad and their experiences with democratization and democracy assistance at home. |
| columbia university masters data science: Enriching Urban Spaces with Ambient Computing, the Internet of Things, and Smart City Design Konomi, Shin'ichi, Roussos, George, 2016-10-06 In recent years, the presence of ubiquitous computing has increasingly integrated into the lives of people in modern society. As these technologies become more pervasive, new opportunities open for making citizens’ environments more comfortable, convenient, and efficient. Enriching Urban Spaces with Ambient Computing, the Internet of Things, and Smart City Design is a pivotal reference source for the latest scholarly material on the interaction between people and computing systems in contemporary society, showcasing how ubiquitous computing influences and shapes urban environments. Highlighting the impacts of these emerging technologies from an interdisciplinary perspective, this book is ideally designed for professionals, researchers, academicians, and practitioners interested in the influential state of pervasive computing within urban contexts. |
| columbia university masters data science: 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. |
| columbia university masters data science: College Admissions Data Sourcebook Northeast Edition Bound 2010-11 , 2010-09 |
| columbia university masters data science: 2012-2013 College Admissions Data Sourcebook West Edition , |
| columbia university masters data science: 2012-2013 College Admissions Data Sourcebook Midwest Edition , |
| columbia university masters data science: Global Citizenship, Common Wealth and Uncommon Citizenships , 2018-08-16 This set of essays critically analyze global citizenship by bringing together leading ideas about citizenship and the commons in this time that both needs and resists a global perspective on issues and relations. Education plays a significant role in how we come to address these issues and this volume will contribute to ensuring that equity, global citizenship, and the common wealth provide platforms from which we might engage in transformational, collective work. The authors address the global significance of debates and struggles about belonging and abjection, solidarity and rejection, identification and othering, as well as love and hate. Global citizenship, as a concept and a practice, is now being met with a dangerous call for insularism and a protracted ethno-nationalism based on global economic imperialism, movements for white supremacy and miscegenation, various forms of religious extremism, and identity politics, but which antithetically, also comes from the anti-globalization movement focused on building strong, sustainable communities. We see a taming of citizens that contributes to the taming of what we understand as the public sphere and the commons, the places of cultural, natural, and intellectual resources that are shared and not privately owned. The work of global citizenship education is distinguishable from the processes of a deadly globalization or destruction of the world that responds to the interlocking issues that make life on the planet precarious for human and non-humans everywhere (albeit an unequal precarity). This book is an invitation into a conversation that explores and makes visible some of the hidden chasms of oppression and inequity in the world. It is meant to provoke both argument and activism as we work to secure common spaces that are broadly life-sustaining. Contributors are: Ali A. Abdi, Sung Kyung Ahn, Chouaib El Bouhali, Xochilt Hernández, Carrie Karsgaard, Marlene McKay, Michael O’Sullivan, Christina Palech, Karen Pashby, Karen J. Pheasant-Neganigwane, Thashika Pillay, Ashley Rerrie, Grace J. Rwiza, Toni Samek, Lynette Shultz, Harry Smaller, Crain Soudien, Derek Tannis, and Irene Friesen Wolfstone. |
| columbia university masters data science: 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 |
| columbia university masters data science: Archaeological Oceanography Robert D. Ballard, 2021-09-14 Archaeological Oceanography is the definitive book on the newly emerging field of deep-sea archaeology. Marine archaeologists have been finding and excavating underwater shipwrecks since at least the early 1950s, but until recently their explorations have been restricted to depths considered shallow by oceanographic standards. This book describes the latest advances that enable researchers to probe the secrets of the deep ocean, and the vital contributions these advances offer to archaeology and fields like maritime history and anthropology. Renowned oceanographer Robert Ballard--who stunned the world with his discovery of the Titanic deep in the North Atlantic--has gathered together the pioneers of archaeological oceanography, a cross-disciplinary group of archaeologists, oceanographers, ocean engineers, and anthropologists who have undertaken ambitious expeditions into the deep sea. In this book, they discuss the history of archaeological oceanography and the evolution and use of advanced deep-submergence technology to locate and excavate ancient and modern shipwrecks and cultural and other sites deep under water. They offer examples from their own expeditions and explain the challenges future programs face in obtaining access to the resources needed to carry out this important and exciting research. The contributors are Robert D. Ballard, Ali Can, Dwight F. Coleman, Mike J. Durbin, Ryan Eustace, Brendan Foley, Cathy Giangrande, Todd S. Gregory, Rachel L. Horlings, Jonathan Howland, Kevin McBride, James B. Newman, Dennis Piechota, Oscar Pizarro, Christopher Roman, Hanumant Singh, Cheryl Ward, and Sarah Webster. |
| columbia university masters data science: International Handbook of Research on Teachers and Teaching Lawrence J. Saha, Anthony Gary Dworkin, 2009-04-17 The International Handbook of Research on Teachers and Teaching provides a fresh look at the ever changing nature of the teaching profession throughout the world. This collection of over 70 articles addresses a wide range of issues relevant for understanding the present educational climate in which the accountability of teachers and the standardized testing of students have become dominant. |
| columbia university masters data science: 2012-2013 College Admissions Data Sourcebook Northeast Edition , |
| columbia university masters data science: Data Engineering Best Practices Richard J. Schiller, David Larochelle, 2024-10-11 Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platforms Key Features Architect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectiveness Explore design patterns and use cases to balance roles, technology choices, and processes for a future-proof design Learn from experts to avoid common pitfalls in data engineering projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionRevolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines. You’ll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you’ll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications. By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.What you will learn Architect scalable data solutions within a well-architected framework Implement agile software development processes tailored to your organization's needs Design cloud-based data pipelines for analytics, machine learning, and AI-ready data products Optimize data engineering capabilities to ensure performance and long-term business value Apply best practices for data security, privacy, and compliance Harness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelines Who this book is for If you are a data engineer, ETL developer, or big data engineer who wants to master the principles and techniques of data engineering, this book is for you. A basic understanding of data engineering concepts, ETL processes, and big data technologies is expected. This book is also for professionals who want to explore advanced data engineering practices, including scalable data solutions, agile software development, and cloud-based data processing pipelines. |
Outdoor Clothing, Outerwear & Accessories | Columbia Sportswear
Columbia makes innovative clothing & footwear for all your outdoor adventures. Get Lost Anywhere: 25-40% off almost everything.
New Arrivals - Outdoor Clothing - Columbia Sportswear
Find the latest arrivals in Columbia Sportswear's line of rugged outerwear, footwear & outdoor accessories. 25-40% off almost everything.
Sale - Columbia Sportswear
Great deals on Columbia jackets, shirts, pants and more. Take advantage of marked down prices while they last. 25-40% off almost everything.
Women's Clothing - Columbia Sportswear
Columbia Greater Rewards Ambassadors Lunar Landing Search. Quicklinks 25-40% Off Almost Everything Father's Day Summer New Arrivals Sun Protection Shorts Tamiami Login. Mini …
Columbia Factory Store at Outlets at Orange - Columbia Sportswear
Our Columbia factory store is located in Orange, California at the Outlets at Orange. We carry innovative outerwear, sportswear, footwear, and accessories for outdoor enthusiasts of all …
Men's Clothing - Hiking Clothing & Accessories - Columbia …
Columbia Greater Rewards Ambassadors Lunar Landing Search. Quicklinks 25-40% Off Almost Everything Father's Day Summer New Arrivals Sun Protection Shorts Tamiami Login. Mini …
Columbia Sportswear | Locations
Find a Columbia Sportswear location near you. spacer. Shop. Columbia 166 stores in United States. Find a Location. All stores; Alabama (1) Arizona (3) California (22) Colorado (6) …
Shop Women's Clothing - Columbia Sportswear
Shop Columbia's full selection of women's clothing and accessories, including jackets, shirts, pants, shoes, and more. 25-40% off almost everything.
Web Deals - Online Deals - Columbia Sportswear
Shop online through the official Columbia Sportswear website. Find men's jackets, shoes, men's boots, pants, fleece sweaters and shirts.
Columbia Sportswear | Miami, Florida
Columbia 1 store in Miami. Find a Location. All stores; Florida; Miami; Columbia Factory Store. Open Now closes at 9:00 PM. 11401 Northwest 12th Street. Ste 336. Miami, FL 33172. US …
Outdoor Clothing, Outerwear & Accessories | Columbia Sportswear
Columbia makes innovative clothing & footwear for all your outdoor adventures. Get Lost Anywhere: 25-40% off almost everything.
New Arrivals - Outdoor Clothing - Columbia Sportswear
Find the latest arrivals in Columbia Sportswear's line of rugged outerwear, footwear & outdoor accessories. 25-40% off almost everything.
Sale - Columbia Sportswear
Great deals on Columbia jackets, shirts, pants and more. Take advantage of marked down prices while they last. 25-40% off almost everything.
Women's Clothing - Columbia Sportswear
Columbia Greater Rewards Ambassadors Lunar Landing Search. Quicklinks 25-40% Off Almost Everything Father's Day Summer New Arrivals Sun Protection Shorts Tamiami Login. Mini …
Columbia Factory Store at Outlets at Orange - Columbia Sportswear
Our Columbia factory store is located in Orange, California at the Outlets at Orange. We carry innovative outerwear, sportswear, footwear, and accessories for outdoor enthusiasts of all …
Men's Clothing - Hiking Clothing & Accessories - Columbia …
Columbia Greater Rewards Ambassadors Lunar Landing Search. Quicklinks 25-40% Off Almost Everything Father's Day Summer New Arrivals Sun Protection Shorts Tamiami Login. Mini …
Columbia Sportswear | Locations
Find a Columbia Sportswear location near you. spacer. Shop. Columbia 166 stores in United States. Find a Location. All stores; Alabama (1) Arizona (3) California (22) Colorado (6) …
Shop Women's Clothing - Columbia Sportswear
Shop Columbia's full selection of women's clothing and accessories, including jackets, shirts, pants, shoes, and more. 25-40% off almost everything.
Web Deals - Online Deals - Columbia Sportswear
Shop online through the official Columbia Sportswear website. Find men's jackets, shoes, men's boots, pants, fleece sweaters and shirts.
Columbia Sportswear | Miami, Florida
Columbia 1 store in Miami. Find a Location. All stores; Florida; Miami; Columbia Factory Store. Open Now closes at 9:00 PM. 11401 Northwest 12th Street. Ste 336. Miami, FL 33172. US …
