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| data science bootcamp 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 bootcamp 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 bootcamp columbia: Environmental Epigenetics L. Joseph Su, Tung-chin Chiang, 2015-05-18 This book examines the toxicological and health implications of environmental epigenetics and provides knowledge through an interdisciplinary approach. Included in this volume are chapters outlining various environmental risk factors such as phthalates and dietary components, life states such as pregnancy and ageing, hormonal and metabolic considerations and specific disease risks such as cancer cardiovascular diseases and other non-communicable diseases. Environmental Epigenetics imparts integrative knowledge of the science of epigenetics and the issues raised in environmental epidemiology. This book is intended to serve both as a reference compendium on environmental epigenetics for scientists in academia, industry and laboratories and as a textbook for graduate level environmental health courses. Environmental Epigenetics imparts integrative knowledge of the science of epigenetics and the issues raised in environmental epidemiology. This book is intended to serve both as a reference compendium on environmental epigenetics for scientists in academia, industry and laboratories and as a textbook for graduate level environmental health courses. |
| data science bootcamp columbia: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data science bootcamp columbia: JavaScript & jQuery Jon Duckett, 2014-07-21 Jon Duckett’s best-selling, full-color introduction to JavaScript—filled with techniques to make websites more interactive and engaging Learn JavaScript and jQuery from the author who has inspired hundreds of thousands of beginner-to-intermediate coders. Build upon your HTML and CSS foundation and take the next step in your programming journey with JavaScript. The world runs on JavaScript and the most influential tech companies are looking for new and experienced programmers alike to bring their websites to life. Finding the right resources online can be overwhelming. Take a confident step in the right direction by choosing the simplicity of JavaScript & jQuery: Interactive Front-End Web Development by veteran web developer and programmer Jon Duckett. Widely regarded for setting a new standard for those looking to learn and master web development, Jon Duckett has inspired web developers through his inventive teaching format pioneered in his bestselling HTML & CSS: Design and Build Websites. He also has helped global brands like Philips, Nike, and Xerox create innovative digital solutions, designing and delivering web and mobile projects with impact and the customer at the forefront. In JavaScript & jQuery, Duckett shares his real-world insights in his unique and highly visual style: Provides an efficient and user-friendly structure that allows readers to progress through the chapters in a self-paced format Combines full-color design graphics and engaging photography to explain the topics in an in-depth yet straightforward manner Recreates techniques seen on other websites such as sliders, content filters, form validation, Ajax content updates, and much more Is perfect for anyone looking to create web applications and games, design mobile apps, or redesign a website using popular web development tools JavaScript & jQuery is clear and actionable, providing organized instruction in ways that other online courses, tutorials, and books have yet to replicate. For readers seeking a personable yet professional guide to using JavaScript in the real world, this one-of-a-kind guide is for you. JavaScript & jQuery is also available as part of two hardcover and paperback sets depending on your web design and development needs: Web Design with HTML, CSS, JavaScript, and jQuery Set Paperback: 9781118907443 Hardcover: 9781119038634 Front-End Back-End Development with HTML, CSS, JavaScript, jQuery, PHP, and MySQL Set Paperback: 9781119813095 Hardcover: 9781119813088 |
| data science bootcamp columbia: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-24 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| data science bootcamp columbia: Applied Machine Learning David Forsyth, 2019-07-12 Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one’s own code. A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use). Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of:• classification using standard machinery (naive bayes; nearest neighbor; SVM)• clustering and vector quantization (largely as in PSCS)• PCA (largely as in PSCS)• variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis)• linear regression (largely as in PSCS)• generalized linear models including logistic regression• model selection with Lasso, elasticnet• robustness and m-estimators• Markov chains and HMM’s (largely as in PSCS)• EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once they’ve been through that, the next one is easy• simple graphical models (in the variational inference section)• classification with neural networks, with a particular emphasis onimage classification• autoencoding with neural networks• structure learning |
| data science bootcamp 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 bootcamp columbia: Foundations of Machine Learning, second edition Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, 2018-12-25 A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition. |
| data science bootcamp columbia: Learning SQL Alan Beaulieu, 2009-04-11 Updated for the latest database management systems -- including MySQL 6.0, Oracle 11g, and Microsoft's SQL Server 2008 -- this introductory guide will get you up and running with SQL quickly. Whether you need to write database applications, perform administrative tasks, or generate reports, Learning SQL, Second Edition, will help you easily master all the SQL fundamentals. Each chapter presents a self-contained lesson on a key SQL concept or technique, with numerous illustrations and annotated examples. Exercises at the end of each chapter let you practice the skills you learn. With this book, you will: Move quickly through SQL basics and learn several advanced features Use SQL data statements to generate, manipulate, and retrieve data Create database objects, such as tables, indexes, and constraints, using SQL schema statements Learn how data sets interact with queries, and understand the importance of subqueries Convert and manipulate data with SQL's built-in functions, and use conditional logic in data statements Knowledge of SQL is a must for interacting with data. With Learning SQL, you'll quickly learn how to put the power and flexibility of this language to work. |
| data science bootcamp columbia: Engineering Software as a Service Armando Fox, David A. Patterson, 2016 (NOTE: this Beta Edition may contain errors. See http://saasbook.info for details.) A one-semester college course in software engineering focusing on cloud computing, software as a service (SaaS), and Agile development using Extreme Programming (XP). This book is neither a step-by-step tutorial nor a reference book. Instead, our goal is to bring a diverse set of software engineering topics together into a single narrative, help readers understand the most important ideas through concrete examples and a learn-by-doing approach, and teach readers enough about each topic to get them started in the field. Courseware for doing the work in the book is available as a virtual machine image that can be downloaded or deployed in the cloud. A free MOOC (massively open online course) at saas-class.org follows the book's content and adds programming assignments and quizzes. See http://saasbook.info for details.(NOTE: this Beta Edition may contain errors. See http://saasbook.info for details.) A one-semester college course in software engineering focusing on cloud computing, software as a service (SaaS), and Agile development using Extreme Programming (XP). This book is neither a step-by-step tutorial nor a reference book. Instead, our goal is to bring a diverse set of software engineering topics together into a single narrative, help readers understand the most important ideas through concrete examples and a learn-by-doing approach, and teach readers enough about each topic to get them started in the field. Courseware for doing the work in the book is available as a virtual machine image that can be downloaded or deployed in the cloud. A free MOOC (massively open online course) at saas-class.org follows the book's content and adds programming assignments and quizzes. See http://saasbook.info for details. |
| data science bootcamp columbia: Deep Learning Illustrated Jon Krohn, Grant Beyleveld, Aglaé Bassens, 2019-08-05 The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come. – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| data science bootcamp 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 bootcamp 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 bootcamp columbia: Numbersense: How to Use Big Data to Your Advantage Kaiser Fung, 2013-07-12 How to make simple sense of complex statistics--from the author of Numbers Rule Your World We live in a world of Big Data--and it's getting bigger every day. Virtually every choice we make hinges on how someone generates data . . . and how someone else interprets it--whether we realize it or not. Where do you send your child for the best education? Big Data. Which airline should you choose to ensure a timely arrival? Big Data. Who will you vote for in the next election? Big Data. The problem is, the more data we have, the more difficult it is to interpret it. From world leaders to average citizens, everyone is prone to making critical decisions based on poor data interpretations. In Numbersense, expert statistician Kaiser Fung explains when you should accept the conclusions of the Big Data experts--and when you should say, Wait . . . what? He delves deeply into a wide range of topics, offering the answers to important questions, such as: How does the college ranking system really work? Can an obesity measure solve America's biggest healthcare crisis? Should you trust current unemployment data issued by the government? How do you improve your fantasy sports team? Should you worry about businesses that track your data? Don't take for granted statements made in the media, by our leaders, or even by your best friend. We're on information overload today, and there's a lot of bad information out there. Numbersense gives you the insight into how Big Data interpretation works--and how it too often doesn't work. You won't come away with the skills of a professional statistician. But you will have a keen understanding of the data traps even the best statisticians can fall into, and you'll trust the mental alarm that goes off in your head when something just doesn't seem to add up. Praise for Numbersense Numbersense correctly puts the emphasis not on the size of big data, but on the analysis of it. Lots of fun stories, plenty of lessons learned—in short, a great way to acquire your own sense of numbers! Thomas H. Davenport, coauthor of Competing on Analytics and President’s Distinguished Professor of IT and Management, Babson College Kaiser’s accessible business book will blow your mind like no other. You’ll be smarter, and you won’t even realize it. Buy. It. Now. Avinash Kaushik, Digital Marketing Evangelist, Google, and author, Web Analytics 2.0 Each story in Numbersense goes deep into what you have to think about before you trust the numbers. Kaiser Fung ably demonstrates that it takes skill and resourcefulness to make the numbers confess their meaning. John Sall, Executive Vice President, SAS Institute Kaiser Fung breaks the bad news—a ton more data is no panacea—but then has got your back, revealing the pitfalls of analysis with stimulating stories from the front lines of business, politics, health care, government, and education. The remedy isn’t an advanced degree, nor is it common sense. You need Numbersense. Eric Siegel, founder, Predictive Analytics World, and author, Predictive Analytics I laughed my way through this superb-useful-fun book and learned and relearned a lot. Highly recommended! Tom Peters, author of In Search of Excellence |
| data science bootcamp columbia: Business Statistics for Contemporary Decision Making Ignacio Castillo, Ken Black, Tiffany Bayley, 2023-05-08 Show students why business statistics is an increasingly important business skill through a student-friendly pedagogy. In this fourth Canadian edition of Business Statistics For Contemporary Decision Making authors Ken Black, Tiffany Bayley, and Ignacio Castillo uses current real-world data to equip students with the business analytics techniques and quantitative decision-making skills required to make smart decisions in today's workplace. |
| data science bootcamp 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 bootcamp columbia: Artificial Intelligence with Python Prateek Joshi, 2017-01-27 Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application. |
| data science bootcamp columbia: Smartups Rob Ryan, 2002 Ryan focuses on methods he has developed over the years for building a sustainable business that makes money. He shows how to turn an idea into real product. |
| data science bootcamp columbia: Data Mining and Predictive Analytics Daniel T. Larose, 2015-02-19 Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives. |
| data science bootcamp 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 bootcamp columbia: All the Sad Young Literary Men Keith Gessen, 2008-04-10 By the author of A Terrible Country and Raising Raffi, a novel of love, sadness, wasted youth, and literary and intellectual ambition—wincingly funny (Vogue) Keith Gessen is a brave and trenchant new literary voice. Known as an award-winning translator of Russian and a book reviewer for publications including The New Yorker and The New York Times, Gessen makes his debut with this critically acclaimed novel, a charming yet scathing portrait of young adulthood at the opening of the twenty-first century. The novel charts the lives of Sam, Mark, and Keith as they overthink their college years, underthink their love lives, and struggle to find a semblance of maturity, responsibility, and even literary fame. |
| data science bootcamp columbia: The Official ACT Prep Guide 2020 - 2021, (Book + 5 Practice Tests + Bonus Online Content) ACT, 2020-04-21 The only guide from the ACT organization, the makers of the exam, with 5 genuine, full-length practice tests in print and online. The Official ACT Prep Guide 2020-2021 is the only guide from the makers of the exam and it includes actual ACT test forms (taken from past ACT exams). It offers 5 actual ACT tests (all with optional writing tests) so you can practice at your own pace. To help you review, this guide provides detailed explanations for every answer and practical tips on how to boost your score on the English, math, reading, science, and optional writing tests. The test creators also created online resources accessible through this book. You can practice online with 5 full length practice tests to mimic the test day experience. These test questions can be organized, filtered, and tracked to test your exam performance. Get ready for test day with this bestselling guide to the ACT. The Official ACT Prep Guide 2020-2021 will help you feel comfortable, confident, and prepared to do your best to ace the ACT! The Official ACT Prep Guide 2020-2021 includes: Information about the September 2020 ACT enhancements Real ACT test forms used in previous years’ exams Five full-length tests available in the book and online, including one NEW full-length test with optional writing test Online practice that mimics the testing experience Customizable questions bank with detailed answer explanations Helpful advice for test day |
| data science bootcamp columbia: Child Data Citizen Veronica Barassi, 2020-12-22 An examination of the datafication of family life--in particular, the construction of our children into data subjects. Our families are being turned into data, as the digital traces we leave are shared, sold, and commodified. Children are datafied even before birth, with pregnancy apps and social media postings, and then tracked through babyhood with learning apps, smart home devices, and medical records. If we want to understand the emergence of the datafied citizen, Veronica Barassi argues, we should look at the first generation of datafied natives: our children. In Child Data Citizen, she examines the construction of children into data subjects, describing how their personal information is collected, archived, sold, and aggregated into unique profiles that can follow them across a lifetime. |
| data science bootcamp columbia: Practical Programming Paul Gries, Jennifer Campbell, Jason Montojo, 2013 Previous edition: published as by Jennifer Campbell ... [et al]. 2009. |
| data science bootcamp columbia: Head First C# Andrew Stellman, Jennifer Greene, 2020-12-04 What will you learn from this book? Dive into C# and create apps, user interfaces, games, and more using this fun and highly visual introduction to C#, .NET Core, and Visual Studio. With this completely updated guide, which covers C# 8.0 and Visual Studio 2019, beginning programmers like you will build a fully functional game in the opening chapter. Then you'll learn how to use classes and object-oriented programming, create 3D games in Unity, and query data with LINQ. And you'll do it all by solving puzzles, doing hands-on exercises, and building real-world applications. By the time you're done, you'll be a solid C# programmer--and you'll have a great time along the way! What's so special about this book? Based on the latest research in cognitive science and learning theory, Head First C# uses a visually rich format to engage your mind rather than a text-heavy approach that puts you to sleep. Why waste your time struggling with new concepts? This multisensory learning experience is designed for the way your brain really works. |
| data science bootcamp columbia: Informatics Education in Healthcare Eta S. Berner, 2020-10-19 This heavily revised second edition defines the current state of the art for informatics education in medicine and healthcare. This field has continued to undergo considerable changes as the field of informatics continues to evolve. The book features extensively revised chapters addressing the latest developments in areas including relevant informatics concepts for those who work in health information technology and those teaching informatics courses in clinical settings, techniques for teaching informatics with limited resources, and the use of online modalities in bioinformatics research education. New topics covered include how to get appropriate accreditation for an informatics program, data science and bioinformatics education, and undergraduate health informatics education. Informatics Education in Healthcare: Lessons Learned addresses the broad range of informatics education programs and available techniques for teaching informatics. It therefore provides a valuable reference for all involved in informatics education. |
| data science bootcamp columbia: The Winners Ed. 50 Innsbruck Editora, 2022-12-20 Ela inovou o setor varejista alimentício na Bahia. |
| data science bootcamp columbia: Artificial Intelligence and the Future of Defense Stephan De Spiegeleire, Matthijs Maas, Tim Sweijs, 2017-05-17 Artificial intelligence (AI) is on everybody’s minds these days. Most of the world’s leading companies are making massive investments in it. Governments are scrambling to catch up. Every single one of us who uses Google Search or any of the new digital assistants on our smartphones has witnessed first-hand how quickly these developments now go. Many analysts foresee truly disruptive changes in education, employment, health, knowledge generation, mobility, etc. But what will AI mean for defense and security? In a new study HCSS offers a unique perspective on this question. Most studies to date quickly jump from AI to autonomous (mostly weapon) systems. They anticipate future armed forces that mostly resemble today’s armed forces, engaging in fairly similar types of activities with a still primarily industrial-kinetic capability bundle that would increasingly be AI-augmented. The authors of this study argue that AI may have a far more transformational impact on defense and security whereby new incarnations of ‘armed force’ start doing different things in novel ways. The report sketches a much broader option space within which defense and security organizations (DSOs) may wish to invest in successive generations of AI technologies. It suggests that some of the most promising investment opportunities to start generating the sustainable security effects that our polities, societies and economies expect may lie in in the realms of prevention and resilience. Also in those areas any large-scale application of AI will have to result from a preliminary open-minded (on all sides) public debate on its legal, ethical and privacy implications. The authors submit, however, that such a debate would be more fruitful than the current heated discussions about ‘killer drones’ or robots. Finally, the study suggests that the advent of artificial super-intelligence (i.e. AI that is superior across the board to human intelligence), which many experts now put firmly within the longer-term planning horizons of our DSOs, presents us with unprecedented risks but also opportunities that we have to start to explore. The report contains an overview of the role that ‘intelligence’ - the computational part of the ability to achieve goals in the world - has played in defense and security throughout human history; a primer on AI (what it is, where it comes from and where it stands today - in both civilian and military contexts); a discussion of the broad option space for DSOs it opens up; 12 illustrative use cases across that option space; and a set of recommendations for - especially - small- and medium sized defense and security organizations. |
| data science bootcamp columbia: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| data science bootcamp columbia: Score Higher on the UCAT Kaplan Test Prep, 2020-04-07 The Expert Guide from Kaplan for 2021 entry One test stands between you and a place at the medical school of your dreams: the UCAT. With 1,500 questions, test-like practice exams, a question bank, and online test updates, Kaplan’s Score Higher on the UCAT, sixth edition, will help build your confidence and make sure you achieve a high score. We know it's crucial that you go into your UCAT exam equipped with the most up-to-date information available. Score Higher on the UCAT comes with access to additional online resources, including any recent exam changes, hundreds of questions, an online question bank, and a mock online test with full worked answers to ensure that there are no surprises waiting for you on test day. The Most Practice 1,500 questions in the book and online—more than any other UCAT book Three full-length tests: one mock online test to help you practise for speed and accuracy in a test-like interface, and two tests with worked answers in the book Online question bank to fine-tune and master your performance on specific question types Expert Guidance The authors of Score Higher on the UCAT have helped thousands of students prepare for the exam. They offer invaluable tips and strategies for every section of the test, helping you to avoid the common pitfalls that trip up other UCAT students. We invented test preparation—Kaplan (www.kaptest.co.uk) has been helping students for 80 years. Our proven strategies have helped legions of students achieve their dreams. |
| data science bootcamp columbia: Python for MBAs Mattan Griffel, Daniel Guetta, 2021-05-04 From the ads that track us to the maps that guide us, the twenty-first century runs on code. The business world is no different. Programming has become one of the fastest-growing topics at business schools around the world. An increasing number of MBAs are choosing to pursue careers in tech. For them and other professionals, having some basic coding knowledge is a must. This book is an introduction to programming with Python for MBA students and others in business positions who need a crash course. One of the most popular programming languages, Python is used for tasks such as building and running websites, data analysis, machine learning, and natural-language processing. Drawing on years of experience providing instruction in this material at Columbia Business School as well as extensive backgrounds in technology, entrepreneurship, and consulting, Mattan Griffel and Daniel Guetta teach the basics of programming from scratch. Beginning with fundamentals such as variables, strings, lists, and functions, they build up to data analytics and practical ways to derive value from large and complex datasets. They focus on business use cases throughout, using the real-world example of a major restaurant chain to offer a concrete look at what Python can do. Written for business students with no previous coding experience and those in business roles that include coding or working with coding teams, Python for MBAs is an indispensable introduction to a versatile and powerful programming language. |
| data science bootcamp columbia: AI in Entertainment: Revolutionizing Content, Gaming, and Special Effects DIZZY DAVIDSON, 2024-07-30 Struggling to fully grasp how AI is transforming the entertainment industry? Wondering how smart technologies are revolutionizing content, gaming, and special effects? Look no further! “AI in Entertainment: Revolutionizing Content, Gaming, and Special Effects” is your ultimate guide to understanding the profound impact of AI on the entertainment world. This book breaks down complex AI concepts into easy-to-understand insights and practical applications that can elevate your creative projects. Benefits of Reading This Book: Unlock AI’s Potential: Learn how AI can enhance content recommendations, improve game design, and create stunning special effects. Stay Ahead in the Industry: Understand the latest AI trends and technologies shaping the future of entertainment. Practical Applications: Discover real-world examples and case studies that illustrate AI’s transformative power. This book is essential for anyone looking to harness AI’s capabilities in the entertainment sector. Whether you’re a content creator, game developer, or media professional, you’ll find valuable insights and actionable strategies to implement AI effectively. Why This Book is Essential: Comprehensive Coverage: From AI basics to advanced applications, this book covers it all. Expert Insights: Written by industry experts, it provides authoritative knowledge and practical advice. Engaging Content: Easy-to-understand language and engaging examples make complex concepts accessible. Bullet Points Transform Your Content: Learn how AI can revolutionize content recommendations and creation. Enhance Gaming: Discover AI strategies that improve game design and player experience. Create Stunning Effects: See how AI is used to create breathtaking special effects in movies and TV shows. Stay Competitive: Keep up with the latest AI trends and technologies in entertainment. Expert Advice: Gain insights from industry leaders and AI experts. Call to Action: Don’t miss out on the opportunity to revolutionize your creative projects with AI. Get your copy of “AI in Entertainment: Revolutionizing Content, Gaming, and Special Effects” today and become an AI-savvy professional! |
| data science bootcamp columbia: The Latino Student's Guide to STEM Careers Laura I. Rendón, Vijay Kanagala, 2017-09-08 This book is an essential resource that Latino/a students and families need to make the best decisions about entering and succeeding in a STEM career. It can also serve to aid faculty, counselors, and advisors to assist students at every step of entering and completing a STEM career. As a fast-growing, major segment of the U.S. population, the next generation of Latinos and Latinas could be key to future American advances in science and technology. With the appropriate encouragement for Latinos/as to enter science, technology, engineering, and mathematics (STEM) careers, they can become the creative innovators who will produce technological advances we all need and can enjoy—from faster tech devices to more energy efficient transportation to cures for diseases and medical conditions. This book presents a compelling case that the nation's Hispanic population must be better represented in STEM careers and that the future of America's technological advances may well depend on the Latino/a population. It focuses on the importance of STEM education for Latinos/as and provides a comprehensive array of the most current information students and families need to make informed decisions about entering and succeeding in a STEM career. Students, families, and educators will fully understand why STEM is so important for Latinos/as, how to plan for a career in STEM, how to pay for and succeed in college, and how to choose a career in STEM. The book also includes compelling testimonials of Latino/a students who have completed a STEM major that offer proof that Latinos/as can overcome life challenges to succeed in STEM fields. |
| data science bootcamp columbia: Data-Driven Science and Engineering Steven L. Brunton, J. Nathan Kutz, 2022-05-05 A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®. |
| data science bootcamp columbia: The History of Statistics Stephen M. Stigler, 1990-03-01 This magnificent book is the first comprehensive history of statistics from its beginnings around 1700 to its emergence as a distinct and mature discipline around 1900. Stephen M. Stigler shows how statistics arose from the interplay of mathematical concepts and the needs of several applied sciences including astronomy, geodesy, experimental psychology, genetics, and sociology. He addresses many intriguing questions: How did scientists learn to combine measurements made under different conditions? And how were they led to use probability theory to measure the accuracy of the result? Why were statistical methods used successfully in astronomy long before they began to play a significant role in the social sciences? How could the introduction of least squares predate the discovery of regression by more than eighty years? On what grounds can the major works of men such as Bernoulli, De Moivre, Bayes, Quetelet, and Lexis be considered partial failures, while those of Laplace, Galton, Edgeworth, Pearson, and Yule are counted as successes? How did Galton’s probability machine (the quincunx) provide him with the key to the major advance of the last half of the nineteenth century? Stigler’s emphasis is upon how, when, and where the methods of probability theory were developed for measuring uncertainty in experimental and observational science, for reducing uncertainty, and as a conceptual framework for quantitative studies in the social sciences. He describes with care the scientific context in which the different methods evolved and identifies the problems (conceptual or mathematical) that retarded the growth of mathematical statistics and the conceptual developments that permitted major breakthroughs. Statisticians, historians of science, and social and behavioral scientists will gain from this book a deeper understanding of the use of statistical methods and a better grasp of the promise and limitations of such techniques. The product of ten years of research, The History of Statistics will appeal to all who are interested in the humanistic study of science. |
| data science bootcamp columbia: Not Exactly Lying Andie Tucher, 2022-03-29 Winner, 2023 Columbia University Press Distinguished Book Award Winner, 2023 Frank Luther Mott / Kappa Tau Alpha Research Award Winner, 2023 Journalism Studies Division Book Award, International Communication Association Winner, 2023 History Book Award, Association for Education in Journalism and Mass Communication Long before the current preoccupation with “fake news,” American newspapers routinely ran stories that were not quite, strictly speaking, true. Today, a firm boundary between fact and fakery is a hallmark of journalistic practice, yet for many readers and publishers across more than three centuries, this distinction has seemed slippery or even irrelevant. From fibs about royal incest in America’s first newspaper to social-media-driven conspiracy theories surrounding Barack Obama’s birthplace, Andie Tucher explores how American audiences have argued over what’s real and what’s not—and why that matters for democracy. Early American journalism was characterized by a hodgepodge of straightforward reporting, partisan broadsides, humbug, tall tales, and embellishment. Around the start of the twentieth century, journalists who were determined to improve the reputation of their craft established professional norms and the goal of objectivity. However, Tucher argues, the creation of outward forms of factuality unleashed new opportunities for falsehood: News doesn’t have to be true as long as it looks true. Propaganda, disinformation, and advocacy—whether in print, on the radio, on television, or online—could be crafted to resemble the real thing. Dressed up in legitimate journalistic conventions, this “fake journalism” became inextricably bound up with right-wing politics, to the point where it has become an essential driver of political polarization. Shedding light on the long history of today’s disputes over disinformation, Not Exactly Lying is a timely consideration of what happens to public life when news is not exactly true. |
| data science bootcamp columbia: Developing Analytic Talent Vincent Granville, 2014-03-24 Learn what it takes to succeed in the the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates. |
| data science bootcamp columbia: Learning Deep Learning Magnus Ekman, 2021-07-19 NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals. -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us. -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| data science bootcamp columbia: The Mathematics of Data Michael W. Mahoney, John C. Duchi, Anna C. Gilbert, 2018-11-15 Nothing provided |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
