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| data science meetup nyc: Data Visualization Made Simple Kristen Sosulski, 2018-09-27 Data Visualization Made Simple is a practical guide to the fundamentals, strategies, and real-world cases for data visualization, an essential skill required in today’s information-rich world. With foundations rooted in statistics, psychology, and computer science, data visualization offers practitioners in almost every field a coherent way to share findings from original research, big data, learning analytics, and more. In nine appealing chapters, the book: examines the role of data graphics in decision-making, sharing information, sparking discussions, and inspiring future research; scrutinizes data graphics, deliberates on the messages they convey, and looks at options for design visualization; and includes cases and interviews to provide a contemporary view of how data graphics are used by professionals across industries Both novices and seasoned designers in education, business, and other areas can use this book’s effective, linear process to develop data visualization literacy and promote exploratory, inquiry-based approaches to visualization problems. |
| data science meetup nyc: R for Everyone Jared P. Lander, 2017-06-13 Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includes Explore R, RStudio, and R packages Use R for math: variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using R’s facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalized linear, and nonlinear models Program basic statistics: mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyze univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp Register your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available. |
| data science meetup nyc: 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 meetup nyc: Getting Started in Data Science Ayodele Odubela, 2020-12-01 Data Science is one of the sexiest jobs of the 21st Century, but few resources are geared towards learners with no prior experience. Getting Started in Data Science simplifies the core of the concepts of Data Science and Machine Learning. This book includes perspectives of a Data Science from someone with a non-traditional route to a Data Science career. Getting Started in Data Science creatively weaves in ethical questions and asks readers to question the harm models can cause as they learn new concepts. Unlike many other books for beginners, this book covers bias and accountability in detail as well as career insight that informs readers of what expectations are in industry Data Science. |
| data science meetup nyc: Data Science at the Command Line Jeroen Janssens, 2021-08-17 This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens provides a Docker image packed with over 100 Unix power tools--useful whether you work with Windows, macOS, or Linux. You'll quickly discover why the command line is an agile, scalable, and extensible technology. Even if you're comfortable processing data with Python or R, you'll learn how to greatly improve your data science workflow by leveraging the command line's power. This book is ideal for data scientists, analysts, engineers, system administrators, and researchers. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on text, CSV, HTML, XML, and JSON files Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow Create your own tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines Model data with dimensionality reduction, regression, and classification algorithms Leverage the command line from Python, Jupyter, R, RStudio, and Apache Spark |
| data science meetup nyc: R for Cloud Computing A Ohri, 2014-11-14 R for Cloud Computing looks at some of the tasks performed by business analysts on the desktop (PC era) and helps the user navigate the wealth of information in R and its 4000 packages as well as transition the same analytics using the cloud. With this information the reader can select both cloud vendors and the sometimes confusing cloud ecosystem as well as the R packages that can help process the analytical tasks with minimum effort, cost and maximum usefulness and customization. The use of Graphical User Interfaces (GUI) and Step by Step screenshot tutorials is emphasized in this book to lessen the famous learning curve in learning R and some of the needless confusion created in cloud computing that hinders its widespread adoption. This will help you kick-start analytics on the cloud including chapters on both cloud computing, R, common tasks performed in analytics including the current focus and scrutiny of Big Data Analytics, setting up and navigating cloud providers. Readers are exposed to a breadth of cloud computing choices and analytics topics without being buried in needless depth. The included references and links allow the reader to pursue business analytics on the cloud easily. It is aimed at practical analytics and is easy to transition from existing analytical set up to the cloud on an open source system based primarily on R. This book is aimed at industry practitioners with basic programming skills and students who want to enter analytics as a profession. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. It will also help researchers and academics but at a practical rather than conceptual level. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy. The cloud computing paradigm is firmly established as the next generation of computing from microprocessors to desktop PCs to cloud. |
| data science meetup nyc: A New World Begins Jeremy Popkin, 2019-12-10 From an award-winning historian, a “vivid” (Wall Street Journal) account of the revolution that created the modern world The French Revolution’s principles of liberty and equality still shape our ideas of a just society—even if, after more than two hundred years, their meaning is more contested than ever before. In A New World Begins, Jeremy D. Popkin offers a riveting account of the revolution that puts the reader in the thick of the debates and the violence that led to the overthrow of the monarchy and the establishment of a new society. We meet Mirabeau, Robespierre, and Danton, in all their brilliance and vengefulness; we witness the failed escape and execution of Louis XVI; we see women demanding equal rights and Black slaves wresting freedom from revolutionaries who hesitated to act on their own principles; and we follow the rise of Napoleon out of the ashes of the Reign of Terror. Based on decades of scholarship, A New World Begins will stand as the definitive treatment of the French Revolution. |
| data science meetup nyc: Technologies of Speculation Sun-ha Hong, 2020-07-28 An inquiry into what we can know in an age of surveillance and algorithms Knitting together contemporary technologies of datafication to reveal a broader, underlying shift in what counts as knowledge, Technologies of Speculation reframes today’s major moral and political controversies around algorithms and artificial intelligence. How many times we toss and turn in our sleep, our voluminous social media activity and location data, our average resting heart rate and body temperature: new technologies of state and self-surveillance promise to re-enlighten the black boxes of our bodies and minds. But Sun-ha Hong suggests that the burden to know and to digest this information at alarming rates is stripping away the liberal subject that ‘knows for themselves’, and risks undermining the pursuit of a rational public. What we choose to track, and what kind of data is extracted from us, shapes a society in which my own experience and sensation is increasingly overruled by data-driven systems. From the rapidly growing Quantified Self community to large-scale dragnet data collection in the name of counter-terrorism and drone warfare, Hong argues that data’s promise of objective truth results in new cultures of speculation. In his analysis of the Snowden affair, Hong demonstrates an entirely new way of thinking through what we could know, and the political and philosophical stakes of the belief that data equates to knowledge. When we simply cannot process all the data at our fingertips, he argues, we look past the inconvenient and the complicated to favor the comprehensible. In the process, racial stereotypes and other longstanding prejudices re-enter our newest technologies by the back door. Hong reveals the moral and philosophical equations embedded into the algorithmic eye that now follows us all. |
| data science meetup nyc: Elegant SciPy Juan Nunez-Iglesias, Stéfan van der Walt, Harriet Dashnow, 2017-08-11 Welcome to Scientific Python and its community. If you’re a scientist who programs with Python, this practical guide not only teaches you the fundamental parts of SciPy and libraries related to it, but also gives you a taste for beautiful, easy-to-read code that you can use in practice. You’ll learn how to write elegant code that’s clear, concise, and efficient at executing the task at hand. Throughout the book, you’ll work with examples from the wider scientific Python ecosystem, using code that illustrates principles outlined in the book. Using actual scientific data, you’ll work on real-world problems with SciPy, NumPy, Pandas, scikit-image, and other Python libraries. Explore the NumPy array, the data structure that underlies numerical scientific computation Use quantile normalization to ensure that measurements fit a specific distribution Represent separate regions in an image with a Region Adjacency Graph Convert temporal or spatial data into frequency domain data with the Fast Fourier Transform Solve sparse matrix problems, including image segmentations, with SciPy’s sparse module Perform linear algebra by using SciPy packages Explore image alignment (registration) with SciPy’s optimize module Process large datasets with Python data streaming primitives and the Toolz library |
| data science meetup nyc: The Innovation Complex Sharon Zukin, 2020-02-03 You hear a lot these days about innovation and entrepreneurship and about how good jobs in tech will save our cities. Yet these common tropes hide a stunning reality: local lives and fortunes are tied to global capital. You see this clearly in metropolises such as San Francisco and New York that have emerged as superstar cities. In these cities, startups bloom, jobs of the future multiply, and a meritocracy trained in digital technology, backed by investors who control deep pools of capital, forms a new class: the tech-financial elite. In The Innovation Complex, the eminent urbanist Sharon Zukin shows the way these forces shape the new urban economy through a rich and illuminating account of the rise of the tech sector in New York City. Drawing from original interviews with venture capitalists, tech evangelists, and economic development officials, she shows how the ecosystem forms and reshapes the city from the ground up. Zukin explores the people and plans that have literally rooted digital technology in the city. That in turn has shaped a workforce, molded a mindset, and generated an archipelago of tech spaces, which in combination have produced a now-hegemonic innovation culture and geography. She begins with the subculture of hackathons and meetups, introduces startup founders and venture capitalists, and explores the transformation of the Brooklyn waterfront from industrial wasteland to innovation coastline. She shows how, far beyond Silicon Valley, cities like New York are shaped by an influential triple helix of business, government, and university leaders--an alliance that joins C. Wright Mills's power elite, real estate developers, and ambitious avatars of academic capitalism. As a result, cities around the world are caught between the demands of the tech economy and communities' desires for growth--a massive and often--insurmountable challenge for those who hope to reap the rewards of innovation's success. |
| data science meetup nyc: みんなのR Jared P. Lander, 2015-06-30 プロのデータサイエンティストから学ぼう！プロのデータサイエンティストである著者が、Rの基礎から最新のモダンなデータ分析まで幅広くかつ丁寧に解説していきます。統計、線形代数、オペレーションズリサーチ、人工知能、機械学習 — たくさんのデータサイエンスのタスクをこなすのにRは必要不可欠なツールです。予測や解析に必要な数多くのアルゴリズムを少ないコードで利用することができ、最近のモダンなデータ解析の挑戦にとても合っています。本書は日常的にRを使ってみたいユーザーに様々な手段を提供しています。実際のデータや興味のある問題を解く際、この本は最後まで役に立つでしょう。 ≪CONTENTS≫1章：Rを手に入れる／2章：Rの環境／3章：Rパッケージ／4章：Rの基本／5章：高度なデータ構造／6章：Rへのデータ取り込み／7章：統計的なグラフィクス／8章：Rの関数を書く／9章：制御文／10章：ループ・Rの方法ではない反復方法／11章：グルーピング操作／12章：データ整形／13章：文字列操作／14章：確率分布／15章：基本統計／16章：線形モデル／17章：一般化線形モデル／18章：モデル評価／19章：正則化と縮小（シュリンケージ）／20章：非線形モデル／21章：時系列と自己相関／22章：クラスタリング／23章：knitrによる再現性・レポートとスライドショー／24章：Rパッケージの構築／付録A：情報リソース／付録B：用語集 ※2016/02/10：ファイルを更新しました。購入済みの方は再ダウンロードをお願いします。 |
| data science meetup nyc: Too Big to Ignore Phil Simon, 2013-03-18 Introduction: This ain't your father's data -- Data 101 and the data deluge -- Demystifying big data -- The elements of persuasion : big data techniquies -- Big data solutions -- Case studies : the big rewards of big data -- Taking the big plunge -- Big data : big issues and big problems -- Looking forward : the future of big data -- Final thoughts. |
| data science meetup nyc: Designing Data Visualizations Noah Iliinsky, Julie Steele, 2011-09-16 Data visualization is an efficient and effective medium for communicating large amounts of information, but the design process can often seem like an unexplainable creative endeavor. This concise book aims to demystify the design process by showing you how to use a linear decision-making process to encode your information visually. Delve into different kinds of visualization, including infographics and visual art, and explore the influences at work in each one. Then learn how to apply these concepts to your design process. Learn data visualization classifications, including explanatory, exploratory, and hybrid Discover how three fundamental influences—the designer, the reader, and the data—shape what you create Learn how to describe the specific goal of your visualization and identify the supporting data Decide the spatial position of your visual entities with axes Encode the various dimensions of your data with appropriate visual properties, such as shape and color See visualization best practices and suggestions for encoding various specific data types |
| data science meetup nyc: Reinventing Banking and Finance Helene Panzarino, Alessandro Hatami, 2020-11-03 Named as the best overall book on banking of 2022 by Investopedia. The finance industry is currently going through a digital revolution, with new and developing technology transforming the world of banking and financial services beyond recognition. Banks and financial institutions worldwide recognize the pressing need to innovate to avoid disruption or displacement by highly agile and often smaller fintech companies. Reinventing Banking and Finance is an essential guide for finance professionals to current trends in fintech, innovation frameworks, the challenges of outsourcing or embedding innovation, and how to effectively collaborate with other organizations. Beginning with the history and background of how banking got to the era of fintech, the book provides a thorough overview of the global fintech ecosystem and the drivers behind innovation in technologies, business models and distribution channels. Examples of key institutions and interviews with innovators and experts shine a light on key financial innovation hubs in UK, US, China, Israel and more, and offer advice for institutions looking to choose the right market for their needs. Covering genuine innovations in AI, machine learning, blockchain and digital identity, Reinventing Banking and Finance offers expert insight into navigating the complex and multi-layered finance industry. |
| data science meetup nyc: The Ethical Algorithm Michael Kearns, Aaron Roth, 2020 Algorithms have made our lives more efficient and entertaining--but not without a significant cost. Can we design a better future, one in which societial gains brought about by technology are balanced with the rights of citizens? The Ethical Algorithm offers a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. |
| data science meetup nyc: Mastering Corda Jamiel Sheikh, 2020-10-09 Mastering Corda provides you with a consistent, linear, and paced path to learning Corda and building modern enterprise-grade decentralized applications. Using this book, anyone from a complete blockchain beginner to an experienced blockchain or enterprise architect can rapidly understand and write applications like a pro while exploring the technical nuances and intricacies of the Corda platform. Corda is designed for use cases such as finance and investments, supply chain, healthcare, trade finance, insurance, and real estate that require a high-volume of transactions, scalability, and data privacy. If you have basic Java skills, this book will help you understand blockchain and show how you can get started immediately and be involved in the disruption of the future. With this book, you will: Understand Corda's value proposition and alignment with business strategies--particularly relevant to business executives and architects Dive deep into Corda's architecture and blockchain fundamentals Rapidly gain extensive knowledge of and hands-on experience with building Corda applications Compare and contrast Corda with Bitcoin, Ethereum, and Hyperledger Effectively prepare for the Corda certification exam and job interviews involving blockchain Perform data analytics and machine learning on Corda nodes |
| data science meetup nyc: Screw the Valley Timothy Sprinkle, 2015-01-13 The most exciting high-tech startups are escaping the expensive and inbred environment of Silicon Valley. Welcome to the future. Entrepreneurs know they must embrace innovation to excel—starting with where they locate their new venture. Fortunately, budding companies seeking fertile ground have more options today than ever before. Screw the Valley calls on today's entrepreneurs and aspiring business owners to forget California and explore other options across the country—cities that offer more room to breathe, easier access to funding and talented workers, fewer heads to butt, and less money down the drain. Timothy Sprinkle visits seven areas that offer a superior landscape for tech startups: Detroit New York City Las Vegas Austin Kansas City Raleigh-Durham Boulder Sprinkle gives readers a window into the startup potential in each city, detailing which industries are thriving where, and highlighting the unique appeal and character of each location. Bright ideas are not geographically limited, and innovation is happening every day in cities all over the country. It's time to think outside the box when it comes to startup location. It's time to say Screw the Valley. |
| data science meetup nyc: Python for Excel Felix Zumstein, 2021-03-04 While Excel remains ubiquitous in the business world, recent Microsoft feedback forums are full of requests to include Python as an Excel scripting language. In fact, it's the top feature requested. What makes this combination so compelling? In this hands-on guide, Felix Zumstein--creator of xlwings, a popular open source package for automating Excel with Python--shows experienced Excel users how to integrate these two worlds efficiently. Excel has added quite a few new capabilities over the past couple of years, but its automation language, VBA, stopped evolving a long time ago. Many Excel power users have already adopted Python for daily automation tasks. This guide gets you started. Use Python without extensive programming knowledge Get started with modern tools, including Jupyter notebooks and Visual Studio code Use pandas to acquire, clean, and analyze data and replace typical Excel calculations Automate tedious tasks like consolidation of Excel workbooks and production of Excel reports Use xlwings to build interactive Excel tools that use Python as a calculation engine Connect Excel to databases and CSV files and fetch data from the internet using Python code Use Python as a single tool to replace VBA, Power Query, and Power Pivot |
| data science meetup nyc: Cool Infographics Randy Krum, 2013-10-23 Make information memorable with creative visual design techniques Research shows that visual information is more quickly and easily understood, and much more likely to be remembered. This innovative book presents the design process and the best software tools for creating infographics that communicate. Including a special section on how to construct the increasingly popular infographic resume, the book offers graphic designers, marketers, and business professionals vital information on the most effective ways to present data. Explains why infographics and data visualizations work Shares the tools and techniques for creating great infographics Covers online infographics used for marketing, including social media and search engine optimization (SEO) Shows how to market your skills with a visual, infographic resume Explores the many internal business uses of infographics, including board meeting presentations, annual reports, consumer research statistics, marketing strategies, business plans, and visual explanations of products and services to your customers With Cool Infographics, you'll learn to create infographics to successfully reach your target audience and tell clear stories with your data. |
| data science meetup nyc: The Responsive City Stephen Goldsmith, Susan Crawford, 2014-08-11 Leveraging Big Data and 21st century technology to renew cities and citizenship in America The Responsive City is a guide to civic engagement and governance in the digital age that will help leaders link important breakthroughs in technology and data analytics with age-old lessons of small-group community input to create more agile, competitive, and economically resilient cities. Featuring vivid case studies highlighting the work of pioneers in New York, Boston, Chicago and more, the book provides a compelling model for the future of governance. The book will help mayors, chief technology officers, city administrators, agency directors, civic groups and nonprofit leaders break out of current paradigms to collectively address civic problems. The Responsive City is the culmination of research originating from the Data-Smart City Solutions initiative, an ongoing project at Harvard Kennedy School working to catalyze adoption of data projects on the city level. The book is co-authored by Professor Stephen Goldsmith, director of Data-Smart City Solutions at Harvard Kennedy School, and Professor Susan Crawford, co-director of Harvard's Berkman Center for Internet and Society. Former New York City Mayor Michael Bloomberg penned the book’s foreword. Based on the authors’ experiences and extensive research, The Responsive City explores topics including: Building trust in the public sector and fostering a sustained, collective voice among communities; Using data-smart governance to preempt and predict problems while improving quality of life; Creating efficiencies and saving taxpayer money with digital tools; and Spearheading these new approaches to government with innovative leadership. |
| data science meetup nyc: Rebooting AI Gary Marcus, Ernest Davis, 2019-09-10 Two leaders in the field offer a compelling analysis of the current state of the art and reveal the steps we must take to achieve a robust artificial intelligence that can make our lives better. “Finally, a book that tells us what AI is, what AI is not, and what AI could become if only we are ambitious and creative enough.” —Garry Kasparov, former world chess champion and author of Deep Thinking Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we have been led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer beating a human in Jeopardy! does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements in the field thus far have occurred in closed systems with fixed sets of rules, and these approaches are too narrow to achieve genuine intelligence. The real world, in contrast, is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Taking inspiration from the human mind, Marcus and Davis explain what we need to advance AI to the next level, and suggest that if we are wise along the way, we won't need to worry about a future of machine overlords. If we focus on endowing machines with common sense and deep understanding, rather than simply focusing on statistical analysis and gatherine ever larger collections of data, we will be able to create an AI we can trust—in our homes, our cars, and our doctors' offices. Rebooting AI provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of how a new generation of AI can make our lives better. |
| data science meetup nyc: Practical Object-oriented Design in Ruby Sandi Metz, 2013 The Complete Guide to Writing More Maintainable, Manageable, Pleasing, and Powerful Ruby Applications Ruby's widely admired ease of use has a downside: Too many Ruby and Rails applications have been created without concern for their long-term maintenance or evolution. The Web is awash in Ruby code that is now virtually impossible to change or extend. This text helps you solve that problem by using powerful real-world object-oriented design techniques, which it thoroughly explains using simple and practical Ruby examples. This book focuses squarely on object-oriented Ruby application design. Practical Object-Oriented Design in Ruby will guide you to superior outcomes, whatever your previous Ruby experience. Novice Ruby programmers will find specific rules to live by; intermediate Ruby programmers will find valuable principles they can flexibly interpret and apply; and advanced Ruby programmers will find a common language they can use to lead development and guide their colleagues. This guide will help you Understand how object-oriented programming can help you craft Ruby code that is easier to maintain and upgrade Decide what belongs in a single Ruby class Avoid entangling objects that should be kept separate Define flexible interfaces among objects Reduce programming overhead costs with duck typing Successfully apply inheritance Build objects via composition Design cost-effective tests Solve common problems associated with poorly designed Ruby code |
| data science meetup nyc: Stupid Things I Won't Do When I Get Old Steven Petrow, 2021-06-29 For fans of David Sedaris and Nora Ephron, a humorous, irreverent, and poignant look at the gifts, stereotypes, and inevitable challenges of aging, based on award-winning journalist Steven Petrow's wildly popular New York Times essay, Things I'll Do Differently When I Get Old. Soon after his 50th birthday, Petrow began assembling a list of “things I won’t do when I get old”—mostly a catalog of all the things he thought his then 70-something year old parents were doing wrong. That list, which included “You won’t have to shout at me that I’m deaf,” and “I won’t blame the family dog for my incontinence,” became the basis of this rousing collection of do’s and don’ts, wills and won’ts that is equal parts hilarious, honest, and practical. The fact is, we don’t want to age the way previous generations did. “Old people” hoard. They bore relatives—and strangers alike—with tales of their aches and pains. They insist on driving long after they’ve become a danger to others (and themselves). They eat dinner at 4pm. They swear they don’t need a cane or walker (and guess what happens next). They never, ever apologize. But there is another way... In Stupid Things I Won’t Do When I Get Old, Petrow candidly addresses the fears, frustrations, and stereotypes that accompany aging. He offers a blueprint for the new old age, and an understanding that aging and illness are not the same. As he writes, “I meant the list to serve as a pointed reminder—to me—to make different choices when I eventually cross the threshold to ‘old.’” Getting older is a privilege. This essential guide reveals how to do it with grace, wisdom, humor, and hope. And without hoarding. Praise for Stupid Things I Won't Do When I Get Old: “Unbelievably witty and relatable, I alternated bursting into laughter and placing my hand over my face in horror thinking, Oh my God, is that me? I often say, at this age we have something young people can never have…wisdom. My dear friend, Steven Petrow, has wisdom to share in this honest, funny, wry guide to keep us young at heart, without desperately hanging onto our youth. I am buying this book for all of my friends!” —Suzanne Somers, New York Times bestselling author of A New Way to Age “Stupid Things I Won’t Do When I Get Old is an irreverent, funny, honest look at aging and all the things we take for granted as normal parts of aging. They don’t need to be. If you struggle with getting older and want to find a fresh perspective on lessons learned about what NOT to do as we age, and what TO do to stay young in heart, spirit, mind and body, read this book.” —Mark Hyman, MD, #1 New York Times bestseller author of The Blood Sugar Solution 10-Day Detox Diet, and Head of Strategy and Innovation at the Cleveland Clinic Center for Functional Medicine. “Steven Petrow resolved to do things differently than his parents had when he gets old because he wished they’d been able to enjoy life more. His solution? He created a list! In this book, he shares the secrets to living a full life regardless of our age. It's all about the decisions we make every day. My advice in a nutshell: Read this book and keep it handy.” —“Dear Abby” (Jeanne Phillips), nationally syndicated advice columnist “It’s never too early to imagine what your life will look like as you age. And as I once wrote, ‘We are not hostages to our fate.’ Petrow’s book will help you plan, think, and redefine what it means to get older—and even laugh while doing it.” —Andrew Weil, MD, New York Times bestselling author of Spontaneous Healing and Healthy Aging: A Lifelong Guide to Your Well-Being “Steven Petrow not only has a great attitude about life, he is wise about how to live it. Like me, he says we should embrace our one life 100% and not let a number—our age—get in the way of anything! Steven’s book will help you rethink the word “aging” and approach this next chapter with a positive and proactive attitude. Plus, this book is fun!” —Denise Austin, renowned fitness expert, author, and columnist “Steven’s writing feels like sitting with a friend—one who is unusually gracious, warm and frank.” —Carolyn Hax, author of the nationally syndicated advice column, Carolyn Hax Praise for Steven Petrow: Steven Petrow's Complete Gay & Lesbian Manners helps gays and straights navigate the subtleties of the same-sex world. —People Move over, Emily Post! When it comes to etiquette for members of the gay, lesbian, bisexual and transgender community—as well as their straight friends, family members and coworkers--author and journalist Steven Petrow is the authority. —TIME What could've easily become a novelty book has emerged as an exhaustively researched, essential resource thanks to advice columnist and etiquette expert Steven Petrow. —The Advocate From having kids to planning funerals, Steven Petrow's Complete Gay & Lesbian Manners has most facets of gay life covered. Ms. Post would approve. —Entertainment Weekly An indispensable refresher course...on what's proper in modern...life. —Kirkus Reviews |
| data science meetup nyc: The Business of Belonging David Spinks, 2021-03-23 A tactical primer for any business embarking on the critical work of actively building community.—Seth Godin, Author, This is Marketing This book perfectly marries the psychology of communities, with the hard-earned secrets of someone who's done the real work over many years. David Spinks is the master of this craft.—Nir Eyal, bestselling author of Hooked and Indistractable The rise of the internet has brought with it an inexorable, almost shockingly persistent drive toward community. From the first social networks to the GameStop trading revolution, engaged communities have shown the ability to transform industries. Businesses need to harness that power. As business community expert David Spinks shows in The Business of Belonging: How to Make Community your Competitive Advantage, the successful brands of tomorrow will be those that create authentic connection, giving customers a sense of real belonging and unlocking unprecedented scale as a result. In his career of over 10 years in the business of building community, Spinks has learned what a winning community strategy looks like. From the fundamental concepts—including how community drives measurable business value and what the appropriate metrics are—to high-level community design and practical engagement techniques, The Business of Belonging is an epic journey into the world of community building. This book is for decision makers who want to better understand the value and opportunity of community, and for community professionals who want to level up their strategy. Featuring a foreword by Startup Grind and Bevy cofounder Derek Andersen, it will give you a step-by-step model for strategically planning, creating, facilitating, and measuring communities that drive business growth. Attracting and retaining community members who are also loyal customers, brand evangelists, and leaders—that’s the goal for today’s connected businesses, and this book is the map to getting there. |
| data science meetup nyc: Friendship in the Age of Loneliness Adam Smiley Poswolsky, 2021-05-04 *NEXT BIG IDEA CLUB SUMMER 2021 NOMINEE* After nearly a year of social distancing and lockdown measures, it’s more clear than ever that our friendships and bonds are vital to our health and happiness. This refreshing, positive guide helps you take care of your people and form deep connections in the digital age. We are lonelier than ever. The average American hasn't made a new friend in the last five years. Research has shown that people with close friends are happier, healthier, and live longer than people who lack strong social bonds. But why—when we are seemingly more connected than ever before—can it feel so difficult to keep those bonds alive and well? Why do we spend only four percent of our time with friends? In this warm, inspiring guide, Adam Smiley Poswolsky proposes a new solution for the mounting pressures of modern life: focus on your friendships. Smiley offers practical habits and playful reminders on how to create meaningful connections, make new friends, and deepen relationships. He'll help you develop a healthier relationship with technology, but he'll also encourage you to prioritize real-world experiences, send snail mail, and engage in self-reflective exercises. Written in short, digestible, action-oriented sections, this book reminds us that nurturing old and new friendships is a ritual, a necessity, and one of the most worthwhile things we can do in life. |
| data science meetup nyc: Agile 2 Cliff Berg, Kurt Cagle, Lisa Cooney, Philippa Fewell, Adrian Lander, Raj Nagappan, Murray Robinson, 2021-03-09 Agile is broken. Most Agile transformations struggle. According to an Allied Market Research study, 63% of respondents stated the failure of agile implementation in their organizations. The problems with Agile start at the top of most organizations with executive leadership not getting what agile is or even knowing the difference between success and failure in agile. Agile transformation is a journey, and most of that journey consists of people learning and trying new approaches in their own work. An agile organization can make use of coaches and training to improve their chances of success. But even then, failure remains because many Agile ideas are oversimplifications or interpreted in an extreme way, and many elements essential for success are missing. Coupled with other ideas that have been dogmatically forced on teams, such as agile team rooms, and an overall inertia and resistance to change in the Agile community, the Agile movement is ripe for change since its birth twenty years ago. Agile 2 represents the work of fifteen experienced Agile experts, distilled into Agile 2: The Next Iteration of Agile by seven members of the team. Agile 2 values these pairs of attributes when properly balanced: thoughtfulness and prescription; outcomes and outputs, individuals and teams; business and technical understanding; individual empowerment and good leadership; adaptability and planning. With a new set of Agile principles to take Agile forward over the next 20 years, Agile 2 is applicable beyond software and hardware to all parts of an agile organization including Agile HR, Agile Finance, and so on. Like the original Agile, Agile 2, is just a set of ideas - powerful ideas. To undertake any endeavor, a single set of ideas is not enough. But a single set of ideas can be a powerful guide. |
| data science meetup nyc: Smart Cities, Smart Future Mike Barlow, Cornelia Levy-Bencheton, 2018-10-23 Are you curious about smart cities? You should be! By mid-century, two-thirds of us will live in cities. The world of tomorrow will be a world of cities. But will they be smart cities? Smart cities are complex blends of technologies, systems and services designed and orchestrated to help people lead productive, fulfilling, safe and happy lives. This remarkable book is a window into our shared future. In crisp language and sharp detail, Mike Barlow and Cornelia Lévy-Bencheton explain how smart cities are powerful forces for positive change. With keen eyes and warm hearts, they invite readers to imagine the world of tomorrow, a fascinating world of connected cities and communities. They capture and convey the depth and richness of the worldwide smart city movement. Smart Cities, Smart Future describes the impact of smart city projects on people in towns, cities and nations around the world. The book includes descriptions of ongoing smart city projects in North America, Europe, Asia and the Middle East. Looking Ahead to an Urban World No two smart cities are alike. No one can say with certainty or precision what the term “smart city” means. There is no standard definition or common template. Today, smart cities are works in progress. They emerge from our hopes and our dreams. This book provides you with the knowledge and insight you need to participate in the smart city movement. It explains how smart cities are “systems of systems” and introduces key concepts such as interoperability, open standards, resiliency, agility, adaptability and continuous improvement. Includes Detailed Glossary of Terms and Essential Vocabulary The book includes a detailed comprehensive glossary of essential smart city terms. The glossary will become your indispensable resource as you engage more deeply with the smart city movement and become more involved in planning our common future in an urban world. Carefully Researched and Crisply Written Smart Cities, Smart Future is carefully researched and fully documented. It includes interviews with leaders and experts in multiple disciplines essential to the development of smart cities, towns, regions, states and nations. Written in the clean style of modern journalism, the book offers a strong and compelling narrative of a changing world. It reminds us that we are responsible for choosing our destiny and determining the shape of things to come. The smart city movement is gaining speed and momentum. Read this book, and enjoy the ride! |
| data science meetup nyc: Creating More Effective Graphs Naomi B. Robbins, 2005 A succinct and highly readable guide to creating effective graphs The right graph can be a powerful tool for communicating information, improving a presentation, or conveying your point in print. If your professional endeavors call for you to present data graphically, here's a book that can help you do it more effectively. Creating More Effective Graphs gives you the basic knowledge and techniques required to choose and create appropriate graphs for a broad range of applications. Using real-world examples everyone can relate to, the author draws on her years of experience in graphical data analysis and presentation to highlight some of today's most effective methods. In clear, concise language, the author answers such common questions as: What constitutes an effective graph for communicating data? How do I choose the type of graph that is best for my data? How do I recognize a misleading graph? Why do some graphs have logarithmic scales? In no time you'll graduate from bar graphs and pie charts to graphs that illuminate data like: Dot plots Box plots Scatterplots Linked micromaps Trellis displays Mosaic plots Month plots Scatterplot matrices . . . most of them requiring only inexpensive, easily downloadable software. Whether you're a novice at graphing or already use graphs in your work but want to improve them, Creating More Effective Graphs will help you develop the kind of clear, accurate, and well-designed graphs that will allow your data to be understood. |
| data science meetup nyc: Artificial Intelligence Stuart Russell, Peter Norvig, 2016-09-10 Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. |
| data science meetup nyc: Python for Algorithmic Trading Yves Hilpisch, 2020-11-12 Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading. You'll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading platforms. Some of the biggest buy- and sell-side institutions make heavy use of Python. By exploring options for systematically building and deploying automated algorithmic trading strategies, this book will help you level the playing field. Set up a proper Python environment for algorithmic trading Learn how to retrieve financial data from public and proprietary data sources Explore vectorization for financial analytics with NumPy and pandas Master vectorized backtesting of different algorithmic trading strategies Generate market predictions by using machine learning and deep learning Tackle real-time processing of streaming data with socket programming tools Implement automated algorithmic trading strategies with the OANDA and FXCM trading platforms |
| data science meetup nyc: Urban Analytics Alex D. Singleton, Seth Spielman, David Folch, 2017-11-27 The economic and political situation of cities has shifted in recent years in light of rapid growth amidst infrastructure decline, the suburbanization of poverty and inner city revitalization. At the same time, the way that data are used to understand urban systems has changed dramatically. Urban Analytics offers a field-defining look at the challenges and opportunities of using new and emerging data to study contemporary and future cities through methods including GIS, Remote Sensing, Big Data and Geodemographics. Written in an accessible style and packed with illustrations and interviews from key urban analysts, this is a groundbreaking new textbook for students of urban planning, urban design, geography, and the information sciences. |
| data science meetup nyc: Untangling Smart Cities Luca Mora, Mark Deakin, 2019-07-04 Untangling Smart Cities: From Utopian Dreams to Innovation Systems for a Technology-Enabled Urban Sustainability helps all key stakeholders understand the complex and often conflicting nature of smart city research, offering valuable insights for designing and implementing strategies to improve the smart city decision-making processes. The book drives the reader to a better theoretical and practical comprehension of smart city development, beginning with a thorough and systematic analysis of the research literature published to date. It addition, it provides an in-depth understanding of the entire smart city knowledge domain, revealing a deeply rooted division in its cognitive-epistemological structure as identified by bibliometric insights. Users will find a book that fills the knowledge gap between theory and practice using case study research and empirical evidence drawn from cities considered leaders in innovative smart city practices. |
| data science meetup nyc: Derivatives Analytics with Python Yves Hilpisch, 2015-08-03 Supercharge options analytics and hedging using the power of Python Derivatives Analytics with Python shows you how to implement market-consistent valuation and hedging approaches using advanced financial models, efficient numerical techniques, and the powerful capabilities of the Python programming language. This unique guide offers detailed explanations of all theory, methods, and processes, giving you the background and tools necessary to value stock index options from a sound foundation. You'll find and use self-contained Python scripts and modules and learn how to apply Python to advanced data and derivatives analytics as you benefit from the 5,000+ lines of code that are provided to help you reproduce the results and graphics presented. Coverage includes market data analysis, risk-neutral valuation, Monte Carlo simulation, model calibration, valuation, and dynamic hedging, with models that exhibit stochastic volatility, jump components, stochastic short rates, and more. The companion website features all code and IPython Notebooks for immediate execution and automation. Python is gaining ground in the derivatives analytics space, allowing institutions to quickly and efficiently deliver portfolio, trading, and risk management results. This book is the finance professional's guide to exploiting Python's capabilities for efficient and performing derivatives analytics. Reproduce major stylized facts of equity and options markets yourself Apply Fourier transform techniques and advanced Monte Carlo pricing Calibrate advanced option pricing models to market data Integrate advanced models and numeric methods to dynamically hedge options Recent developments in the Python ecosystem enable analysts to implement analytics tasks as performing as with C or C++, but using only about one-tenth of the code or even less. Derivatives Analytics with Python — Data Analysis, Models, Simulation, Calibration and Hedging shows you what you need to know to supercharge your derivatives and risk analytics efforts. |
| data science meetup nyc: Sculpting Data for ML Rishabh Misra, 2021-01-17 In the contemporary world of Artificial Intelligence and Machine Learning, data is the new oil. For Machine Learning algorithms to work their magic, it is imperative to lay a firm foundation with relevant data. Sculpting Data for ML introduces the readers to the first act of Machine Learning, Dataset Curation. This book puts forward practical tips to identify valuable information from the extensive amount of crude data available at our fingertips. The step-by-step guide accompanies code examples in Python from the extraction of real-world datasets and illustrates ways to hone the skills of extracting meaningful datasets. In addition, the book also dives deep into how data fits into the Machine Learning ecosystem and tries to highlight the impact good quality data can have on the Machine Learning system's performance. What's Inside? * Significance of data in Machine Learning * Identification of relevant data signals * End-to-end process of data collection and dataset construction * Overview of extraction tools like BeautifulSoup and Selenium * Step-by-step guide with Python code examples of real-world use cases * Synopsis of Data Preprocessing and Feature Engineering techniques * Introduction to Machine Learning paradigms from a data perspective This book is for Machine Learning researchers, practitioners, or enthusiasts who want to tackle the data availability challenges to address real-world problems. The authors Jigyasa Grover & Rishabh Misra are Machine Learning Engineers by profession and are passionate about tackling real-world problems leveraging their data curation and ML expertise. The book is endorsed by leading ML experts from both academia and industry. It has forewords by: * Julian McAuley, Associate Professor at University of California San Diego * Laurence Moroney, Lead Artificial Intelligence Advocate at Google * Mengting Wan, Senior Applied Scientist at Microsoft |
| data science meetup nyc: Applied Predictive Modeling Max Kuhn, Kjell Johnson, 2013-05-17 Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. |
| data science meetup nyc: Where Is My Flying Car? J. Storrs Hall, 2021-11-30 From an engineer and futurist, an impassioned account of technological stagnation since the 1970s and an imaginative blueprint for a richer, more abundant future The science fiction of the 1960s promised us a future remade by technological innovation: we’d vacation in geodesic domes on Mars, have meaningful conversations with computers, and drop our children off at school in flying cars. Fast-forward 60 years, and we’re still stuck in traffic in gas-guzzling sedans and boarding the same types of planes we flew in over half a century ago. What happened to the future we were promised? In Where Is My Flying Car?, J. Storrs Hall sets out to answer this deceptively simple question. What starts as an examination of the technical limitations of building flying cars evolves into an investigation of the scientific, technological, and social roots of the economic stagnation that started in the 1970s. From the failure to adopt nuclear energy and the suppression of cold fusion technology to the rise of a counterculture hostile to progress, Hall recounts how our collective ambitions for the future were derailed, with devastating consequences for global wealth creation and distribution. Hall then outlines a framework for a future powered by exponential progress—one in which we build as much in the world of atoms as we do in the world of bits, one rich in abundance and wonder. Drawing on years of original research and personal engineering experience, Where Is My Flying Car?, originally published in 2018, is an urgent, timely analysis of technological progress over the last 50 years and a bold vision for a better future. |
| data science meetup nyc: 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 meetup nyc: Data Pipelines with Apache Airflow Bas P. Harenslak, Julian de Ruiter, 2021-04-27 This book teaches you how to build and maintain effective data pipelines. Youll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. -- |
| data science meetup nyc: Museums and Digital Culture Tula Giannini, Jonathan P. Bowen, 2019-05-06 This book explores how digital culture is transforming museums in the 21st century. Offering a corpus of new evidence for readers to explore, the authors trace the digital evolution of the museum and that of their audiences, now fully immersed in digital life, from the Internet to home and work. In a world where life in code and digits has redefined human information behavior and dominates daily activity and communication, ubiquitous use of digital tools and technology is radically changing the social contexts and purposes of museum exhibitions and collections, the work of museum professionals and the expectations of visitors, real and virtual. Moving beyond their walls, with local and global communities, museums are evolving into highly dynamic, socially aware and relevant institutions as their connections to the global digital ecosystem are strengthened. As they adopt a visitor-centered model and design visitor experiences, their priorities shift to engage audiences, convey digital collections, and tell stories through exhibitions. This is all part of crafting a dynamic and innovative museum identity of the future, made whole by seamless integration with digital culture, digital thinking, aesthetics, seeing and hearing, where visitors are welcomed participants. The international and interdisciplinary chapter contributors include digital artists, academics, and museum professionals. In themed parts the chapters present varied evidence-based research and case studies on museum theory, philosophy, collections, exhibitions, libraries, digital art and digital future, to bring new insights and perspectives, designed to inspire readers. Enjoy the journey! |
| data science meetup nyc: Как знакомиться с интересными людьми. Искусство и наука быть влиятельным Джон Леви, 2022-04-28 Какую бы цель вы перед собой ни поставили — расширение бизнеса, создание устойчивой корпоративной культуры, поддержка общественного движения или изменение собственных привычек, — вы не можете достичь ее в одиночку. Ваш успех (что бы это ни значило) определяют окружающие вас люди, и они могут изменить течение вашей жизни. У исследователя человеческого поведения Джона Леви не было ни денег, ни репутации, ни статуса, но он сумел пригласить к себе домой нобелевских лауреатов, победителей олимпиад, знаменитостей, глав компаний из списка Fortune 500, а однажды даже принцессу, и они не только давали ему советы, но и готовили ужин, мыли посуду, подметали пол, а затем благодарили его за прекрасно проведенное время. Эта книга познакомит вас с универсальной стратегией успеха в формировании значимых связей с теми, кто может повлиять на вас самих, вашу жизнь и на все, что вам дорого. «Подход, описанный здесь, эффективен для всех — для родителей, пытающихся помочь школьнику; для стеснительного сотрудника, старающегося заработать репутацию; для руководителя международной компании, желающего привлечь клиентов и повысить престиж бренда; для некоммерческих структур... Это приглашение открыть для себя, насколько хороша может стать жизнь, увидеть, что влияет на наши решения, что делает нас успешными, а самое главное — понять, кто станет вашим другом, потому что человек, с которым вы подружитесь, способен изменить вашу жизнь так, как вы даже не представляете. Это приглашение узнать, какое влияние вы можете иметь в самых важных для вас областях». (Джон Леви) |
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 …
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 …
