| data science meetups nyc: Data Scientists at Work Sebastian Gutierrez, 2014-12-12 Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. Data scientist is the sexiest job in the 21st century, according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report. Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (André Karpištšenko, Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). The book features a stimulating foreword by Google's Director of Research, Peter Norvig. Each of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. Data Scientists at Work parts the curtain on the interviewees’ earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients. |
| data science meetups nyc: R for Everyone Jared P. Lander, 2014 A guide to using and understanding the 'R' computer programming language. |
| data science meetups 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 meetups 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 meetups nyc: HBR Guide to Data Analytics Basics for Managers (HBR Guide Series) Harvard Business Review, 2018-03-13 Don't let a fear of numbers hold you back. Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. How do you get started? Whether you're working with data experts or running your own tests, you'll find answers in the HBR Guide to Data Analytics Basics for Managers. This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others. You'll learn how to: Identify the metrics you need to measure Run experiments and A/B tests Ask the right questions of your data experts Understand statistical terms and concepts Create effective charts and visualizations Avoid common mistakes |
| data science meetups nyc: Bad Data Handbook Q. Ethan McCallum, 2012-11-14 Mapping the world of data problems--Cover. |
| data science meetups nyc: Harvard Business Review Guides Ultimate Boxed Set (16 Books) Harvard Business Review, Nancy Duarte, Bryan A. Garner, Mary Shapiro, Jeff Weiss, 2019-02-26 How-to guides to your most pressing work challenges. This 16-volume, specially priced boxed set makes a perfect gift for aspiring leaders looking for trusted advice on such diverse topics as data analytics, negotiating, business writing, and coaching. This set includes: Persuasive Presentations Better Business Writing Finance Basics Data Analytics Building Your Business Case Making Every Meeting Matter Project Management Emotional Intelligence Getting the Right Work Done Negotiating Leading Teams Coaching Employees Performance Management Delivering Effective Feedback Dealing with Conflict Managing Up and Across Arm yourself with the advice you need to succeed on the job, with the most trusted brand in business. Packed with how-to essentials from leading experts, the HBR Guides provide smart answers to your most pressing work challenges. |
| data science meetups 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 80 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, and engineers; software and machine learning engineers; and system administrators. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on text, CSV, HTM, XML, and JSON files Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow Create reusable command-line tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines Model data with dimensionality reduction, clustering, regression, and classification algorithms |
| data science meetups nyc: Interactive Data Visualization for the Web Scott Murray, 2017-08-03 Author Scott Murray teaches you the fundamental concepts and methods of D3, a JavaScript library that lets you express data visually in a web browser. |
| data science meetups nyc: Data Science at the Command Line Jeroen Janssens, 2014-09-25 This hands-on 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—whether you’re on Windows, OS X, or Linux—author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools. Discover why the command line is an agile, scalable, and extensible technology. Even if you’re already comfortable processing data with, say, Python or R, you’ll greatly improve your data science workflow by also leveraging the power of the command line. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on plain text, CSV, HTML/XML, and JSON Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow using Drake Create reusable tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines using GNU Parallel Model data with dimensionality reduction, clustering, regression, and classification algorithms |
| data science meetups nyc: Creativity and Data Marketing Becky Wang, 2017-01-03 The world is moving towards universal connectivity at a dizzying rate; underpinning this complex system of incessant transaction, connection and digital experience is an infrastructure that generates a trail of data. This trail not only tells us about human behaviour, but provides vital insights into market dynamics, consumer behaviour, as well as the relationships we value and the culture we live in. Creativity and Data Marketing helps marketers access this data, find meaning in it and leverage it creatively to gain a competitive advantage. Creativity and Data Marketing addresses the need to analyse data creatively, and in particular how balancing tangible insights with creative market influence can maximise business innovation and results. The book clarifies where businesses can improve existing infrastructure, processes and activities, as well as finding new addressable markets ready to validate or rethink market demand. By identifying how and why a consumer interacts with touch points beyond paid media, for example forums, blog content, native advertising and word-of-mouth, Becky Wang presents a creativity and data blueprint on how businesses can make lucrative steps forward to innovate their products, services and communication strategies, laying the groundwork for long-term results. Online resources include bonus content covering analytics methods, evolving research, data platforms and more, and a creative brief template. |
| data science meetups 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 meetups nyc: Presentation Zen Garr Reynolds, 2009-04-15 FOREWORD BY GUY KAWASAKI Presentation designer and internationally acclaimed communications expert Garr Reynolds, creator of the most popular Web site on presentation design and delivery on the Net — presentationzen.com — shares his experience in a provocative mix of illumination, inspiration, education, and guidance that will change the way you think about making presentations with PowerPoint or Keynote. Presentation Zen challenges the conventional wisdom of making slide presentations in today’s world and encourages you to think differently and more creatively about the preparation, design, and delivery of your presentations. Garr shares lessons and perspectives that draw upon practical advice from the fields of communication and business. Combining solid principles of design with the tenets of Zen simplicity, this book will help you along the path to simpler, more effective presentations. |
| data science meetups 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 meetups nyc: Bowling Alone: Revised and Updated Robert D. Putnam, 2020-10-13 Updated to include a new chapter about the influence of social media and the Internet—the 20th anniversary edition of Bowling Alone remains a seminal work of social analysis, and its examination of what happened to our sense of community remains more relevant than ever in today’s fractured America. Twenty years, ago, Robert D. Putnam made a seemingly simple observation: once we bowled in leagues, usually after work; but no longer. This seemingly small phenomenon symbolized a significant social change that became the basis of the acclaimed bestseller, Bowling Alone, which The Washington Post called “a very important book” and Putnam, “the de Tocqueville of our generation.” Bowling Alone surveyed in detail Americans’ changing behavior over the decades, showing how we had become increasingly disconnected from family, friends, neighbors, and social structures, whether it’s with the PTA, church, clubs, political parties, or bowling leagues. In the revised edition of his classic work, Putnam shows how our shrinking access to the “social capital” that is the reward of communal activity and community sharing still poses a serious threat to our civic and personal health, and how these consequences have a new resonance for our divided country today. He includes critical new material on the pervasive influence of social media and the internet, which has introduced previously unthinkable opportunities for social connection—as well as unprecedented levels of alienation and isolation. At the time of its publication, Putnam’s then-groundbreaking work showed how social bonds are the most powerful predictor of life satisfaction, and how the loss of social capital is felt in critical ways, acting as a strong predictor of crime rates and other measures of neighborhood quality of life, and affecting our health in other ways. While the ways in which we connect, or become disconnected, have changed over the decades, his central argument remains as powerful and urgent as ever: mending our frayed social capital is key to preserving the very fabric of our society. |
| data science meetups nyc: Unix Power Tools Shelley Powers, 2003 With the growing popularity of Linux and the advent of Darwin, Unix has metamorphosed into something new and exciting. No longer perceived as a difficult operating system, more and more users are discovering the advantages of Unix for the first time. But whether you are a newcomer or a Unix power user, you'll find yourself thumbing through the goldmine of information in the new edition of Unix Power Tools to add to your store of knowledge. Want to try something new? Check this book first, and you're sure to find a tip or trick that will prevent you from learning things the hard way. The latest edition of this best-selling favorite is loaded with advice about almost every aspect of Unix, covering all the new technologies that users need to know. In addition to vital information on Linux, Darwin, and BSD, Unix Power Tools 3rd Edition now offers more coverage of bash, zsh, and other new shells, along with discussions about modern utilities and applications. Several sections focus on security and Internet access. And there is a new chapter on access to Unix from Windows, addressing the heterogeneous nature of systems today. You'll also find expanded coverage of software installation and packaging, as well as basic information on Perl and Python. Unix Power Tools 3rd Edition is a browser's book...like a magazine that you don't read from start to finish, but leaf through repeatedly until you realize that you've read it all. Bursting with cross-references, interesting sidebars explore syntax or point out other directions for exploration, including relevant technical details that might not be immediately apparent. The book includes articles abstracted from other O'Reilly books, new information that highlights program tricks and gotchas, tips posted to the Net over the years, and other accumulated wisdom. Affectionately referred to by readers as the Unix book, UNIX Power Tools provides access to information every Unix user is going to need to know. It will help you think creatively about UNIX, and will help you get to the point where you can analyze your own problems. Your own solutions won't be far behind. |
| data science meetups 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 meetups 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 meetups nyc: Machine Learning and Data Science Blueprints for Finance Hariom Tatsat, Sahil Puri, Brad Lookabaugh, 2020-10-01 Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations |
| data science meetups 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 meetups nyc: Hands-On Machine Learning with Azure Thomas K Abraham, Parashar Shah, Jen Stirrup, Lauri Lehman, Anindita Basak, 2018-10-31 Implement machine learning, cognitive services, and artificial intelligence solutions by leveraging Azure cloud technologies Key FeaturesLearn advanced concepts in Azure ML and the Cortana Intelligence Suite architectureExplore ML Server using SQL Server and HDInsight capabilitiesImplement various tools in Azure to build and deploy machine learning modelsBook Description Implementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage. However, Azure has created ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML projects in the cloud in a cost-effective way. The book begins by covering the benefits of ML and AI in the cloud. You will then explore Microsoft’s Team Data Science Process to establish a repeatable process for successful AI development and implementation. You will also gain an understanding of AI technologies available in Azure and the Cognitive Services APIs to integrate them into bot applications. This book lets you explore prebuilt templates with Azure Machine Learning Studio and build a model using canned algorithms that can be deployed as web services. The book then takes you through a preconfigured series of virtual machines in Azure targeted at AI development scenarios. You will get to grips with the ML Server and its capabilities in SQL and HDInsight. In the concluding chapters, you’ll integrate patterns with other non-AI services in Azure. By the end of this book, you will be fully equipped to implement smart cognitive actions in your models. What you will learnDiscover the benefits of leveraging the cloud for ML and AIUse Cognitive Services APIs to build intelligent botsBuild a model using canned algorithms from Microsoft and deploy it as a web serviceDeploy virtual machines in AI development scenariosApply R, Python, SQL Server, and Spark in AzureBuild and deploy deep learning solutions with CNTK, MMLSpark, and TensorFlowImplement model retraining in IoT, Streaming, and Blockchain solutionsExplore best practices for integrating ML and AI functions with ADLA and logic appsWho this book is for If you are a data scientist or developer familiar with Azure ML and cognitive services and want to create smart models and make sense of data in the cloud, this book is for you. You’ll also find this book useful if you want to bring powerful machine learning services into your cloud applications. Some experience with data manipulation and processing, using languages like SQL, Python, and R, will aid in understanding the concepts covered in this book |
| data science meetups 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 meetups nyc: Phishing Detection Using Content-Based Image Classification Shekhar Khandelwal, Rik Das, 2022-06-01 Phishing Detection Using Content-Based Image Classification is an invaluable resource for any deep learning and cybersecurity professional and scholar trying to solve various cybersecurity tasks using new age technologies like Deep Learning and Computer Vision. With various rule-based phishing detection techniques at play which can be bypassed by phishers, this book provides a step-by-step approach to solve this problem using Computer Vision and Deep Learning techniques with significant accuracy. The book offers comprehensive coverage of the most essential topics, including: Programmatically reading and manipulating image data Extracting relevant features from images Building statistical models using image features Using state-of-the-art Deep Learning models for feature extraction Build a robust phishing detection tool even with less data Dimensionality reduction techniques Class imbalance treatment Feature Fusion techniques Building performance metrics for multi-class classification task Another unique aspect of this book is it comes with a completely reproducible code base developed by the author and shared via python notebooks for quick launch and running capabilities. They can be leveraged for further enhancing the provided models using new advancement in the field of computer vision and more advanced algorithms. |
| data science meetups 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 meetups nyc: Thinking with Data Max Shron, 2014-01-20 Many analysts are too concerned with tools and techniques for cleansing, modeling, and visualizing datasets and not concerned enough with asking the right questions. In this practical guide, data strategy consultant Max Shron shows you how to put the why before the how, through an often-overlooked set of analytical skills. Thinking with Data helps you learn techniques for turning data into knowledge you can use. You’ll learn a framework for defining your project, including the data you want to collect, and how you intend to approach, organize, and analyze the results. You’ll also learn patterns of reasoning that will help you unveil the real problem that needs to be solved. Learn a framework for scoping data projects Understand how to pin down the details of an idea, receive feedback, and begin prototyping Use the tools of arguments to ask good questions, build projects in stages, and communicate results Explore data-specific patterns of reasoning and learn how to build more useful arguments Delve into causal reasoning and learn how it permeates data work Put everything together, using extended examples to see the method of full problem thinking in action |
| data science meetups nyc: Mastering Java for Data Science Alexey Grigorev, 2017-04-27 Use Java to create a diverse range of Data Science applications and bring Data Science into production About This Book An overview of modern Data Science and Machine Learning libraries available in Java Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks. Easy-to-follow illustrations and the running example of building a search engine. Who This Book Is For This book is intended for software engineers who are comfortable with developing Java applications and are familiar with the basic concepts of data science. Additionally, it will also be useful for data scientists who do not yet know Java but want or need to learn it. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing the existing stack, this book is for you! What You Will Learn Get a solid understanding of the data processing toolbox available in Java Explore the data science ecosystem available in Java Find out how to approach different machine learning problems with Java Process unstructured information such as natural language text or images Create your own search engine Get state-of-the-art performance with XGBoost Learn how to build deep neural networks with DeepLearning4j Build applications that scale and process large amounts of data Deploy data science models to production and evaluate their performance In Detail Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings. Style and approach This is a practical guide where all the important concepts such as classification, regression, and dimensionality reduction are explained with the help of examples. |
| data science meetups nyc: Python for Finance Yves J. Hilpisch, 2018-12-05 The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks. |
| data science meetups nyc: Continuous Discovery Habits Teresa Torres, 2021-05-19 If you haven't had the good fortune to be coached by a strong leader or product coach, this book can help fill that gap and set you on the path to success. - Marty Cagan How do you know that you are making a product or service that your customers want? How do you ensure that you are improving it over time? How do you guarantee that your team is creating value for your customers in a way that creates value for your business? In this book, you'll learn a structured and sustainable approach to continuous discovery that will help you answer each of these questions, giving you the confidence to act while also preparing you to be wrong. You'll learn to balance action with doubt so that you can get started without being blindsided by what you don't get right. If you want to discover products that customers love-that also deliver business results-this book is for you. |
| data science meetups nyc: Practical DataOps Harvinder Atwal, 2019-12-09 Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles. This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will LearnDevelop a data strategy for your organization to help it reach its long-term goals Recognize and eliminate barriers to delivering data to users at scale Work on the right things for the right stakeholders through agile collaboration Create trust in data via rigorous testing and effective data management Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes Create cross-functional self-organizing teams focused on goals not reporting lines Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products Who This Book Is For Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production. |
| data science meetups nyc: Artificial Intelligence in Finance Yves Hilpisch, 2020-10-14 The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI) Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practice Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets Identify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategies Understand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about |
| data science meetups nyc: Financial Theory with Python Yves Hilpisch, 2021-09-23 Nowadays, finance, mathematics, and programming are intrinsically linked. This book provides the relevant foundations of each discipline to give you the major tools you need to get started in the world of computational finance. Using an approach where mathematical concepts provide the common background against which financial ideas and programming techniques are learned, this practical guide teaches you the basics of financial economics. Written by the best-selling author of Python for Finance, Yves Hilpisch, Financial Theory with Python explains financial, mathematical, and Python programming concepts in an integrative manner so that the interdisciplinary concepts reinforce each other. Draw upon mathematics to learn the foundations of financial theory and Python programming Learn about financial theory, financial data modeling, and the use of Python for computational finance Leverage simple economic models to better understand basic notions of finance and Python programming concepts Use both static and dynamic financial modeling to address fundamental problems in finance, such as pricing, decision-making, equilibrium, and asset allocation Learn the basics of Python packages useful for financial modeling, such as NumPy, pandas, Matplotlib, and SymPy |
| data science meetups nyc: Reinforcement Learning for Finance Yves J. Hilpisch, 2024-10-14 Reinforcement learning (RL) has led to several breakthroughs in AI. The use of the Q-learning (DQL) algorithm alone has helped people develop agents that play arcade games and board games at a superhuman level. More recently, RL, DQL, and similar methods have gained popularity in publications related to financial research. This book is among the first to explore the use of reinforcement learning methods in finance. Author Yves Hilpisch, founder and CEO of The Python Quants, provides the background you need in concise fashion. ML practitioners, financial traders, portfolio managers, strategists, and analysts will focus on the implementation of these algorithms in the form of self-contained Python code and the application to important financial problems. This book covers: Reinforcement learning Deep Q-learning Python implementations of these algorithms How to apply the algorithms to financial problems such as algorithmic trading, dynamic hedging, and dynamic asset allocation This book is the ideal reference on this topic. You'll read it once, change the examples according to your needs or ideas, and refer to it whenever you work with RL for finance. Dr. Yves Hilpisch is founder and CEO of The Python Quants, a group that focuses on the use of open source technologies for financial data science, AI, asset management, algorithmic trading, and computational finance. |
| data science meetups nyc: The Data Science Handbook Carl Shan, Henry Wang, William Chen, Max Song, 2015-05-03 The Data Science Handbook is a curated collection of 25 candid, honest and insightful interviews conducted with some of the world's top data scientists.In this book, you'll hear how the co-creator of the term 'data scientist' thinks about career and personal success. You'll hear from a young woman who created her own data scientist curriculum, subsequently landing her a role in the field. Readers of this book will be left with war stories, wisdom and |
| data science meetups 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 meetups nyc: Numsense! Data Science for the Layman Annalyn Ng, 2017-03-24 Used in Stanford's CS102 Big Data (Spring 2017) course. Want to get started on data science? Our promise: no math added. This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations, as well as lots of visuals, all of which are colorblind-friendly. Popular concepts covered include: A/B Testing Anomaly Detection Association Rules Clustering Decision Trees and Random Forests Regression Analysis Social Network Analysis Neural Networks Features: Intuitive explanations and visuals Real-world applications to illustrate each algorithm Point summaries at the end of each chapter Reference sheets comparing the pros and cons of algorithms Glossary list of commonly-used terms With this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions. |
| data science meetups 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 meetups nyc: Researching Digital Life James Ash, Rob Kitchin, Agnieszka Leszczynski, 2024-03-15 We now live in a world where all aspects of everyday life are thoroughly mediated by digital technologies. Making sense of digital life is accordingly an essential undertaking for social science and humanities scholars. This multidisciplinary book provides an essential guide to researching digital life: Orienting readers with respect to methodologies, research design, and research ethics. Detailing key research methods, including interviews, surveys, ethnographies, walking methodologies, arts-based and participatory approaches, historical analysis, data visualisation, mapping and data analytics. Demonstrating these methods in action in real-world studies that have investigated apps and interfaces, social and locative media, mobilities, smart cities, and digital labour and work. The authors provide: • Non-Eurocentric perspectives and case studies from diverse disciplines • Annotated further reading to help you situate your research alongside existing research in your field • An outline of future directions for researching digital life. Accessible in style and richly illustrated, the chapters provide a wealth of key insights and practical information to ensure research projects are successfully planned and implemented. |
| data science meetups nyc: Predictive Analytics for Human Resources Jac Fitz-enz, John Mattox, II, 2014-07-28 Create and run a human resource analytics project with confidence For any human resource professional that wants to harness the power of analytics, this essential resource answers the questions: Where do I start? and What tools are available? Predictive Analytics for Human Resources is designed to answer these and other vital questions. The book explains the basics of every business—the vision, the brand, and the culture, and shows how predictive analytics supports them. The authors put the focus on the fundamentals of predictability and include a framework of logical questions to help set up an analytic program or project, then follow up by offering a clear explanation of statistical applications. Predictive Analytics for Human Resources is a how-to guide filled with practical and targeted advice. The book starts with the basic idea of engaging in predictive analytics and walks through case simulations showing statistical examples. In addition, this important resource addresses the topics of internal coaching, mentoring, and sponsoring and includes information on how to recruit a sponsor. In the book, you'll find: A comprehensive guide to developing and implementing a human resource analytics project Illustrative examples that show how to go to market, develop a leadership model, and link it to financial targets through causal modeling Explanations of the ten steps required in building an analytics function How to add value through analysis of systems such as staffing, training, and retention For anyone who wants to launch an analytics project or program for HR, this complete guide provides the information and instruction to get started the right way. |
| data science meetups nyc: Data Analysis with Python David Taieb, 2018-12-31 Learn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis. Key FeaturesBridge your data analysis with the power of programming, complex algorithms, and AIUse Python and its extensive libraries to power your way to new levels of data insightWork with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time seriesExplore this modern approach across with key industry case studies and hands-on projectsBook Description Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence. What you will learnA new toolset that has been carefully crafted to meet for your data analysis challengesFull and detailed case studies of the toolset across several of today’s key industry contextsBecome super productive with a new toolset across Python and Jupyter NotebookLook into the future of data science and which directions to develop your skills nextWho this book is for This book is for developers wanting to bridge the gap between them and data scientists. Introducing PixieDust from its creator, the book is a great desk companion for the accomplished Data Scientist. Some fluency in data interpretation and visualization is assumed. It will be helpful to have some knowledge of Python, using Python libraries, and some proficiency in web development. |
| data science meetups nyc: Klara and the Sun Kazuo Ishiguro, 2021-03-02 NEW YORK TIMES BESTSELLER • Once in a great while, a book comes along that changes our view of the world. This magnificent novel from the Nobel laureate and author of Never Let Me Go is “an intriguing take on how artificial intelligence might play a role in our futures ... a poignant meditation on love and loneliness” (The Associated Press). • A GOOD MORNING AMERICA Book Club Pick! Here is the story of Klara, an Artificial Friend with outstanding observational qualities, who, from her place in the store, watches carefully the behavior of those who come in to browse, and of those who pass on the street outside. She remains hopeful that a customer will soon choose her. Klara and the Sun is a thrilling book that offers a look at our changing world through the eyes of an unforgettable narrator, and one that explores the fundamental question: what does it mean to love? |
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 …
Exam No. 9058 - Page 3 - NYC.gov
0110.0005 Plans, manages, and/or supervises the activities of staff collecting data related to organizationalresearch. 0110.0006 Plans, manages, and/or supervises the activities of staff …
THE 11TH ANNUAL BIG DATA & ANALYTICS HEALTHCARE …
increasingly data-driven healthcare environment, it is essential to prioritize patient privacy, data security, and informed consent. Take away specific solutions to: Optimize national health data …
Introduction to Data Science - Guide to Intelligent Data …
What is Data Science? 7 [Wikipedia quoting Dhar 13, Leek 13] Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and …
INDIANA UNIVERSITY DATA SCIENCE - Luddy School of …
Sep 11, 2023 · As a Master of Data Science student, you have the option of focusing on one of the following four distinct tracks: (1) Applied Data Science; (2) Big Data Systems; (3) …
Copyright © European Data Science Academy All rights …
Our summer series of Machine Learning for Data Science Bootcamps got off to a flying start this week at the Open Data Institute in London. 10 learners took part in this first of four summer …
HeilerGeorg CV en extended - csh.ac.at
Innovation, data science & automation SPECIALS HARD SKILLS WORK EXPERIENCE ALLIANZ EDUCATION Meetups - I am a speaker at several data science meetups in Vienna Startup - …
'$0$ '0%2. &KDS %LJ 'DWD 'DWD 6FLHQFH - DAMA NY
2yhuylhz 2i %lj 'dwd 'dwd 6flhqfh ,qirupdwlrq lv qrw gdwd 'dwd 6flhqfh lv doprvw wkh lqyhuvh ri %xvlqhvv ,qwhooljhqfh % , %lj 'dwd 6flhqfh kdv
Masters In Data Science And Business Analytics Full PDF
A Master's degree in Data Science & Business Analytics equips you with the knowledge and skills to become a sought-after expert in this high-demand field. What Can You Expect to Learn? A …
Reference Tables for Physical Setting/EARTH SCIENCE - New …
Physical Setting/Earth Science. The University of the State of New York • THE STATE EDUCATION DEPARTMENT • Albany, New York 12234 • www.nysed.gov Reference Tables …
Introduction to Data Science A Beginner's Guide
©DatabaseTown.com • Bionomial Data ( Variable data with only two options e.g. good or bad, true or false ) • Nominal or Unordered Data (Variable data which is in unordered form e.g. red, …
A BeginnerÕs Guide to )GVVKPI;QWT(KTUV &CVC5EKGPEG,QD
www.springboard.com GETTING YOUR FIRST DATA SCIENCE JOB 2 Table of contents FOREWORD ..... 1 INTRODUCTION .....
Mayor BUREAU OF EXAMINATIONS NOTICE OF …
Examples of acceptable course work are: data telecommunications, computer science, electronics, and electrical engineering. Examples of acceptable experience in data …
School Construction Authority - New York City Council
enhancements (internet connectivity projects, telephone upgrades and data and software systems), Finance Division Briefing Paper School Construction Authority 5 . and Mayoral and …
2024 Update on New York City Staffing Trends - Office of the …
using data from August 2022, when the City’s full-time workforce totaled 281,333 employees, a decline of 6.4 percent from a year-end record of 300,446 employees in FY 2020. Elevated …
DeepPhish Simulating Malicious AI - Black Hat Briefings
Agenda •Phishing URL Detection using Machine Learning •Malicious Cert Detection using Deep Learning •DeepPhish: Simulating Malicious AI •Demo
VISION ZERO YEAR FOUR REPORT - NYC.gov
Columbia University Data Science Institute to gain deeper insights into what happens on the streets and why. Engineering / Year Four Accelerated Interventions Where They Are Needed …
Applied Data Analytics - api.mdx.ac.ae
consulting, corporate training in data science, and knowledge sharing sessions. The following are the key offerings of the InsightsX Lab: Datathons (Data Hackathons), Use Case Nights …
OLIVIER JEUNEN
June ’19 Neural Networks and Causal Recommendation Data Science Summer School, Ecole Polytechnique, FR´ ’17–’21 Research Thesis Supervisor and Jury Member M.Sc. Computer …
10th Annual New York City Epidemiology Forum - NYU …
9:00-9:30 Registration and Coffee 9:30-9:40 Welcome and Introduction Lorna Thorpe, PhD 9:40-10:30 Keynote: “Learning to Sweat the Big Stuff in Epidemiology and Public Health” Jay …
Is data science worth it quora - uploads.strikinglycdn.com
Data ScienceSo many tools to worry about, so few you’ll actually useData science can cover virtually any quantitative work. Two data scientists at different companies, or even within the …
Mayor BUREAU OF EXAMINATIONS NOTICE OF …
eric l. adams mayor department of citywide administrative services bureau of examinations dawn m. pinnock commissioner notice of examination associate education analyst
A Simple Introduction To Data Science Full PDF
A Simple Introduction To Data Science Foundations of Data Science Department of Computer 4 4 Convergence of Random Walks on Undirected Graphs 88 4 4 1 Using ...
The Complete Collection of Data Science Cheat Sheets
learning and data science. Abid holds a master’s degree in Technology Management and a bachelor’s degree in Telecommunication Engineering. His vision is to build an AI product using …
Mayor BUREAU OF EXAMINATIONS NOTICE OF …
semester credits in computer science or a related computer field and one year of satisfactory, full-time computer software experience in computer systems development and analysis, …
USTR Supply Chain Resilience Inquiry: Table of Exhibits
that guarantee the ability to move data across international borders, prohibit forced localization of data or restrictions based on nationality of ownership, and protect source codes, among other …
STRATEGIC PLAN 2022 - NYC.gov
Power of Data 20 Tech Innovation for All 26 Enhance Technology Resiliency 32 Roadmap 41 OTI Strategic Plan 2022 1 Contents. 2 OTI Strategic Plan 2022. ... point when NYC is starting to …
A Simple Introduction To Data Science (Download Only)
A Simple Introduction To Data Science Data Science: A Simple for the Curious Mind Ever wondered how Netflix recommends movies you'll love, how Amazon knows what you ...
Data Scientists der nächsten Generation - uni.li
„Data Science“ im Rahmen des Vier -Ländereck Data Science Meetups . 18 Studierende aus der Vertiefungsrichtung „Data Science“ arbeiteten ein Semester lang eng mit den Unternehmen …
NYC Department of Environmental Protection: Climate …
* New York State P-12 Science Learning Standards ** Amplify K-8 Science Curriculum NYC Department of Environmental Protection: Climate Change Module Standards Connections New …
FIRE DEPARTMENT CITY OF NEW YORK - NYC.gov
and the Science Safety Manual to address key responsibilitiesof teachers in School Science Laboratories. Persons performing the duties for supervising the operation of a chemical …
Post Graduate Diploma in Data Science - International …
Data Science as one of the sectors with critical supply demand imbalance. Our vision is to design and deliver a quality online Post Graduate Diploma in Data Science to drive the growth of the …
ATS CHEAT SHEET FOR CHARTERS - New York City Charter …
ATS CHEAT SHEET FOR CHARTERS S Access Step 1: Principal/Ops Leader adds staff member to TO in order to provision a DOE email (@schools.nyc.gov) Step 2: Once you receive your …
Carlos Fernandez-Granda - Courant Institute of Mathematical …
These notes were developed for the course Probability and Statistics for Data Science at the Center for Data Science in NYU. The goal is to provide an overview of fundamental concepts in …
NYCHA
Welcome to the 2025 Edition of the NYCHA Development Data Book. The Performance Tracking and Analytics Department (PTAD) has made various changes to the book this year to enhance …
NOTICEOFEXAMINATION - NYC.gov
evaluation,workforceplanning,recruitment,positionclassification,personnelmanagement,training,labor andmanagementrelations,andemployeebenefits. 18 ...
SYSTEMIC CHILD FATALITY REVIEW 2021 ANNUAL REPORT
In 2018, ACS adopted a safety science approach2 to reviewing fatalities, based on innovations in aviation, health care and other industries to improve safety. The safety science approach …
NEW YORK CITY TRANSIT KEY PERFORMANCE METRICS - MTA
Data Review The subway satisfaction rate for June is 59%, down two percentage points from May. Overall line satisfaction is down one point to 61%, and overall station satisfaction is flat at …
Automating Complex Data Analysis: A Disruptive Business …
Python/NumPy/SciPy at data science meetups and conferences. Nonetheless a search of all job advertisements in the United States posted on the popular LinkedIn Job site on May 31, 2017 …
DATA SCIENCE - Penerbit Buku Widina
Sebelum kita mempelajari ilmu statistik dalam penerapan data science, kita akan mengenal apa itu statistik, statistika dan data science. Seperti yang terlihat pada gambar 1. Data science …
Suicide by Quarter in New York City, 2015 to 2021 - NYC.gov
Call 888-NYC-WELL (888-692-9355), text “WELL” to 65173 or chat at . nyc.gov/nycwell. • If you or someone you know is thinking about suicide and lives outside of NYC, call or text . 988, or chat …
LL34 Compliance - NYC.gov
The LL34 Compliance tab allows you to complete all Doing Business Data information within PASSPort. Local Law 34 of 2007 (LL 34) is New York City’s campaign finance law that limits
DEPARTMENT OF INFORMATION TECHNOLOGY
Goal 2a Increase the public’s use of City government information through NYC.gov. Performance Indicators Actual Target 4-Month Actual FY19 FY20 FY21 FY22 FY23 FY21 FY22 NYC.gov …
Data science : fondamentaux et études de cas - fnac-static.com
Data science : fondamentaux et études de cas Data science : fondamentaux et études de cas Data science : Éric Biernat Michel Lutz Préface de Yann LeCun, Directeur de Facebook …
Machine Learning with PySpark - content.e-bookshelf.de
the last eight plus years working on multiple Data projects. He has used Machine Learning and Deep Learning techniques in numerous client projects using R, Python, Spark, and …
DATA SCIENCE
Therefore, CBSE is introducing ‘Data Science’ as a skill module of 12 hours duration in class VIII and as a skill subject in classes IX-XII. CBSE acknowledges the initiative by Microsoft India in …
Table of Contents - riskcue.id
What is Data Science? Different Roles within Data Science How Different Companies Think About Data Science 1 Earlystage startups (200 employees or fewer) looking to build a data product 2 …
ACTUARIAL SPECIALIST LEVEL I - NYC.gov
or supplemented by at least 24 semester credits in actuarial science, mathematics or statistics. Mathematics or Actuarial Sciences Degree is preferred. Computer proficiency with spreadsheet …
NEW YORK CITY DRINKING WATER SUPPLY AND QUALITY …
federal monitoring requirements. These data are summarized in tables starting on page 11. Additionally, DEP performed more than 200,600 analyses on 11,600 samples from the upstate …
AUDIENCE INPUTSON PUERTO RICO S AGENDAFORA …
•The US Federal Census is the only piece of data we must measure retail sales on a quarterly basis. •Technological-digital productivity is not measured. Efforts were made in the past by …
Epi Data Brief - NYC.gov
Visit EpiQuery – the Health Department’s online, interactive health data system at . nyc.gov/health/EpiQuery . Epi Data Brief, No. 57 Page 3 . ... Services, Healthy People 2020 …
