| data science bootcamps reddit: Powerful Python Aaron Maxwell, 2024-11-08 Once you've mastered the basics of Python, how do you skill up to the top 1%? How do you focus your learning time on topics that yield the most benefit for production engineering and data teams—without getting distracted by info of little real-world use? This book answers these questions and more. Based on author Aaron Maxwell's software engineering career in Silicon Valley, this unique book focuses on the Python first principles that act to accelerate everything else: the 5% of programming knowledge that makes the remaining 95% fall like dominos. It's also this knowledge that helps you become an exceptional Python programmer, fast. Learn how to think like a Pythonista: explore advanced Pythonic thinking Create lists, dicts, and other data structures using a high-level, readable, and maintainable syntax Explore higher-order function abstractions that form the basis of Python libraries Examine Python's metaprogramming tool for priceless patterns of code reuse Master Python's error model and learn how to leverage it in your own code Learn the more potent and advanced tools of Python's object system Take a deep dive into Python's automated testing and TDD Learn how Python logging helps you troubleshoot and debug more quickly |
| data science bootcamps reddit: Pandas for Everyone Daniel Y. Chen, 2017-12-15 The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning |
| data science bootcamps reddit: Statistical Thinking from Scratch M. D. Edge, 2019 Focuses on detailed instruction in a single statistical technique, simple linear regression (SLR), with the goal of gaining tools, understanding, and intuition that can be applied to other contexts. |
| data science bootcamps reddit: Introduction to Machine Learning with Python Andreas C. Müller, Sarah Guido, 2016-09-26 Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills |
| data science bootcamps reddit: Storytelling with Data Cole Nussbaumer Knaflic, 2015-10-09 Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it! |
| data science bootcamps reddit: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
| data science bootcamps reddit: Data Science from Scratch Joel Grus, 2015-04-14 Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases |
| data science bootcamps reddit: Breakup Bootcamp Amy Chan, 2020-12-01 “A relationship expert whose work is like that of a scientific Carrie Bradshaw.” —THE OBSERVER A self-affirming, holistic guide for everyone—single or married, divorced or dating—to transforming heartbreak into healing by the founder of the innovative and revolutionary Renew Breakup Bootcamp Amy Chan hit rock bottom when she discovered that her boyfriend cheated on her. Although she was angry and broken-hearted, Chan soon came to realize that the breakup was the shakeup she needed to redirect her life. Instead of descending into darkness, she used the pain of the breakup as a bridge to self-actualization. She devoted herself to learning various healing modalities from the ancient to the scientific, and dived into the psychology of love. It worked. Fast forward years later, Amy completely transformed her life, her relationships and founded a breakup bootcamp helping countless women heal their hearts. In Breakup Bootcamp, Amy Chan directs her experience as a relationship columnist and as the creator of Renew Breakup Bootcamp into a practical, thoughtful guide to turning broken hearts into an opportunity to break out of complacency and destructive habits. Dubbed the Chief Heart Hacker, Amy Chan grounds her practical advice and tried and tested methods rooted in cutting-edge psychology and research, helping first her bootcamp attendees and now her readers most effectively heal and reclaim their self-love. Breakup Bootcamp comes at the perfect time, when many are feeling the intensity of being in or out of a relationship, lonely or suffocated, and flirting with old toxic relationships they’ve outgrown. Relatable, life-changing, and backed by sound scientific research, Breakup Bootcamp can help anyone turn their greatest heartbreak into a powerful tool for growth. |
| data science bootcamps reddit: Practical SQL, 2nd Edition Anthony DeBarros, 2022-01-25 Analyze data like a pro, even if you’re a beginner. Practical SQL is an approachable and fast-paced guide to SQL (Structured Query Language), the standard programming language for defining, organizing, and exploring data in relational databases. Anthony DeBarros, a journalist and data analyst, focuses on using SQL to find the story within your data. The examples and code use the open-source database PostgreSQL and its companion pgAdmin interface, and the concepts you learn will apply to most database management systems, including MySQL, Oracle, SQLite, and others.* You’ll first cover the fundamentals of databases and the SQL language, then build skills by analyzing data from real-world datasets such as US Census demographics, New York City taxi rides, and earthquakes from US Geological Survey. Each chapter includes exercises and examples that teach even those who have never programmed before all the tools necessary to build powerful databases and access information quickly and efficiently. You’ll learn how to: Create databases and related tables using your own data Aggregate, sort, and filter data to find patterns Use functions for basic math and advanced statistical operations Identify errors in data and clean them up Analyze spatial data with a geographic information system (PostGIS) Create advanced queries and automate tasks This updated second edition has been thoroughly revised to reflect the latest in SQL features, including additional advanced query techniques for wrangling data. This edition also has two new chapters: an expanded set of instructions on for setting up your system plus a chapter on using PostgreSQL with the popular JSON data interchange format. Learning SQL doesn’t have to be dry and complicated. Practical SQL delivers clear examples with an easy-to-follow approach to teach you the tools you need to build and manage your own databases. * Microsoft SQL Server employs a variant of the language called T-SQL, which is not covered by Practical SQL. |
| data science bootcamps reddit: A Curious Moon Rob Conery, 2020-12-13 Starting an application is simple enough, whether you use migrations, a model-synchronizer or good old-fashioned hand-rolled SQL. A year from now, however, when your app has grown and you're trying to measure what's happened... the story can quickly change when data is overwhelming you and you need to make sense of what's been accumulating. Learning how PostgreSQL works is just one aspect of working with data. PostgreSQL is there to enable, enhance and extend what you do as a developer/DBA. And just like any tool in your toolbox, it can help you create crap, slice off some fingers, or help you be the superstar that you are.That's the perspective of A Curious Moon - data is the truth, data is your friend, data is your business. The tools you use (namely PostgreSQL) are simply there to safeguard your treasure and help you understand what it's telling you.But what does it mean to be data-minded? How do you even get started? These are good questions and ones I struggled with when outlining this book. I quickly realized that the only way you could truly understand the power and necessity of solid databsae design was to live the life of a new DBA... thrown into the fire like we all were at some point...Meet Dee Yan, our fictional intern at Red:4 Aerospace. She's just been handed the keys to a massive set of data, straight from Saturn, and she has to load it up, evaluate it and then analyze it for a critical project. She knows that PostgreSQL exists... but that's about it.Much more than a tutorial, this book has a narrative element to it a bit like The Martian, where you get to know Dee and the problems she faces as a new developer/DBA... and how she solves them.The truth is in the data... |
| data science bootcamps reddit: Basic Training in Mathematics R. Shankar, 2013-12-20 Based on course material used by the author at Yale University, this practical text addresses the widening gap found between the mathematics required for upper-level courses in the physical sciences and the knowledge of incoming students. This superb book offers students an excellent opportunity to strengthen their mathematical skills by solving various problems in differential calculus. By covering material in its simplest form, students can look forward to a smooth entry into any course in the physical sciences. |
| data science bootcamps reddit: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| data science bootcamps reddit: Multivariable Calculus James Stewart, 2011-09-27 Success in your calculus course starts here! James Stewart's CALCULUS, 7e, International Metric texts are world-wide best-sellers for a reason: they are clear, accurate, and filled with relevant, real-world examples. With MULTIVARIABLE CALCULUS, 7e, International Metric Edition Stewart conveys not only the utility of calculus to help you develop technical competence, but also gives you an appreciation for the intrinsic beauty of the subject. His patient examples and built-in learning aids will help you build your mathematical confidence and achieve your goals in the course! |
| data science bootcamps reddit: Ask a Manager Alison Green, 2018-05-01 From the creator of the popular website Ask a Manager and New York’s work-advice columnist comes a witty, practical guide to 200 difficult professional conversations—featuring all-new advice! There’s a reason Alison Green has been called “the Dear Abby of the work world.” Ten years as a workplace-advice columnist have taught her that people avoid awkward conversations in the office because they simply don’t know what to say. Thankfully, Green does—and in this incredibly helpful book, she tackles the tough discussions you may need to have during your career. You’ll learn what to say when • coworkers push their work on you—then take credit for it • you accidentally trash-talk someone in an email then hit “reply all” • you’re being micromanaged—or not being managed at all • you catch a colleague in a lie • your boss seems unhappy with your work • your cubemate’s loud speakerphone is making you homicidal • you got drunk at the holiday party Praise for Ask a Manager “A must-read for anyone who works . . . [Alison Green’s] advice boils down to the idea that you should be professional (even when others are not) and that communicating in a straightforward manner with candor and kindness will get you far, no matter where you work.”—Booklist (starred review) “The author’s friendly, warm, no-nonsense writing is a pleasure to read, and her advice can be widely applied to relationships in all areas of readers’ lives. Ideal for anyone new to the job market or new to management, or anyone hoping to improve their work experience.”—Library Journal (starred review) “I am a huge fan of Alison Green’s Ask a Manager column. This book is even better. It teaches us how to deal with many of the most vexing big and little problems in our workplaces—and to do so with grace, confidence, and a sense of humor.”—Robert Sutton, Stanford professor and author of The No Asshole Rule and The Asshole Survival Guide “Ask a Manager is the ultimate playbook for navigating the traditional workforce in a diplomatic but firm way.”—Erin Lowry, author of Broke Millennial: Stop Scraping By and Get Your Financial Life Together |
| data science bootcamps reddit: Building a Career in Software Daniel Heller, 2020-09-27 Software engineering education has a problem: universities and bootcamps teach aspiring engineers to write code, but they leave graduates to teach themselves the countless supporting tools required to thrive in real software companies. Building a Career in Software is the solution, a comprehensive guide to the essential skills that instructors don't need and professionals never think to teach: landing jobs, choosing teams and projects, asking good questions, running meetings, going on-call, debugging production problems, technical writing, making the most of a mentor, and much more. In over a decade building software at companies such as Apple and Uber, Daniel Heller has mentored and managed tens of engineers from a variety of training backgrounds, and those engineers inspired this book with their hundreds of questions about career issues and day-to-day problems. Designed for either random access or cover-to-cover reading, it offers concise treatments of virtually every non-technical challenge you will face in the first five years of your career—as well as a selection of industry-focused technical topics rarely covered in training. Whatever your education or technical specialty, Building a Career in Software can save you years of trial and error and help you succeed as a real-world software professional. What You Will Learn Discover every important nontechnical facet of professional programming as well as several key technical practices essential to the transition from student to professional Build relationships with your employer Improve your communication, including technical writing, asking good questions, and public speaking Who This Book is For Software engineers either early in their careers or about to transition to the professional world; that is, all graduates of computer science or software engineering university programs and all software engineering boot camp participants. |
| data science bootcamps reddit: Grokking Algorithms Aditya Bhargava, 2016-05-12 This book does the impossible: it makes math fun and easy! - Sander Rossel, COAS Software Systems Grokking Algorithms is a fully illustrated, friendly guide that teaches you how to apply common algorithms to the practical problems you face every day as a programmer. You'll start with sorting and searching and, as you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python. Learning about algorithms doesn't have to be boring! Get a sneak peek at the fun, illustrated, and friendly examples you'll find in Grokking Algorithms on Manning Publications' YouTube channel. Continue your journey into the world of algorithms with Algorithms in Motion, a practical, hands-on video course available exclusively at Manning.com (www.manning.com/livevideo/algorithms-?in-motion). Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology An algorithm is nothing more than a step-by-step procedure for solving a problem. The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to understand them but refuse to slog through dense multipage proofs, this is the book for you. This fully illustrated and engaging guide makes it easy to learn how to use the most important algorithms effectively in your own programs. About the Book Grokking Algorithms is a friendly take on this core computer science topic. In it, you'll learn how to apply common algorithms to the practical programming problems you face every day. You'll start with tasks like sorting and searching. As you build up your skills, you'll tackle more complex problems like data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python. By the end of this book, you will have mastered widely applicable algorithms as well as how and when to use them. What's Inside Covers search, sort, and graph algorithms Over 400 pictures with detailed walkthroughs Performance trade-offs between algorithms Python-based code samples About the Reader This easy-to-read, picture-heavy introduction is suitable for self-taught programmers, engineers, or anyone who wants to brush up on algorithms. About the Author Aditya Bhargava is a Software Engineer with a dual background in Computer Science and Fine Arts. He blogs on programming at adit.io. Table of Contents Introduction to algorithms Selection sort Recursion Quicksort Hash tables Breadth-first search Dijkstra's algorithm Greedy algorithms Dynamic programming K-nearest neighbors |
| data science bootcamps reddit: Python for Data Analysis Wes McKinney, 2017-09-25 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples |
| data science bootcamps reddit: Barbarians at the Gate Bryan Burrough, John Helyar, 2009-10-13 #1 New York Times bestseller and arguably the best business narrative ever written, Barbarians at the Gate is the classic account of the fall of RJR Nabisco at the hands of a buyout from investment firm KKR. A book that stormed both the bestseller list and the public imagination, a book that created a genre of its own, and a book that gets at the heart of Wall Street and the '80s culture it helped define, Barbarians at the Gate is a modern classic—a masterpiece of investigatory journalism and a rollicking book of corporate derring-do and financial swordsmanship. The fight to control RJR Nabisco during October and November of 1988 was more than just the largest takeover in Wall Street history. Marked by brazen displays of ego not seen in American business for decades, it became the high point of a new gilded age and its repercussions are still being felt. The tale remains the ultimate story of greed and glory—a story and a cast of characters that determined the course of global business and redefined how deals would be done and fortunes made in the decades to come. Barbarians at the Gate is the gripping account of these two frenzied months, of deal makers and publicity flaks, of an old-line industrial powerhouse (home of such familiar products a Oreos and Camels) that became the victim of the ruthless and rapacious style of finance in the 1980s. As reporters for The Wall Street Journal, Burrough and Helyar had extensive access to all the characters in this drama. They take the reader behind the scenes at strategy meetings and society dinners, into boardrooms and bedrooms, providing an unprecedentedly detailed look at how financial operations at the highest levels are conducted but also a richly textured social history of wealth at the twilight of the Reagan era. At the center of the huge power struggle is RJR Nabisco's president, the high-living Ross Johnson. It's his secret plan to buy out the company that sets the frenzy in motion, attracting the country's leading takeover players: Henry Kravis, the legendary leveraged-buyout king of investment firm KKR, whose entry into the fray sets off an acquisitive commotion; Peter Cohen, CEO of Shearson Lehman Hutton and Johnson's partner, who needs a victory to propel his company to an unchallenged leadership in the lucrative mergers and acquisitions field; the fiercely independent Ted Forstmann, motivated as much by honor as by his rage at the corruption he sees taking over the business he cherishes; Jim Maher and his ragtag team, struggling to regain credibility for the decimated ranks at First Boston; and an army of desperate bankers, lawyers, and accountants, all drawn inexorably to the greatest prize of their careers—and one of the greatest prizes in the history of American business. Written with the bravado of a novel and researched with the diligence of a sweeping cultural history, Barbarians at the Gate is present at the front line of every battle of the campaign. Here is the unforgettable story of that takeover in all its brutality. In a new afterword specially commissioned for the story's 20th anniversary, Burrough and Helyar return to visit the heroes and villains of this epic story, tracing the fallout of the deal, charting the subsequent success and failure of those involved, and addressing the incredible impact this story—and the book itself—made on the world. |
| data science bootcamps reddit: 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 bootcamps reddit: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| data science bootcamps reddit: Programming Collective Intelligence Toby Segaran, 2007-08-16 Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details. -- Dan Russell, Google Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths. -- Tim Wolters, CTO, Collective Intellect |
| data science bootcamps reddit: The Art of Learning Josh Waitzkin, 2008-05-27 An eight-time national chess champion and world champion martial artist shares the lessons he has learned from two very different competitive arenas, identifying key principles about learning and performance that readers can apply to their life goals. Reprint. 35,000 first printing. |
| data science bootcamps reddit: The Tragedy of Heterosexuality Jane Ward, 2020-09-01 Winner, 2021 PROSE Award in the Cultural Anthropology & Sociology Category Finalist, 2021 Lambda Literary Award in LGBTQ Studies A troubling account of heterosexual desire in the era of #MeToo Heterosexuality is in crisis. Reports of sexual harassment, misconduct, and rape saturate the news in the era of #MeToo. Straight men and women spend thousands of dollars every day on relationship coaches, seduction boot camps, and couple’s therapy in a search for happiness. In The Tragedy of Heterosexuality, Jane Ward smartly explores what, exactly, is wrong with heterosexuality in the twenty-first century, and what straight people can do to fix it for good. She shows how straight women, and to a lesser extent straight men, have tried to mend a fraught patriarchal system in which intimacy, sexual fulfillment, and mutual respect are expected to coexist alongside enduring forms of inequality, alienation, and violence in straight relationships. Ward also takes an intriguing look at the multi-billion-dollar self-help industry, which markets goods and services to help heterosexual couples without addressing the root of their problems. Ultimately, she encourages straight men and women to take a page out of queer culture, reminding them “about the human capacity to desire, fuck, and show respect at the same time.” |
| data science bootcamps reddit: Tiny Habits B. J. Fogg, 2020 The world's leading expert on habit formation shows how you can have a happier, healthier life: by starting small. Myth: Change is hard. Reality: Change can be easy if you know the simple steps of Behavior Design. Myth: It's all about willpower. Reality: Willpower is fickle and finite, and exactly the wrong way to create habits. Myth: You have to make a plan and stick to it. Reality: You transform your life by starting small and being flexible. BJ FOGG is here to change your life--and revolutionize how we think about human behavior. Based on twenty years of research and Fogg's experience coaching more than 40,000 people, Tiny Habits cracks the code of habit formation. With breakthrough discoveries in every chapter, you'll learn the simplest proven ways to transform your life. Fogg shows you how to feel good about your successes instead of bad about your failures. Whether you want to lose weight, de-stress, sleep better, or be more productive each day, Tiny Habits makes it easy to achieve. Already the habit guru to companies around the world, Fogg brings his proven method to a global audience for the first time. Whether you want to lose weight, de-stress, sleep better, or exercise more, Tiny Habits makes it easy to achieve. |
| data science bootcamps reddit: Deep Learning from Scratch Seth Weidman, 2019-09-09 With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You’ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way. Author Seth Weidman shows you how neural networks work using a first principles approach. You’ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you’ll be set up for success on all future deep learning projects. This book provides: Extremely clear and thorough mental models—accompanied by working code examples and mathematical explanations—for understanding neural networks Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework Working implementations and clear-cut explanations of convolutional and recurrent neural networks Implementation of these neural network concepts using the popular PyTorch framework |
| data science bootcamps reddit: An Introduction to Data Science Jeffrey S. Saltz, Jeffrey M. Stanton, 2017-08-25 An Introduction to Data Science is an easy-to-read data science textbook for those with no prior coding knowledge. It features exercises at the end of each chapter, author-generated tables and visualizations, and R code examples throughout. |
| data science bootcamps reddit: Blown to Bits Harold Abelson, Ken Ledeen, Harry R. Lewis, 2008 'Blown to Bits' is about how the digital explosion is changing everything. The text explains the technology, why it creates so many surprises and why things often don't work the way we expect them to. It is also about things the information explosion is destroying: old assumptions about who is really in control of our lives. |
| data science bootcamps reddit: Data Analysis Using SQL and Excel Gordon S. Linoff, 2010-09-16 Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like. |
| data science bootcamps reddit: Python for Everybody Charles R. Severance, 2016-04-09 Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.Python is an easy to use and easy to learn programming language that is freely available on Macintosh, Windows, or Linux computers. So once you learn Python you can use it for the rest of your career without needing to purchase any software.This book uses the Python 3 language. The earlier Python 2 version of this book is titled Python for Informatics: Exploring Information.There are free downloadable electronic copies of this book in various formats and supporting materials for the book at www.pythonlearn.com. The course materials are available to you under a Creative Commons License so you can adapt them to teach your own Python course. |
| data science bootcamps reddit: 52 E-mails to Transform Your Marriage Samantha Rodman, 2016-11-01 Press “send” for amazing results! With 52 E-mails to Transform Your Marriage, you’ll find a year’s worth of e-mails to help you reconnect with your spouse, reignite intimacy, and keep your love alive. There’s no doubt marriage can be a challenge—we’ve all heard that half of marriages end in divorce. A common problem you may face as a couple is feeling stuck or disconnected—lonely within the marriage—as if you and your spouse were worlds apart, even as you present a united front. Attempts to discuss the problem may lead to painful arguments, and even couples therapy may prove more expensive and time-consuming than effective, putting each of you on the spot and moving so quickly that you may leave, session after session, without feeling closer. E-mail, however, can be a much less threatening way to communicate your true thoughts and feelings. Based on the author’s popular online relationship coaching sessions, each chapter of this book provides an e-mail writing assignment focused on a different topic, such as sex, intimacy, communication, trust, and the future. These weekly assignments will grant you both the time to write—which can be extremely therapeutic in itself—and read what the other has to say without the need for an immediate response. With 52 E-mails to Transform Your Marriage, you’ll discover useful tips for good communication, learn how to respond to messages with empathy and validation, and be well on your way to rediscovering and sustaining the love that brought you together in the first place. |
| data science bootcamps reddit: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| data science bootcamps reddit: The Infinite Machine Camila Russo, 2020-07-14 Written with the verve of such works as The Big Short, The History of the Future, and The Spider Network, here is the fascinating, true story of the rise of Ethereum, the second-biggest digital asset in the world, the growth of cryptocurrency, and the future of the internet as we know it. Everyone has heard of Bitcoin, but few know about the second largest cryptocurrency, Ethereum, which has been heralded as the next internet. The story of Ethereum begins with Vitalik Buterin, a supremely gifted nineteen-year-old autodidact who saw the promise of blockchain when the technology was in its earliest stages. He convinced a crack group of coders to join him in his quest to make a super-charged, global computer. The Infinite Machine introduces Vitalik’s ingenious idea and unfolds Ethereum’s chaotic beginnings. It then explores the brilliant innovation and reckless greed the platform—an infinitely adaptable foundation for experimentation and new applications—has unleashed and the consequences that resulted as the frenzy surrounding it grew: increased regulatory scrutiny, incipient Wall Street interest, and the founding team’s effort to get the Ethereum platform to scale so it can eventually be accessible to the masses. Financial journalist and cryptocurrency expert Camila Russo details the wild and often hapless adventures of a team of hippy-anarchists, reluctantly led by an ambivalent visionary, and lays out how this new foundation for the internet will spur both transformation and fraud—turning some into millionaires and others into felons—and revolutionize our ideas about money. |
| data science bootcamps reddit: The Statistical Sleuth Fred L. Ramsey, Daniel W. Schafer, 2002 Prepare for exams and succeed in your statistics course with this comprehensive solutions manual! Featuring worked out-solutions to the problems in THE STATISTICAL SLEUTH: A COURSE IN METHODS OF DATA ANALYSIS, 2nd Edition, this manual shows you how to approach and solve problems using the same step-by-step explanations found in your textbook examples. |
| data science bootcamps reddit: Models Mark Manson, 2017-09-11 You can become irresistibly attractive to women without changing who you are. So says Mark Manson, superstar blogger and author of the international bestseller, The Subtle Art of Not Giving A F*ck, a self help book that packs a punch. Mark brings the same approach to teaching men what they need to know about attracting women. In Models he shows us how much it sucks trying to attract women using the tricks and tactics recommended by other books. Instead, he says, men need to focus on seduction as an emotional process not a physical or social one. What matters is the intention, the motivation, the authenticity. To improve your dating life you must improve your emotional life - how you feel about yourself and how you express yourself to others. Funny, irreverent and confronting, Models is a mature and honest guide on how a man can attract women by giving up the bullsh*t and becoming an honest broker. A detailed guide to modern sexual ethics Sydney Morning Herald There's nothing subtle about Mark Manson. He's crude and vulgar and doesn't give a f*ck . . . He's as painfully honest as he is outrageously funny Huffington Post |
| data science bootcamps reddit: Oracle 12c: SQL Joan Casteel, 2015-09-08 Introduce the latest version of the fundamental SQL language used in all relational databases today with Casteel’s ORACLE 12C: SQL, 3E. Much more than a study guide, this edition helps those who have only a basic knowledge of databases master the latest SQL and Oracle concepts and techniques. Learners gain a strong understanding of how to use Oracle 12c SQL most effectively as they prepare for the first exam in the Oracle Database Administrator or Oracle Developer Certification Exam paths. This edition initially focuses on creating database objects, including tables, constraints, indexes, sequences, and more. The author then explores data query techniques, such as row filtering, joins, single-row functions, aggregate functions, subqueries, and views, as well as advanced query topics. ORACLE 12C: SQL, 3E introduces the latest features and enhancements in 12c, from enhanced data types and invisible columns to new CROSS and OUTER APPLY methods for joins. To help readers transition to further studies, appendixes introduce SQL tuning, compare Oracle's SQL syntax with other databases, and overview Oracle connection interface tools: SQL Developer and SQL Plus. Readers can trust ORACLE 12C: SQL, 3E to provide the knowledge for Oracle certification testing and the solid foundation for pursuing a career as a successful database administrator or developer. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version. |
| data science bootcamps reddit: Black Hole Focus Isaiah Hankel, 2014-05-05 ...an absurdly motivating book. –A.J. Jacobs, New York Times bestselling author Don’t get stuck on a career path you have no passion for. Don’t waste your intelligence on something that doesn’t really mean anything more to you than a paycheck. Let Isaiah Hankel help you define a focus so powerful that everything in your life will be pulled towards it. Create your purpose and change your life. Be focused. Be fulfilled. Be successful. Black Hole Focus has been endorsed by top names in business, entrepreneurship, and academia, including 4 times New York Times bestseller AJ Jacobs and Harvard Medical School Postdoc Director Dr. Jim Gould. The book is broken up into 3 different sections; the first section shows you why you need a purpose in life, the second section shows you how to find your new purpose, and the third section shows you how to achieve your goals when facing adversity. In this book, you will learn: How to understand what you really want in life and how to get it Why people with a powerful purpose live to 100 How to rapidly improve focus and change your life using the secret techniques of an international memory champion How people like Jim Carrey, Oprah Winfrey, and J.K. Rowling transformed pain into purpose How to start a business by avoiding willpower depletion and the life hack lie Black Hole Focus includes exclusive case studies from medical practitioners, research scientists, lawyers, corporate executives and small business owners who have used the techniques described in this book to achieve massive success in their own lives. About the Author: Dr. Hankel is an internationally recognized expert in the biotechnology industry and prolific public speaker. He's given over 250 seminars in 22 different countries while working with many of the world's most respected companies and institutions, including Harvard University, Oxford University, Roche Pharmaceuticals, Eli Lilly & Company, Baxter International and Pfizer. Dr. Hankel uses the science of purpose and the principles of entrepreneurship to help people achieve their biggest goals. |
| data science bootcamps reddit: Construction Estimating Using Excel Steven J. Peterson, 2007 For courses in Construction Estimating in two year and four year construction management programs. Construction Estimating with Excel introduces readers to the fundamental principles of estimating and includes drawing sets, real-world exercises, and examples that give beginners critical estimating experience. The book moves step-by-step through the estimating process, discussing the art of estimating, the quantity takeoff, how to put costs to the estimate and how to finalize the bid. It is also the first text to demonstrate how Microsoft Excel can be used to improve the estimating process. Because it introduces spreadsheets as a way of increasing estimating productivity and accuracy, the book can help both beginning and experienced estimators improve their skills. |
| data science bootcamps reddit: C++ Programming: From Problem Analysis to Program Design D. S. Malik, 2017-05-24 Learn how to program with C++ using today’s definitive choice for your first programming language experience -- C++ PROGRAMMING: FROM PROBLEM ANALYSIS TO PROGRAM DESIGN, 8E. D.S. Malik’s time-tested, user-centered methodology incorporates a strong focus on problem-solving with full-code examples that vividly demonstrate the hows and whys of applying programming concepts and utilizing C++ to work through a problem. Thoroughly updated end-of-chapter exercises, more than 20 extensive new programming exercises, and numerous new examples drawn from Dr. Malik’s experience further strengthen the reader’s understanding of problem solving and program design in this new edition. This book highlights the most important features of C++ 14 Standard with timely discussions that ensure this edition equips you to succeed in your first programming experience and well beyond. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version. |
| data science bootcamps reddit: The White Coat Investor James M. Dahle, 2014-01 Written by a practicing emergency physician, The White Coat Investor is a high-yield manual that specifically deals with the financial issues facing medical students, residents, physicians, dentists, and similar high-income professionals. Doctors are highly-educated and extensively trained at making difficult diagnoses and performing life saving procedures. However, they receive little to no training in business, personal finance, investing, insurance, taxes, estate planning, and asset protection. This book fills in the gaps and will teach you to use your high income to escape from your student loans, provide for your family, build wealth, and stop getting ripped off by unscrupulous financial professionals. Straight talk and clear explanations allow the book to be easily digested by a novice to the subject matter yet the book also contains advanced concepts specific to physicians you won't find in other financial books. This book will teach you how to: Graduate from medical school with as little debt as possible Escape from student loans within two to five years of residency graduation Purchase the right types and amounts of insurance Decide when to buy a house and how much to spend on it Learn to invest in a sensible, low-cost and effective manner with or without the assistance of an advisor Avoid investments which are designed to be sold, not bought Select advisors who give great service and advice at a fair price Become a millionaire within five to ten years of residency graduation Use a Backdoor Roth IRA and Stealth IRA to boost your retirement funds and decrease your taxes Protect your hard-won assets from professional and personal lawsuits Avoid estate taxes, avoid probate, and ensure your children and your money go where you want when you die Minimize your tax burden, keeping more of your hard-earned money Decide between an employee job and an independent contractor job Choose between sole proprietorship, Limited Liability Company, S Corporation, and C Corporation Take a look at the first pages of the book by clicking on the Look Inside feature Praise For The White Coat Investor Much of my financial planning practice is helping doctors to correct mistakes that reading this book would have avoided in the first place. - Allan S. Roth, MBA, CPA, CFP(R), Author of How a Second Grader Beats Wall Street Jim Dahle has done a lot of thinking about the peculiar financial problems facing physicians, and you, lucky reader, are about to reap the bounty of both his experience and his research. - William J. Bernstein, MD, Author of The Investor's Manifesto and seven other investing books This book should be in every career counselor's office and delivered with every medical degree. - Rick Van Ness, Author of Common Sense Investing The White Coat Investor provides an expert consult for your finances. I now feel confident I can be a millionaire at 40 without feeling like a jerk. - Joe Jones, DO Jim Dahle has done for physician financial illiteracy what penicillin did for neurosyphilis. - Dennis Bethel, MD An excellent practical personal finance guide for physicians in training and in practice from a non biased source we can actually trust. - Greg E Wilde, M.D Scroll up, click the buy button, and get started today! |
| data science bootcamps reddit: Business Intelligence Demystified Anoop Kumar V K, 2021-09-25 Clear your doubts about Business Intelligence and start your new journey KEY FEATURES ● Includes successful methods and innovative ideas to achieve success with BI. ● Vendor-neutral, unbiased, and based on experience. ● Highlights practical challenges in BI journeys. ● Covers financial aspects along with technical aspects. ● Showcases multiple BI organization models and the structure of BI teams. DESCRIPTION The book demystifies misconceptions and misinformation about BI. It provides clarity to almost everything related to BI in a simplified and unbiased way. It covers topics right from the definition of BI, terms used in the BI definition, coinage of BI, details of the different main uses of BI, processes that support the main uses, side benefits, and the level of importance of BI, various types of BI based on various parameters, main phases in the BI journey and the challenges faced in each of the phases in the BI journey. It clarifies myths about self-service BI and real-time BI. The book covers the structure of a typical internal BI team, BI organizational models, and the main roles in BI. It also clarifies the doubts around roles in BI. It explores the different components that add to the cost of BI and explains how to calculate the total cost of the ownership of BI and ROI for BI. It covers several ideas, including unconventional ideas to achieve BI success and also learn about IBI. It explains the different types of BI architectures, commonly used technologies, tools, and concepts in BI and provides clarity about the boundary of BI w.r.t technologies, tools, and concepts. The book helps you lay a very strong foundation and provides the right perspective about BI. It enables you to start or restart your journey with BI. WHAT YOU WILL LEARN ● Builds a strong conceptual foundation in BI. ● Gives the right perspective and clarity on BI uses, challenges, and architectures. ● Enables you to make the right decisions on the BI structure, organization model, and budget. ● Explains which type of BI solution is required for your business. ● Applies successful BI ideas. WHO THIS BOOK IS FOR This book is a must-read for business managers, BI aspirants, CxOs, and all those who want to drive the business value with data-driven insights. TABLE OF CONTENTS 1. What is Business Intelligence? 2. Why do Businesses need BI? 3. Types of Business Intelligence 4. Challenges in Business Intelligence 5. Roles in Business Intelligence 6. Financials of Business Intelligence 7. Ideas for Success with BI 8. Introduction to IBI 9. BI Architectures 10. Demystify Tech, Tools, and Concepts in BI |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
