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| data science python coding interview questions: Ace the Data Science Interview Kevin Huo, Nick Singh, 2021 |
| data science python coding interview questions: Cracking the Data Science Interview Maverick Lin, 2019-12-17 Cracking the Data Science Interview is the first book that attempts to capture the essence of data science in a concise, compact, and clean manner. In a Cracking the Coding Interview style, Cracking the Data Science Interview first introduces the relevant concepts, then presents a series of interview questions to help you solidify your understanding and prepare you for your next interview. Topics include: - Necessary Prerequisites (statistics, probability, linear algebra, and computer science) - 18 Big Ideas in Data Science (such as Occam's Razor, Overfitting, Bias/Variance Tradeoff, Cloud Computing, and Curse of Dimensionality) - Data Wrangling (exploratory data analysis, feature engineering, data cleaning and visualization) - Machine Learning Models (such as k-NN, random forests, boosting, neural networks, k-means clustering, PCA, and more) - Reinforcement Learning (Q-Learning and Deep Q-Learning) - Non-Machine Learning Tools (graph theory, ARIMA, linear programming) - Case Studies (a look at what data science means at companies like Amazon and Uber) Maverick holds a bachelor's degree from the College of Engineering at Cornell University in operations research and information engineering (ORIE) and a minor in computer science. He is the author of the popular Data Science Cheatsheet and Data Engineering Cheatsheet on GCP and has previous experience in data science consulting for a Fortune 500 company focusing on fraud analytics. |
| data science python coding interview questions: A Collection of Data Science Interview Questions Solved in Python and Spark Antonio Gulli, 2015-09-22 BigData and Machine Learning in Python and Spark |
| data science python coding interview questions: 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 python coding interview questions: Elements of Programming Interviews Adnan Aziz, Tsung-Hsien Lee, Amit Prakash, 2012 The core of EPI is a collection of over 300 problems with detailed solutions, including 100 figures, 250 tested programs, and 150 variants. The problems are representative of questions asked at the leading software companies. The book begins with a summary of the nontechnical aspects of interviewing, such as common mistakes, strategies for a great interview, perspectives from the other side of the table, tips on negotiating the best offer, and a guide to the best ways to use EPI. The technical core of EPI is a sequence of chapters on basic and advanced data structures, searching, sorting, broad algorithmic principles, concurrency, and system design. Each chapter consists of a brief review, followed by a broad and thought-provoking series of problems. We include a summary of data structure, algorithm, and problem solving patterns. |
| data science python coding interview questions: Python Interview Questions Meenu Kohli, 2019-04-05 The book is written assuming that the reader has basic knowledge of Python programming. A brief introduction is provided for all relevant topics. Every topic is followed by all types of possible questions that an examiner or interviewer can ask the reader. The questions are arranged chapter wise so that it is easy for the reader to move from easy to complex questions. Key features:Strengthens the foundations. Lists down all important points that you need to know related to various topics in an organized manner. Prepares you for coding related interview questions. Prepares you with questions related to Algorithms and Data structures. Prepares you for theoretical questions. Provides In depth explanation of complex topics and Questions. Focuses on how to think logically to solve a problem. Follows systematic approach that will help you to prepare for an interview in short duration of time. Prepares you to think logically and answer interview questions. Table of ContentsSECTION I : PYTHON BASICSIntroduction to Python Data Types and Their in-built Functions Operators in PythonDecision Making and Loops User Defined Functions Classes and Inheritance FilesSECTION II: PYTHON DATA STRUCTURE AND ALGORITHMAlgorithm Analysis and Big-O Array Sequence Stacks, Queues, and Deque Linked List Recursion TreesSearching and Sorting |
| data science python coding interview questions: Machine Learning Bookcamp Alexey Grigorev, 2021-11-23 The only way to learn is to practice! In Machine Learning Bookcamp, you''ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you''ve learned in previous chapters. By the end of the bookcamp, you''ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technology Machine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that''s exactly what you''ll be doing in Machine Learning Bookcamp. about the book In Machine Learning Bookcamp you''ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you''ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You''ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you''re done working through these fun and informative projects, you''ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems. what''s inside Code fundamental ML algorithms from scratch Collect and clean data for training models Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images and text Deploy ML models to a production-ready environment about the reader For readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning. |
| data science python coding interview questions: Data Science with Machine Learning Narayanan Vishwanathan, 2019-09-20 Starts with statistics then goes towards Core Python followed by numpy to pandas to scipy and sklearnKey features Easy to learn, step by step explanation of examples. Questions related to core/basic Python, Excel, basic and advanced statistics are included. Covers numpy, scipy, sklearn and pandas to a greater detail with good number of examples Description The book e;Data science with Machine learning- Python interview questionse; is a true companion of people aspiring for data science and machine learning and provides answers to mostly asked questions in a easy to remember and presentable form.Data science is one of the hottest topics mainly because of the application areas it is involved and things which were once upon of time, impossible with earlier software has been made easy. This book is mainly intended to be used as last-minute revision, before interview, as all the important concepts have been given in simple and understand format. Many examples have been provided so that same can be used while giving answers in interview.This book tries to include various terminologies and logic used both as a part of Data Science and Machine learning for last minute revision. As such you can say that this book acts as a companion whenever you want to go for interview.Simple to use words have been used in the answers for the questions to help ease of remembering and representation of same. Examples where ever deemed necessary have been provided so that same can be used while giving answers in interview. Author tried to consolidate whatever he came across, on multiple interviews that he attended and put the same in words so that it becomes easy for the reader of the book to give direction on how the interview would be.With the number of data science jobs increasing, Author is sure that everyone who wants to pursue this field would like to keep this book as a constant companion. What will you learn You can learn the basic concept and terms related to Data Science You will get to learn how to program in python You can learn the basic questions of python programming By reading this book you can get to know the basics of Numpy You will get familiarity with the questions asked in interview related to Pandas. You will learn the concepts of Scipy, Matplotib, and Statistics with Excel Sheet Who this book is forThe book is intended for anyone wish to learn Python Data Science, Numpy, Pandas, Scipy, Matplotib and Statistics with Excel Sheet. This book content also covers the basic questions which are asked during an interview. This book is mainly intended to help people represent their answer in a sensible way to the interviewer. The answers have been carefully rendered in a way to make things quite simple and yet represent the seriousness and complexity of matter. Since data science is incomplete without mathematics we have also included a part of the book dedicated to statistics. Table of contents1. Data Science Basic Questions and Terms2. Python Programming Questions3. Numpy Interview Questions4. Pandas Interview Questions5. Scipy and its Applications6. Matplotlib Samples to Remember7. Statistics with Excel Sheet About the authorMr Vishwanathan has twenty years of hard code experience in software industry spanning across many multinational companies and domains. Playing with data to derive meaningful insights has been his domain and that is what took him towards data science and machine learning. |
| data science python coding interview questions: A Collection of Advanced Data Science and Machine Learning Interview Questions Solved in Python and Spark (Ii) Antonio Gulli, 2015-11-18 A collection of Machine Learning interview questions in Python and Spark |
| data science python coding interview questions: Cracking The Machine Learning Interview Nitin Suri, 2018-12-18 A breakthrough in machine learning would be worth ten Microsofts. -Bill Gates Despite being one of the hottest disciplines in the Tech industry right now, Artificial Intelligence and Machine Learning remain a little elusive to most.The erratic availability of resources online makes it extremely challenging for us to delve deeper into these fields. Especially when gearing up for job interviews, most of us are at a loss due to the unavailability of a complete and uncondensed source of learning. Cracking the Machine Learning Interview Equips you with 225 of the best Machine Learning problems along with their solutions. Requires only a basic knowledge of fundamental mathematical and statistical concepts. Assists in learning the intricacies underlying Machine Learning concepts and algorithms suited to specific problems. Uniquely provides a manifold understanding of both statistical foundations and applied programming models for solving problems. Discusses key points and concrete tips for approaching real life system design problems and imparts the ability to apply them to your day to day work. This book covers all the major topics within Machine Learning which are frequently asked in the Interviews. These include: Supervised and Unsupervised Learning Classification and Regression Decision Trees Ensembles K-Nearest Neighbors Logistic Regression Support Vector Machines Neural Networks Regularization Clustering Dimensionality Reduction Feature Extraction Feature Engineering Model Evaluation Natural Language Processing Real life system design problems Mathematics and Statistics behind the Machine Learning Algorithms Various distributions and statistical tests This book can be used by students and professionals alike. It has been drafted in a way to benefit both, novices as well as individuals with substantial experience in Machine Learning. Following Cracking The Machine Learning Interview diligently would equip you to face any Machine Learning Interview. |
| data science python coding interview questions: Python Quick Interview Guide Shyamkant Limaye, 2021-04-10 Quick solutions to frequently asked algorithm and data structure questions.Ê KEY FEATURESÊÊ _ Learn how to crack the Data structure and Algorithms Code test using the top 75 questions/solutions discussed in the book. _ Refresher on Python data structures and writing clean, actionable python codes. _ Simplified solutions on translating business problems into executable programs and applications. DESCRIPTIONÊ Python is the most popular programming language, and hence, there is a huge demand for Python programmers. Even if you have learnt Python or have done projects on AI, you cannot enter the top companies unless you have cleared the Algorithms and data Structure coding test. This book presents 75 most frequently asked coding questions by top companies of the world. It not only focuses on the solution strategy, but also provides you with the working code. This book will equip you with the skills required for developing and analyzing algorithms for various situations. This book teaches you how to measure Time Complexity, it then provides solutions to questions on the Linked list, Stack, Hash table, and Math. Then you can review questions and solutions based on graph theory and application techniques. Towards the end, you will come across coding questions on advanced topics such as Backtracking, Greedy, Divide and Conquer, and Dynamic Programming. After reading this book, you will successfully pass the python interview with high confidence and passion for exploring python in future. WHAT YOU WILL LEARN _ Design an efficient algorithm to solve the problem. _ Learn to use python tricks to make your program competitive. _ Learn to understand and measure time and space complexity. _ Get solutions to questions based on Searching, Sorting, Graphs, DFS, BFS, Backtracking, Dynamic programming. WHO THIS BOOK IS FORÊÊ This book will help professionals and beginners clear the Data structures and Algorithms coding test. Basic knowledge of Python and Data Structures is a must. TABLE OF CONTENTS 1. Lists, binary search and strings 2. Linked lists and stacks 3. Hash table and maths 4. Trees and graphs 5. Depth first search 6. Breadth first search 7. Backtracking 8. Greedy and divide and conquer algorithms 9. Dynamic programming |
| data science python coding interview questions: Think Data Structures Allen B. Downey, 2017-07-07 If you’re a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineering—data structures and algorithms—in a way that’s clearer, more concise, and more engaging than other materials. By emphasizing practical knowledge and skills over theory, author Allen Downey shows you how to use data structures to implement efficient algorithms, and then analyze and measure their performance. You’ll explore the important classes in the Java collections framework (JCF), how they’re implemented, and how they’re expected to perform. Each chapter presents hands-on exercises supported by test code online. Use data structures such as lists and maps, and understand how they work Build an application that reads Wikipedia pages, parses the contents, and navigates the resulting data tree Analyze code to predict how fast it will run and how much memory it will require Write classes that implement the Map interface, using a hash table and binary search tree Build a simple web search engine with a crawler, an indexer that stores web page contents, and a retriever that returns user query results Other books by Allen Downey include Think Java, Think Python, Think Stats, and Think Bayes. |
| data science python coding interview questions: 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 python coding interview questions: Programming Interviews Exposed John Mongan, Noah Suojanen Kindler, Eric Giguère, 2011-08-10 The pressure is on during the interview process but with the right preparation, you can walk away with your dream job. This classic book uncovers what interviews are really like at America's top software and computer companies and provides you with the tools to succeed in any situation. The authors take you step-by-step through new problems and complex brainteasers they were asked during recent technical interviews. 50 interview scenarios are presented along with in-depth analysis of the possible solutions. The problem-solving process is clearly illustrated so you'll be able to easily apply what you've learned during crunch time. You'll also find expert tips on what questions to ask, how to approach a problem, and how to recover if you become stuck. All of this will help you ace the interview and get the job you want. What you will learn from this book Tips for effectively completing the job application Ways to prepare for the entire programming interview process How to find the kind of programming job that fits you best Strategies for choosing a solution and what your approach says about you How to improve your interviewing skills so that you can respond to any question or situation Techniques for solving knowledge-based problems, logic puzzles, and programming problems Who this book is for This book is for programmers and developers applying for jobs in the software industry or in IT departments of major corporations. Wrox Beginning guides are crafted to make learning programming languages and technologies easier than you think, providing a structured, tutorial format that will guide you through all the techniques involved. |
| data science python coding interview questions: 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 python coding interview questions: Cracking the Coding Interview Gayle Laakmann McDowell, 2011 Now in the 5th edition, Cracking the Coding Interview gives you the interview preparation you need to get the top software developer jobs. This book provides: 150 Programming Interview Questions and Solutions: From binary trees to binary search, this list of 150 questions includes the most common and most useful questions in data structures, algorithms, and knowledge based questions. 5 Algorithm Approaches: Stop being blind-sided by tough algorithm questions, and learn these five approaches to tackle the trickiest problems. Behind the Scenes of the interview processes at Google, Amazon, Microsoft, Facebook, Yahoo, and Apple: Learn what really goes on during your interview day and how decisions get made. Ten Mistakes Candidates Make -- And How to Avoid Them: Don't lose your dream job by making these common mistakes. Learn what many candidates do wrong, and how to avoid these issues. Steps to Prepare for Behavioral and Technical Questions: Stop meandering through an endless set of questions, while missing some of the most important preparation techniques. Follow these steps to more thoroughly prepare in less time. |
| data science python coding interview questions: Heard in Data Science Interviews Kal Mishra, 2018-10-03 A collection of over 650 actual Data Scientist/Machine Learning Engineer job interview questions along with their full answers, references, and useful tips |
| data science python coding interview questions: Quant Job Interview Questions and Answers Mark Joshi, Nick Denson, Nicholas Denson, Andrew Downes, 2013 The quant job market has never been tougher. Extensive preparation is essential. Expanding on the successful first edition, this second edition has been updated to reflect the latest questions asked. It now provides over 300 interview questions taken from actual interviews in the City and Wall Street. Each question comes with a full detailed solution, discussion of what the interviewer is seeking and possible follow-up questions. Topics covered include option pricing, probability, mathematics, numerical algorithms and C++, as well as a discussion of the interview process and the non-technical interview. All three authors have worked as quants and they have done many interviews from both sides of the desk. Mark Joshi has written many papers and books including the very successful introductory textbook, The Concepts and Practice of Mathematical Finance. |
| data science python coding interview questions: Be the Outlier Shrilata Murthy, 2020-07-27 According to LinkedIn's third annual U.S. Emerging Jobs Report, the data scientist role is ranked third among the top-15 emerging jobs in the U.S. Though the field of data science has been exploding, there didn't appear to be a comprehensive resource to help data scientists navigate the interview process... until now. In Be the Outlier: How to Ace Data Science Interviews, data scientist Shrilata Murthy covers all aspects of a data science interview in today's industry. Murthy combines her own experience in the job market with expert insight from data scientists with Google, Facebook, Amazon, NASA, Aetna, MBB & Big 4 consulting firms, and many more. In this book, you'll learn... the foundational knowledge that is key to any data science interview the 100-Word Story framework for writing a stellar resume what to expect from a variety of interview styles (take-home, presentation, case study, etc.), and actionable ways to differentiate yourself from your peers. By using real-world examples, practice questions, and sample interviews, Murthy has created an easy-to-follow guide that will help you crack any data science interview. After reading Be the Outlier, get ready to land your dream job in data science. |
| data science python coding interview questions: Coding Interview Questions Narasimha Karumanchi, 2012 Peeling Data Structures and Algorithms: * Programming puzzles for interviews * Campus Preparation * Degree/Masters Course Preparation * Instructor's * GATE Preparation * Big job hunters: Microsoft, Google, Amazon, Yahoo, Flip Kart, Adobe, IBM Labs, Citrix, Mentor Graphics, NetApp, Oracle, Webaroo, De-Shaw, Success Factors, Face book, McAfee and many more * Reference Manual for working people |
| data science python coding interview questions: Data Science Projects with Python Stephen Klosterman, 2019-04-30 Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key FeaturesTackle data science problems by identifying the problem to be solvedIllustrate patterns in data using appropriate visualizationsImplement suitable machine learning algorithms to gain insights from dataBook Description Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You’ll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you’ll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions. By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data. What you will learnInstall the required packages to set up a data science coding environmentLoad data into a Jupyter notebook running PythonUse Matplotlib to create data visualizationsFit machine learning models using scikit-learnUse lasso and ridge regression to regularize your modelsCompare performance between models to find the best outcomesUse k-fold cross-validation to select model hyperparametersWho this book is for If you are a data analyst, data scientist, or business analyst who wants to get started using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of Python and data analytics will help you get the most from this book. Familiarity with mathematical concepts such as algebra and basic statistics will also be useful. |
| data science python coding interview questions: 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 python coding interview questions: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| data science python coding interview questions: Python Interviews Michael Driscoll, 2018-02-28 Mike Driscoll takes you on a journey talking to a hall-of-fame list of truly remarkable Python experts. You’ll be inspired every time by their passion for the Python language, as they share with you their experiences, contributions, and careers in Python. Key Features Hear from these key Python thinkers about the current status of Python, and where it's heading in the future Listen to their close thoughts on significant Python topics, such as Python's role in scientific computing, and machine learning Understand the direction of Python, and what needs to change for Python 4 Book Description Each of these twenty Python Interviews can inspire and refresh your relationship with Python and the people who make Python what it is today. Let these interviews spark your own creativity, and discover how you also have the ability to make your mark on a thriving tech community. This book invites you to immerse in the Python landscape, and let these remarkable programmers show you how you too can connect and share with Python programmers around the world. Learn from their opinions, enjoy their stories, and use their tech tips. • Brett Cannon - former director of the PSF, Python core developer, led the migration to Python 3. • Steve Holden - tireless Python promoter and former chairman and director of the PSF. • Carol Willing - former director of the PSF and Python core developer, Project Jupyter Steering Council member. • Nick Coghlan - founding member of the PSF's Packaging Working Group and Python core developer. • Jessica McKellar - former director of the PSF and Python activist. • Marc-André Lemburg - Python core developer and founding member of the PSF. • Glyph Lefkowitz - founder of Twisted and fellow of the PSF • Doug Hellmann - fellow of the PSF, creator of the Python Module of the Week blog, Python community member since 1998. • Massimo Di Pierro - fellow of the PSF, data scientist and the inventor of web2py. • Alex Martelli - fellow of the PSF and co-author of Python in a Nutshell. • Barry Warsaw - fellow of the PSF, Python core developer since 1995, and original member of PythonLabs. • Tarek Ziadé - founder of Afpy and author of Expert Python Programming. • Sebastian Raschka - data scientist and author of Python Machine Learning. • Wesley Chun - fellow of the PSF and author of the Core Python Programming books. • Steven Lott - Python blogger and author of Python for Secret Agents. • Oliver Schoenborn - author of Pypubsub and wxPython mailing list contributor. • Al Sweigart - bestselling author of Automate the Boring Stuff with Python and creator of the Python modules Pyperclip and PyAutoGUI. • Luciano Ramalho - fellow of the PSF and the author of Fluent Python. • Mike Bayer - fellow of the PSF, creator of open source libraries including SQLAlchemy. • Jake Vanderplas - data scientist and author of Python Data Science Handbook. What you will learn How successful programmers think The history of Python Insights into the minds of the Python core team Trends in Python programming Who this book is for Python programmers and students interested in the way that Python is used – past and present – with useful anecdotes. It will also be of interest to those looking to gain insights from top programmers. |
| data science python coding interview questions: Data Science and Machine Learning Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman, 2019-11-20 Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code |
| data science python coding interview questions: Programming Pearls Jon Bentley, 2016-04-21 When programmers list their favorite books, Jon Bentley’s collection of programming pearls is commonly included among the classics. Just as natural pearls grow from grains of sand that irritate oysters, programming pearls have grown from real problems that have irritated real programmers. With origins beyond solid engineering, in the realm of insight and creativity, Bentley’s pearls offer unique and clever solutions to those nagging problems. Illustrated by programs designed as much for fun as for instruction, the book is filled with lucid and witty descriptions of practical programming techniques and fundamental design principles. It is not at all surprising that Programming Pearls has been so highly valued by programmers at every level of experience. In this revision, the first in 14 years, Bentley has substantially updated his essays to reflect current programming methods and environments. In addition, there are three new essays on testing, debugging, and timing set representations string problems All the original programs have been rewritten, and an equal amount of new code has been generated. Implementations of all the programs, in C or C++, are now available on the Web. What remains the same in this new edition is Bentley’s focus on the hard core of programming problems and his delivery of workable solutions to those problems. Whether you are new to Bentley’s classic or are revisiting his work for some fresh insight, the book is sure to make your own list of favorites. |
| data science python coding interview questions: Coding Interview Questions Narasimha Karumanchi, 2012-05 Coding Interview Questions is a book that presents interview questions in simple and straightforward manner with a clear-cut explanation. This book will provide an introduction to the basics. It comes handy as an interview and exam guide for computer scientists. Programming puzzles for interviews Campus Preparation Degree/Masters Course Preparation Big job hunters: Apple, Microsoft, Google, Amazon, Yahoo, Flip Kart, Adobe, IBM Labs, Citrix, Mentor Graphics, NetApp, Oracle, Webaroo, De-Shaw, Success Factors, Face book, McAfee and many more Reference Manual for working people Topics Covered: Programming BasicsIntroductionRecursion and BacktrackingLinked Lists Stacks Queues Trees Priority Queue and HeapsGraph AlgorithmsSortingSearching Selection Algorithms [Medians] Symbol TablesHashing String Algorithms Algorithms Design Techniques Greedy Algorithms Divide and Conquer Algorithms Dynamic Programming Complexity Classes Design Interview Questions Operating System Concepts Computer Networking Basics Database Concepts Brain Teasers NonTechnical Help Miscellaneous Concepts Note: If you already have Data Structures and Algorithms Made Easy no need to buy this. |
| data science python coding interview questions: Let Us Python Kanetkar Yashavant, 2019-09-20 Learn Python Quickly, A Programmer-Friendly Guide Key features Strengthens the foundations, as detailed explanation of programming language concepts are given. Lists down all important points that you need to know related to various topics in an organized manner. Prepares you for coding related interview and theoretical questions. Provides In depth explanation of complex topics and Questions. Focuses on how to think logically to solve a problem. Follows systematic approach that will help you to prepare for an interview in short duration of time. Description Most Programmer's learning Python are usually comfortable with some or the other programming language and are not interested in going through the typical learning curve of learning the first programming language. Instead, they are looking for something that can get them off the ground quickly. They are looking for similarities and differences in a feature that they have used in other language(s). This book should help them immediately. It guides you from the fundamentals of using module through the use of advanced object orientation. What will you learn Data types, Control flow instructions, console & File Input/Output Strings, list & tuples, List comprehension Sets & Dictionaries, Functions & Lambdas Dictionary Comprehension Modules, classes and objects, Inheritance Operator overloading, Exception handling Iterators & Generators, Decorators, Command-line Parsing Who this book is forStudents, Programmers, researchers, and software developers who wish to learn the basics of Python programming language. Table of contents1. Introduction to Python2. Python Basics3. Strings4. Control Flow Instructions5. Console Input/Output6. Lists7. Tuples8. Sets9. Dictionaries10. Functions11. Modules12. Classes and Objects13. Intricacies of Classes and Objects14. Inheritance15. Exception Handling16. File Input/Output17. MiscellanyAbout the authorYashavant KanetkarThrough his books and Quest Video Courses on C, C++, Java, Python, Data Structures, .NET, IoT, etc. Yashavant Kanetkar has created, moulded and groomed lacs of IT careers in the last three decades. Yashavant's books and Quest videos have made a significant contribution in creating top-notch IT manpower in India and abroad. Yashavant's books are globally recognized and millions of students / professionals have benefitted from them. Yashavant's books have been translated into Hindi, Gujarati, Japanese, Korean and Chinese languages. Many of his books are published in India, USA, Japan, Singapore, Korea and China. Yashavant is a much sought after speaker in the IT field and has conducted seminars/workshops at TedEx, IITs, IIITs, NITs and global software companies. Yashavant has been honored with the prestigious e;Distinguished Alumnus Awarde; by IIT Kanpur for his entrepreneurial, professional and academic excellence. This award was given to top 50 alumni of IIT Kanpur who have made significant contribution towards their profession and betterment of society in the last 50 years. In recognition of his immense contribution to IT education in India, he has been awarded the e;Best .NET Technical Contributore; and e;Most Valuable Professionale; awards by Microsoft for 5 successive years. Yashavant holds a BE from VJTI Mumbai and M.Tech. from IIT Kanpur. Yadhavant's current affiliations include being a Director of KICIT Pvt Ltd. And KSET Pvt Ltd. His Linkedin profile: linkedin.com/in/yashavant-kanetkar-9775255 Aditya Kanetkar holds a Master's Degree in Computer Science from Georgia Tech, Atlanta. Prior to that, he completed his Bachelor's Degree in Computer Science and Engineering from IIT Guwahati. Aditya started his professional career as a Software Engineer at Oracle America Inc. at Redwood City, California. Currently he works with Microsoft Corp., USA. Aditya is a very keen programmer since his intern fays at Redfin, Amazon Inc. and Arista Networks. His current passion is anything remotely connected to Python, Machine Learning and C# related technologies. His Linkedin Profile: linkedin.com/in/aditya-kanetkar-a4292397 |
| data science python coding interview questions: Programming Interview Problems Leonardo Rossi, 2020-11-05 Are you preparing for a programming interview? Would you like to work at one of the Internet giants, such as Google, Facebook, Amazon, Apple, Microsoft or Netflix? Are you looking for a software engineer position? Are you studying computer science or programming? Would you like to improve your programming skills? If the answer to any of these questions is yes, this book is for you! The book contains very detailed answers and explanations for the most common dynamic programming problems asked in programming interviews. The solutions consist of cleanly written code, with plenty of comments, accompanied by verbal explanations, hundreds of drawings, diagrams and detailed examples, to help you get a good understanding of even the toughest problems. The goal is for you to learn the patterns and principles needed to solve even dynamic programming problems that you have never seen before. Here is what you will get: A 180-page book presenting dynamic programming problems that are often asked in interviews. Multiple solutions for each problem, starting from simple but naive answers that are gradually improved until reaching the optimal solution. Plenty of detailed examples and walkthroughs, so that you can see right away how the solution works. 350+ drawings and diagrams which cater towards visual learners. Clear and detailed verbal explanations of how to approach the problems and how the code works. Analysis of time and space complexity. Discussion of other variants of the same problem, with solutions. Unit tests, including the reasoning behind choosing each one (edge case identification, performance evaluation etc.). Suggestions regarding what clarification questions you should ask, for each problem. Multiple solutions to the problems, where appropriate. General Python implementation tips. Wishing you the best of luck with your interviews! |
| data science python coding interview questions: Problem Solving with Algorithms and Data Structures Using Python Bradley N. Miller, David L. Ranum, 2011 Thes book has three key features : fundamental data structures and algorithms; algorithm analysis in terms of Big-O running time in introducied early and applied throught; pytohn is used to facilitates the success in using and mastering data strucutes and algorithms. |
| data science python coding interview questions: Dynamic Programming for Coding Interviews Meenakshi, Kamal Rawat, 2017-01-18 I wanted to compute 80th term of the Fibonacci series. I wrote the rampant recursive function, int fib(int n){ return (1==n || 2==n) ? 1 : fib(n-1) + fib(n-2); } and waited for the result. I wait… and wait… and wait… With an 8GB RAM and an Intel i5 CPU, why is it taking so long? I terminated the process and tried computing the 40th term. It took about a second. I put a check and was shocked to find that the above recursive function was called 204,668,309 times while computing the 40th term. More than 200 million times? Is it reporting function calls or scam of some government? The Dynamic Programming solution computes 100th Fibonacci term in less than fraction of a second, with a single function call, taking linear time and constant extra memory. A recursive solution, usually, neither pass all test cases in a coding competition, nor does it impress the interviewer in an interview of company like Google, Microsoft, etc. The most difficult questions asked in competitions and interviews, are from dynamic programming. This book takes Dynamic Programming head-on. It first explain the concepts with simple examples and then deep dives into complex DP problems. |
| data science python coding interview questions: 500 Data Science Interview Questions and Answers Vamsee Puligadda, Get that job, you aspire for! Want to switch to that high paying job? Or are you already been preparing hard to give interview the next weekend? Do you know how many people get rejected in interviews by preparing only concepts but not focusing on actually which questions will be asked in the interview? Don't be that person this time. This is the most comprehensive Data Science interview questions book that you can ever find out. It contains: 500 most frequently asked and important Data Science interview questions and answers Wide range of questions which cover not only basics in Data Science but also most advanced and complex questions which will help freshers, experienced professionals, senior developers, testers to crack their interviews. |
| data science python coding interview questions: How Smart Machines Think Sean Gerrish, 2018-10-30 Everything you've always wanted to know about self-driving cars, Netflix recommendations, IBM's Watson, and video game-playing computer programs. The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM's Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today's machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world—and to play Atari video games better than humans. He explains Watson's famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution—at least for now. Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people. |
| data science python coding interview questions: Problem Solving in Data Structures & Algorithms Using Python Hemant Jain, 2019-05-16 Problem Solving in Data Structures & Algorithms is a series of books about the usage of Data Structures and Algorithms in computer programming. The book is easy to follow and is written for interview preparation point of view. In these books, the examples are solved in various languages like Go, C, C++, Java, C#, Python, VB, JavaScript and PHP. GitHub Repositories for these books. https: //github.com/Hemant-Jain-Author Book's Composition This book introduces you to the world of data structures and algorithms. Data structures defines the way in which data is arranged in memory for fast and efficient access while algorithms are a set of instruction to solve problems by manipulating these data structures. Designing an efficient algorithm is a very important skill that all software companies, e.g. Microsoft, Google, Facebook etc. pursues. Most of the interviews for these companies are focused on knowledge of data-structures and algorithms. They look for how candidates use concepts of data structures and algorithms to solve complex problems efficiently. Apart from knowing, a programming language you also need to have good command of these key computer fundamentals to not only qualify the interview but also excel in you jobs as a software engineer. This book assumes that you are a C language developer. You are not an expert in C language, but you are well familiar with concepts of classes, functions, arrays, pointers and recursion. At the start of this book, we will be looking into Complexity Analysis followed by the various data structures and their algorithms. We will be looking into a Linked-List, Stack, Queue, Trees, Heap, Hash-Table and Graphs. We will also be looking into Sorting, Searching techniques. In last few chapters, we will be looking into various algorithmic techniques. Such as, Brute-Force algorithms, Greedy algorithms, Divide and Conquer algorithms, Dynamic Programming, Reduction and Backtracking. . Table of Contents Chapter 0: How to use this book. Chapter 1: Algorithms Analysis Chapter 2: Approach to solve algorithm design problems Chapter 3: Abstract Data Type & C# Collections Chapter 4: Searching Chapter 5: Sorting Chapter 6: Linked List Chapter 7: Stack Chapter 8: Queue Chapter 9: Tree Chapter 10: Priority Queue Chapter 11: Hash-Table Chapter 12: Graphs Chapter 13: String Algorithms Chapter 14: Algorithm Design Techniques Chapter 15: Brute Force Algorithm Chapter 16: Greedy Algorithm Chapter 17: Divide & Conquer Chapter 18: Dynamic Programming Chapter 19: Backtracking Chapter 20: Complexity Theory |
| data science python coding interview questions: Data Science Interviews Exposed Jane You, Yanping Huang, Iris Wang, Feng Cao (Computer scientist), Ian Gao, 2015 The era has come when data science is changing the world and everyone's life. Data Science Interviews Exposed is the first book in the industry that covers everything you need to know to prepare for a data science career: from job market overview to job roles description, from resume preparation to soft skill development, and most importantly, the real interview questions and detailed answers. We hope this book can help the candidates in the data science job market, as well as those who need guidance to begin a data science career.--Back cover. |
| data science python coding interview questions: Deep Learning Interviews Shlomo Kashani, 2020-12-09 The book's contents is a large inventory of numerous topics relevant to DL job interviews and graduate level exams. That places this work at the forefront of the growing trend in science to teach a core set of practical mathematical and computational skills. It is widely accepted that the training of every computer scientist must include the fundamental theorems of ML, and AI appears in the curriculum of nearly every university. This volume is designed as an excellent reference for graduates of such programs. |
| data science python coding interview questions: Machine Learning Paul Wilmott, 2019-05-20 Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the following topics - K Nearest Neighbours; K Means Clustering; Naïve Bayes Classifier; Regression Methods; Support Vector Machines; Self-Organizing Maps; Decision Trees; Neural Networks; Reinforcement Learning |
| data science python coding interview questions: Algorithms Robert Sedgewick, Kevin Wayne, 2014-02-01 This book is Part I of the fourth edition of Robert Sedgewick and Kevin Wayne’s Algorithms, the leading textbook on algorithms today, widely used in colleges and universities worldwide. Part I contains Chapters 1 through 3 of the book. The fourth edition of Algorithms surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use. The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts. The companion web site, algs4.cs.princeton.edu contains An online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material The MOOC related to this book is accessible via the Online Course link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants. Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience. |
| data science python coding interview questions: Cracking the PM Interview Gayle Laakmann McDowell, Jackie Bavaro, 2013 How many pizzas are delivered in Manhattan? How do you design an alarm clock for the blind? What is your favorite piece of software and why? How would you launch a video rental service in India? This book will teach you how to answer these questions and more. Cracking the PM Interview is a comprehensive book about landing a product management role in a startup or bigger tech company. Learn how the ambiguously-named PM (product manager / program manager) role varies across companies, what experience you need, how to make your existing experience translate, what a great PM resume and cover letter look like, and finally, how to master the interview: estimation questions, behavioral questions, case questions, product questions, technical questions, and the super important pitch. |
| data science python coding interview questions: Grokking the System Design Interview Design Gurus, 2021-12-18 This book (also available online at www.designgurus.org) by Design Gurus has helped 60k+ readers to crack their system design interview (SDI). System design questions have become a standard part of the software engineering interview process. These interviews determine your ability to work with complex systems and the position and salary you will be offered by the interviewing company. Unfortunately, SDI is difficult for most engineers, partly because they lack experience developing large-scale systems and partly because SDIs are unstructured in nature. Even engineers who've some experience building such systems aren't comfortable with these interviews, mainly due to the open-ended nature of design problems that don't have a standard answer. This book is a comprehensive guide to master SDIs. It was created by hiring managers who have worked for Google, Facebook, Microsoft, and Amazon. The book contains a carefully chosen set of questions that have been repeatedly asked at top companies. What's inside? This book is divided into two parts. The first part includes a step-by-step guide on how to answer a system design question in an interview, followed by famous system design case studies. The second part of the book includes a glossary of system design concepts. Table of Contents First Part: System Design Interviews: A step-by-step guide. Designing a URL Shortening service like TinyURL. Designing Pastebin. Designing Instagram. Designing Dropbox. Designing Facebook Messenger. Designing Twitter. Designing YouTube or Netflix. Designing Typeahead Suggestion. Designing an API Rate Limiter. Designing Twitter Search. Designing a Web Crawler. Designing Facebook's Newsfeed. Designing Yelp or Nearby Friends. Designing Uber backend. Designing Ticketmaster. Second Part: Key Characteristics of Distributed Systems. Load Balancing. Caching. Data Partitioning. Indexes. Proxies. Redundancy and Replication. SQL vs. NoSQL. CAP Theorem. PACELC Theorem. Consistent Hashing. Long-Polling vs. WebSockets vs. Server-Sent Events. Bloom Filters. Quorum. Leader and Follower. Heartbeat. Checksum. About the Authors Designed Gurus is a platform that offers online courses to help software engineers prepare for coding and system design interviews. Learn more about our courses at www.designgurus.org. |
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 …
