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| amazon data science interview: 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. |
| amazon data science interview: 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. |
| amazon data science interview: Cracking the Data Science Interview Leondra R. Gonzalez, Aaren Stubberfield, 2024-02-29 Rise above the competition and excel in your next interview with this one-stop guide to Python, SQL, version control, statistics, machine learning, and much more Key Features Acquire highly sought-after skills of the trade, including Python, SQL, statistics, and machine learning Gain the confidence to explain complex statistical, machine learning, and deep learning theory Extend your expertise beyond model development with version control, shell scripting, and model deployment fundamentals Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company. Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you’ll find tips on job hunting, resume writing, and creating a top-notch portfolio. You’ll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview. By the end of this interview guide, you’ll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job.What you will learn Explore data science trends, job demands, and potential career paths Secure interviews with industry-standard resume and portfolio tips Practice data manipulation with Python and SQL Learn about supervised and unsupervised machine learning models Master deep learning components such as backpropagation and activation functions Enhance your productivity by implementing code versioning through Git Streamline workflows using shell scripting for increased efficiency Who this book is for Whether you're a seasoned professional who needs to brush up on technical skills or a beginner looking to enter the dynamic data science industry, this book is for you. To get the most out of this book, basic knowledge of Python, SQL, and statistics is necessary. However, anyone familiar with other analytical languages, such as R, will also find value in this resource as it helps you revisit critical data science concepts like SQL, Git, statistics, and deep learning, guiding you to crack through data science interviews. |
| amazon data science interview: Ace the Data Science Interview Kevin Huo, Nick Singh, 2021 |
| amazon data science interview: 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 |
| amazon data science interview: 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. |
| amazon data science interview: Machine Learning Interviews Susan Shu Chang, 2023-11-29 As tech products become more prevalent today, the demand for machine learning professionals continues to grow. But the responsibilities and skill sets required of ML professionals still vary drastically from company to company, making the interview process difficult to predict. In this guide, data science leader Susan Shu Chang shows you how to tackle the ML hiring process. Having served as principal data scientist in several companies, Chang has considerable experience as both ML interviewer and interviewee. She'll take you through the highly selective recruitment process by sharing hard-won lessons she learned along the way. You'll quickly understand how to successfully navigate your way through typical ML interviews. This guide shows you how to: Explore various machine learning roles, including ML engineer, applied scientist, data scientist, and other positions Assess your interests and skills before deciding which ML role(s) to pursue Evaluate your current skills and close any gaps that may prevent you from succeeding in the interview process Acquire the skill set necessary for each machine learning role Ace ML interview topics, including coding assessments, statistics and machine learning theory, and behavioral questions Prepare for interviews in statistics and machine learning theory by studying common interview questions |
| amazon data science interview: Getting Started with Data Science Murtaza Haider, 2015-12-14 Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon. |
| amazon data science interview: Big Data Analytics Arun K. Somani, Ganesh Chandra Deka, 2017-10-30 The proposed book will discuss various aspects of big data Analytics. It will deliberate upon the tools, technology, applications, use cases and research directions in the field. Chapters would be contributed by researchers, scientist and practitioners from various reputed universities and organizations for the benefit of readers. |
| amazon data science interview: The 9 Pitfalls of Data Science Gary Smith, Jay Cordes, 2019-07-08 Data science has never had more influence on the world. Large companies are now seeing the benefit of employing data scientists to interpret the vast amounts of data that now exists. However, the field is so new and is evolving so rapidly that the analysis produced can be haphazard at best. The 9 Pitfalls of Data Science shows us real-world examples of what can go wrong. Written to be an entertaining read, this invaluable guide investigates the all too common mistakes of data scientists - who can be plagued by lazy thinking, whims, hunches, and prejudices - and indicates how they have been at the root of many disasters, including the Great Recession. Gary Smith and Jay Cordes emphasise how scientific rigor and critical thinking skills are indispensable in this age of Big Data, as machines often find meaningless patterns that can lead to dangerous false conclusions. The 9 Pitfalls of Data Science is loaded with entertaining tales of both successful and misguided approaches to interpreting data, both grand successes and epic failures. These cautionary tales will not only help data scientists be more effective, but also help the public distinguish between good and bad data science. |
| amazon data science interview: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-06 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 |
| amazon data science interview: The AI Marketing Canvas Raj Venkatesan, Jim Lecinski, 2021-05-18 This book offers a direct, actionable plan CMOs can use to map out initiatives that are properly sequenced and designed for success—regardless of where their marketing organization is in the process. The authors pose the following critical questions to marketers: (1) How should modern marketers be thinking about artificial intelligence and machine learning? and (2) How should marketers be developing a strategy and plan to implement AI into their marketing toolkit? The opening chapters provide marketing leaders with an overview of what exactly AI is and how is it different than traditional computer science approaches. Venkatesan and Lecinski, then, propose a best-practice, five-stage framework for implementing what they term the AI Marketing Canvas. Their approach is based on research and interviews they conducted with leading marketers, and offers many tangible examples of what brands are doing at each stage of the AI Marketing Canvas. By way of guidance, Venkatesan and Lecinski provide examples of brands—including Google, Lyft, Ancestry.com, and Coca-Cola—that have successfully woven AI into their marketing strategies. The book concludes with a discussion of important implications for marketing leaders—for your team and culture. |
| amazon data science interview: Data Science for Business Professionals Probyto Data Science and Consulting Pvt. Ltd., 2020-05-06 Primer into the multidisciplinary world of Data Science KEY FEATURESÊÊ - Explore and use the key concepts of Statistics required to solve data science problems - Use Docker, Jenkins, and Git for Continuous Development and Continuous Integration of your web app - Learn how to build Data Science solutions with GCP and AWS DESCRIPTIONÊ The book will initially explain the What-Why of Data Science and the process of solving a Data Science problem. The fundamental concepts of Data Science, such as Statistics, Machine Learning, Business Intelligence, Data pipeline, and Cloud Computing, will also be discussed. All the topics will be explained with an example problem and will show how the industry approaches to solve such a problem. The book will pose questions to the learners to solve the problems and build the problem-solving aptitude and effectively learn. The book uses Mathematics wherever necessary and will show you how it is implemented using Python with the help of an example dataset.Ê WHAT WILL YOU LEARNÊÊ - Understand the multi-disciplinary nature of Data Science - Get familiar with the key concepts in Mathematics and Statistics - Explore a few key ML algorithms and their use cases - Learn how to implement the basics of Data Pipelines - Get an overview of Cloud Computing & DevOps - Learn how to create visualizations using Tableau WHO THIS BOOK IS FORÊ This book is ideal for Data Science enthusiasts who want to explore various aspects of Data Science. Useful for Academicians, Business owners, and Researchers for a quick reference on industrial practices in Data Science.Ê TABLE OF CONTENTS 1. Data Science in Practice 2. Mathematics Essentials 3. Statistics Essentials 4. Exploratory Data Analysis 5. Data preprocessing 6. Feature Engineering 7. Machine learning algorithms 8. Productionizing ML models 9. Data Flows in Enterprises 10. Introduction to Databases 11. Introduction to Big Data 12. DevOps for Data Science 13. Introduction to Cloud Computing 14. Deploy Model to Cloud 15. Introduction to Business IntelligenceÊ 16. Data Visualization Tools 17. Industry Use Case 1 Ð FormAssist 18. Industry Use Case 2 Ð PeopleReporter 19. Data Science Learning Resources 20. Do It Your Self Challenges 21. MCQs for Assessments |
| amazon data science interview: Cracking the Data Science Interview LEONDRA R.. STUBBERFIELD GONZALEZ (AAREN.), Aaren Stubberfield, 2024-02-29 Approach data science interviews with confidence with this one-stop guide focused on Python programming, SQL, shell/bash, version control, statistics, and ML modeling. |
| amazon data science interview: Disruptive Analytics Thomas W. Dinsmore, 2016-08-27 Learn all you need to know about seven key innovations disrupting business analytics today. These innovations—the open source business model, cloud analytics, the Hadoop ecosystem, Spark and in-memory analytics, streaming analytics, Deep Learning, and self-service analytics—are radically changing how businesses use data for competitive advantage. Taken together, they are disrupting the business analytics value chain, creating new opportunities. Enterprises who seize the opportunity will thrive and prosper, while others struggle and decline: disrupt or be disrupted. Disruptive Business Analytics provides strategies to profit from disruption. It shows you how to organize for insight, build and provision an open source stack, how to practice lean data warehousing, and how to assimilate disruptive innovations into an organization. Through a short history of business analytics and a detailed survey of products and services, analytics authority Thomas W. Dinsmore provides a practical explanation of the most compelling innovations available today. What You'll Learn Discover how the open source business model works and how to make it work for you See how cloud computing completely changes the economics of analytics Harness the power of Hadoop and its ecosystem Find out why Apache Spark is everywhere Discover the potential of streaming and real-time analytics Learn what Deep Learning can do and why it matters See how self-service analytics can change the way organizations do business Who This Book Is For Corporate actors at all levels of responsibility for analytics: analysts, CIOs, CTOs, strategic decision makers, managers, systems architects, technical marketers, product developers, IT personnel, and consultants. |
| amazon data science interview: DataBricks® PySpark 2.x Certification Practice Questions , This book contains the questions answers and some FAQ about the Databricks Spark Certification for version 2.x, which is the latest release from Apache Spark. In this book we will be having in total 75 practice questions. Almost all required question would have in detail explanation to the questions and answers, wherever required. Don’t consider this book as a guide, it is more of question and answer practice book. This book also give some references as well like how to prepare further to ensure that you clear the certification exam. This book will particularly focus on the Python version of the certification preparation material. Please note these are practice questions and not dumps, hence just memorizing the question and answers will not help in the real exam. You need to understand the concepts in detail as well as you should be able to solve the programming questions at the end in real worlds work you should be able to write code using PySpark whether you are Data Engineer, Data Analytics Engineer, Data Scientists or Programmer. Hence, take the opportunity to learn each question and also go through the explanation of the questions. |
| amazon data science interview: Apache Cassandra Certification Practice Material : 2019 , About Professional Certification of Apache Cassandra: Apache Cassandra is one of the most popular NoSQL Database currently being used by many of the organization, globally in every industry like Aviation, Finance, Retail, Social Networking etc. It proves that there is quite a huge demand for certified Cassandra professionals. Having certification make your selection in the company make much easier. This certification is conducted by the DataStax®, which has the Enterprise Version of the Apache Cassandra and Leader in providing support for the open source Apache Cassandra NoSQL database. Cassandra is one of the Unique NoSQL Database. So go for its certification, it will certainly help in - Getting the Job - Increase in your salary - Growth in your career. - Managing Tera Bytes of Data. - Learning Distributed Database - Using CQL (Cassandra Query Language) Cassandra Certification Information: - Number of questions: 60 Multiple Choice - Time allowed in minutes: 90 - Required passing score: 75% - Languages: English Exam Objectives: There are in total 5 sections and you will be asked total 60 questions in real exam. Please check each section below with regards to the exam objective 1. Apache Cassandra™ data modeling 2. Fundamentals of replication and consistency 3. The distributed and internal architecture of Apache Cassandra™ 4. Installation and configuration 5. Basic tooling |
| amazon data science interview: Data Mining and Exploration Chong Ho Alex Yu, 2022-10-27 This book introduces both conceptual and procedural aspects of cutting-edge data science methods, such as dynamic data visualization, artificial neural networks, ensemble methods, and text mining. There are at least two unique elements that can set the book apart from its rivals. First, most students in social sciences, engineering, and business took at least one class in introductory statistics before learning data science. However, usually these courses do not discuss the similarities and differences between traditional statistics and modern data science; as a result learners are disoriented by this seemingly drastic paradigm shift. In reaction, some traditionalists reject data science altogether while some beginning data analysts employ data mining tools as a “black box”, without a comprehensive view of the foundational differences between traditional and modern methods (e.g., dichotomous thinking vs. pattern recognition, confirmation vs. exploration, single method vs. triangulation, single sample vs. cross-validation etc.). This book delineates the transition between classical methods and data science (e.g. from p value to Log Worth, from resampling to ensemble methods, from content analysis to text mining etc.). Second, this book aims to widen the learner's horizon by covering a plethora of software tools. When a technician has a hammer, every problem seems to be a nail. By the same token, many textbooks focus on a single software package only, and consequently the learner tends to fit the problem with the tool, but not the other way around. To rectify the situation, a competent analyst should be equipped with a tool set, rather than a single tool. For example, when the analyst works with crucial data in a highly regulated industry, such as pharmaceutical and banking, commercial software modules (e.g., SAS) are indispensable. For a mid-size and small company, open-source packages such as Python would come in handy. If the research goal is to create an executive summary quickly, the logical choice is rapid model comparison. If the analyst would like to explore the data by asking what-if questions, then dynamic graphing in JMP Pro is a better option. This book uses concrete examples to explain the pros and cons of various software applications. |
| amazon data science interview: The Know-It-Alls Noam Cohen, 2017-11-07 Included in Backchannel’s (WIRED.com) “Top Tech Books of 2017” An “important” book on the “pervasive influence of Silicon Valley on our economy, culture and politics.” —New York Times How the titans of tech's embrace of economic disruption and a rampant libertarian ideology is fracturing America and making it a meaner place In The Know-It-Alls former New York Times technology columnist Noam Cohen chronicles the rise of Silicon Valley as a political and intellectual force in American life. Beginning nearly a century ago and showcasing the role of Stanford University as the incubator of this new class of super geeks, Cohen shows how smart guys like Jeff Bezos, Peter Thiel, Sergey Brin, Larry Page, and Mark Zuckerberg fell in love with a radically individualistic ideal and then mainstreamed it. With these very rich men leading the way, unions, libraries, public schools, common courtesy, and even government itself have been pushed aside to make way for supposedly efficient market-based encounters via the Internet. Donald Trump’s election victory was an inadvertent triumph of the disruption that Silicon Valley has been pushing: Facebook and Twitter, eager to entertain their users, turned a blind eye to the fake news and the hateful ideas proliferating there. The Rust Belt states that shifted to Trump are the ones being left behind by a meritocratic Silicon Valley ideology that promotes an economy where, in the words of LinkedIn founder Reid Hoffman, each of us is our own start-up. A society that belittles civility, empathy, and collaboration can easily be led astray. The Know-It-Alls explains how these self-proclaimed geniuses failed this most important test of democracy. |
| amazon data science interview: Profound Stories: A Companion to Deming's Journey to Profound Knowledge John Willis, Derek Lewis, 2024-07-16 In this captivating companion to Deming's Journey to Profound Knowledge, authors John Willis and Derek Lewis share the untold stories and fascinating details that didn't make it into the original book. Profound Stories takes readers on a deeper dive into the life and times of W. Edwards Deming, offering rare insights and anecdotes that further illuminate the legendary figure's journey to developing his influential System of Profound Knowledge. From Deming's humble origins to his wartime efforts and his pivotal role in Japan's post-war economic miracle, Willis and Lewis leave no stone unturned. Readers will discover the intriguing history behind key concepts like the PDSA Cycle and the Red Bead Game, as well as Deming's connections to other notable figures like Claude Shannon and Vannevar Bush. Profound Stories and Deming's Journey to Profound Knowledge explores the far-reaching impact of Deming's ideas, from the US Census to the American automotive industry to NASA's Apollo program. Willis and Lewis masterfully weave together historical context and personal accounts, creating a rich tapestry that brings Deming's story to life in vivid detail. Whether you're a devoted Deming follower or simply curious about the man behind the philosophy, this engaging and enlightening collection of stories offers a fresh perspective on Deming's life and legacy, revealing the profound impact of his ideas on the world we live in today. |
| amazon data science interview: Hack Recruiting Victor Assad, 2019-07-23 Praise for Hack Recruiting It is a brilliant piece of work. A must-read for those of us in global corporations, or companies of any size really, that seek to act NOW. --Julia Martensen, Head of HR Strategy and Innovation at DB Schenker. Victor Assad uncovers longstanding empirical research from I/O psychologists on how to best match job candidates to jobs and the best of today's digital technology. He sees a world (that is emerging today) in which AI ontologies (which are identifying information and relationships about today's global and diverse workforces) will make significant improvements for matching candidates to jobs while reducing recruiting cycle times, costs and selection biases. Victor points out that HR now has the digital tools it needs to dramatically transform recruiting and the role of the recruiter. HR can now build strategic talent pools, improve the employee experience, and digitally collect insightful analytics that will open up a new era of understanding on what truly drives employee performance and innovation. --Angela Hood, Founder and CEO of ThisWay Global. Must read book if you are a recruiter or talent acquisition head. It goes over best practices and hacks each step of recruiting. --Sandeep Purwar, Founder/CEO, Bevov |
| amazon data science interview: 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. |
| amazon data science interview: The Reliable Field Guide To UFO Science, Media And Data Sources Stephen J. Dirac, 2022-07-29 What makes this UFO book different? The Reliable Field Guide to UFO Science, Media and Data Sources contains an incredible amount of research and source material, including: • What Proof Is Out there? • The Various Hypotheses and Phenomena • Relevant People, Science Experts, Programs and Projects • Research Organizations, Archives, Databases and Government Reports • 20th To Early 21st Century Researchers, Authors and Documentarians Remember, if you have been searching for an organized and holistic collection of data on this fascinating and divisive subject, The Reliable Field Guide to UFO Science, Media and Data Sources is the book you’ve been searching for. Not another UFO Book? This book is a complete and comprehensive 548 pages of solid resources and knowledge, not just on the subject of UFO’s but also a deep dive into the various branches and related concepts such as the Starseed Hypothesis, the Sasquatch/Bigfoot Phenomenon, the Crop Circle Hypothesis, the Men In Black Hypothesis and many more. Is the TRUTH really out there? Exceptional claims require exceptional proofs however and the concept of Unidentified Flying Objects is no longer purely in the realms of science fiction/fantasy. Recently, with the latest improvements in image capturing and analytical technology and the proliferation of media and data sources we have acquired fantastic amounts of knowledge about the universe but still do not know how much more there is to be discovered. As J B S Haldane once said: 'The universe is not only stranger than we imagine, it is stranger than we can imagine.' It is only natural that an intelligent and inquisitive mind, fascinated by anomalous experiences, should eventually turn its attention to the UFO mystery. Whatever your position on UFO’s, from total believer to a complete skeptic, it’s always better to arm yourself with the most up-to-date information on what we currently know, what we think we know and the people and personalities behind the theories and explanations of the various phenomena. The Reliable Field Guide to UFO Science, Media and Data Sources recognizes that the concept of “UFO” must also incorporate the possibilities of a wider spectrum of Unidentified Anomalous Phenomena/UAP and explores these concepts and ideas thoroughly. This book takes a wide, holistic view of the subject and recognizes that the concept of “UFO” must also incorporate the possibilities of a wider spectrum of Unidentified Anomalous Phenomena/UAP.. USO, Unidentified Submerged Phenomena - Psychic Phenomena - Paranormal - Survival of Consciousness after death - Sasquatch, Bigfoot - Government Black Programs, Conspiracies, USAP/Unacknowledged(waived) Special Access Programs - Breakaway civilization - Time Travel - Unknown Secret Histories of Humankind - Roswell and UFO Crash Retrievals - Government Cover-ups and Disinformation Programs - Remote Viewing - Ancient Cultures - UFO/UAP Hypotheses |
| amazon data science interview: Becoming a Data Head Alex J. Gutman, Jordan Goldmeier, 2021-04-13 Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful. Thomas H. Davenport, Research Fellow, Author of Competing on Analytics, Big Data @ Work, and The AI Advantage You’ve heard the hype around data—now get the facts. In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it. You’ll learn how to: Think statistically and understand the role variation plays in your life and decision making Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace Understand what’s really going on with machine learning, text analytics, deep learning, and artificial intelligence Avoid common pitfalls when working with and interpreting data Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head—an active participant in data science, statistics, and machine learning. Whether you’re a business professional, engineer, executive, or aspiring data scientist, this book is for you. |
| amazon data science interview: Data Science for Librarians Yunfei Du, Hammad Rauf Khan, 2020-03-26 This unique textbook intersects traditional library science with data science principles that readers will find useful in implementing or improving data services within their libraries. Data Science for Librarians introduces data science to students and practitioners in library services. Writing for academic, public, and school library managers; library science students; and library and information science educators, authors Yunfei Du and Hammad Rauf Khan provide a thorough overview of conceptual and practical tools for data librarian practice. Partially due to how quickly data science evolves, libraries have yet to recognize core competencies and skills required to perform the job duties of a data librarian. As society transitions from the information age into the era of big data, librarians and information professionals require new knowledge and skills to stay current and take on new job roles, such as data librarianship. Such skills as data curation, research data management, statistical analysis, business analytics, visualization, smart city data, and learning analytics are relevant in library services today and will become increasingly so in the near future. This text serves as a tool for library and information science students and educators working on data science curriculum design. |
| amazon data science interview: Data Science and Analytics (with Python, R and SPSS Programming) V.K. Jain, The Book has been written completely as per AICTE recommended syllabus on Data Sciences. SALIENT FEATURES OF THE BOOK: Explains how data is collected, managed and stored for data science. With complete courseware for understand the key concepts in data science including their real-world applications and the toolkit used by data scientists. Implement data collection and management. Provided with state of the arts subjectwise. With all required tutorials on R, Python and Bokeh, Anaconda, IBM SPSS-21 and Matplotlib. |
| amazon data science interview: Data Science Uncovering the Reality Pulkit Bansal, Kunal Kishore, Pankaj Gupta, Srijan Saket, Neeraj Kumar, 2020-04-15 Data Science has become a popular field of work today. However a good resource to understand applied Data Science is still missing. In Data Science Uncovering the Reality, a group of IITians unravel how Data Science is done in the industry. They have interviewed Data Science and technology leaders at top companies in India and presented their learnings here. This book will give you honest answers to questions such as: How to build a career in Data Science? How A.I. is used in the world’s most successful companies. How Data Science leaders actually work and the challenges they face. |
| amazon data science interview: Encyclopedia of Computer Science and Technology Phillip A. Laplante, 2017-10-02 With breadth and depth of coverage, the Encyclopedia of Computer Science and Technology, Second Edition has a multi-disciplinary scope, drawing together comprehensive coverage of the inter-related aspects of computer science and technology. The topics covered in this encyclopedia include: General and reference Hardware Computer systems organization Networks Software and its engineering Theory of computation Mathematics of computing Information systems Security and privacy Human-centered computing Computing methodologies Applied computing Professional issues Leading figures in the history of computer science The encyclopedia is structured according to the ACM Computing Classification System (CCS), first published in 1988 but subsequently revised in 2012. This classification system is the most comprehensive and is considered the de facto ontological framework for the computing field. The encyclopedia brings together the information and historical context that students, practicing professionals, researchers, and academicians need to have a strong and solid foundation in all aspects of computer science and technology. |
| amazon data science interview: The Data Science Design Manual Steven S. Skiena, 2017-07-01 This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com) |
| amazon data science interview: Developing Analytic Talent Vincent Granville, 2014-03-24 Learn what it takes to succeed in the the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates. |
| amazon data science interview: The AI Value Playbook Lisa Weaver-Lambert, 2024-08-30 Learn from real-world examples how leveraging AI, including machine learning and generative AI, is imperative for businesses to navigate risk, drive value, and gain a competitive advantage Key Features Understand machine learning and generative AI terminology, concepts, and the AI technology stack. Learn from diverse real-world case studies narrated by business leaders in their own voice. Apply a value-driven approach to AI applications across multiple business sectors. Book DescriptionBusiness leaders are challenged by the speed of AI innovation and how to navigate disruption and uncertainty. This book is a crucial resource for those who want to understand how to leverage AI to drive business value, drawn from the firsthand experience of those who have been implementing this technology successfully. The AI Value Playbook focuses on questions frequently posed by leaders and boards. How can businesses adapt to these emerging technologies? How can they start building and deploying AI as a strategic asset to drive efficiency? What risks or threats need to be considered? How quickly can value be created? This book is a response to those demands. In a series of in-depth and wide-ranging conversations with practitioners, from CEOs leading new generative AI-based companies to Data Scientists and CFOs working in more traditional companies. Our experts share their hard-earned wisdom, talking candidly about their successes and failures, and what excites them about the future. These interviews offer unique insights for business leaders to apply to their own organizations. The book distils a value-driven playbook for how AI can be put to work today.What you will learn Fundamentals of AI concepts and the tech stack How AI works with real-world practical applications How to integrate into your company's overall strategy How to incorporate generative AI in your processes How to drive value with sector-wide examples How to organize an AI-driven operating model How to use AI for competitive advantage The dos and don'ts of AI application Who this book is for The AI Value Playbook is aimed at supporting non-technical executives and board members to quickly formulate a perspective on how to integrate AI. This book addresses the gap in data and AI knowledge in leadership teams that have an appetite for nuanced, targeted and practical solutions. It includes which levers and processes to consider to future-proof their business. It speaks to an audience interested in understanding how AI can drive value for their organisations. |
| amazon data science interview: Fulfillment Alec MacGillis, 2021-03-16 A New York Times Book Review Editors' Choice A grounded and expansive examination of the American economic divide . . . It takes a skillful journalist to weave data and anecdotes together so effectively. —Carolyn Kellogg, Los Angeles Times An award-winning journalist investigates Amazon’s impact on the wealth and poverty of towns and cities across the United States. In 1937, the famed writer and activist Upton Sinclair published a novel bearing the subtitle A Story of Ford-America. He blasted the callousness of a company worth “a billion dollars” that underpaid its workers while forcing them to engage in repetitive and sometimes dangerous assembly line labor. Eighty-three years later, the market capitalization of Amazon.com has exceeded one trillion dollars, while the value of the Ford Motor Company hovers around thirty billion. We have, it seems, entered the age of one-click America—and as the coronavirus makes Americans more dependent on online shopping, its sway will only intensify. Alec MacGillis’s Fulfillment is not another inside account or exposé of our most conspicuously dominant company. Rather, it is a literary investigation of the America that falls within that company’s growing shadow. As MacGillis shows, Amazon’s sprawling network of delivery hubs, data centers, and corporate campuses epitomizes a land where winner and loser cities and regions are drifting steadily apart, the civic fabric is unraveling, and work has become increasingly rudimentary and isolated. Ranging across the country, MacGillis tells the stories of those who’ve thrived and struggled to thrive in this rapidly changing environment. In Seattle, high-paid workers in new office towers displace a historic black neighborhood. In suburban Virginia, homeowners try to protect their neighborhood from the environmental impact of a new data center. Meanwhile, in El Paso, small office supply firms seek to weather Amazon’s takeover of government procurement, and in Baltimore a warehouse supplants a fabled steel plant. Fulfillment also shows how Amazon has become a force in Washington, D.C., ushering readers through a revolving door for lobbyists and government contractors and into CEO Jeff Bezos’s lavish Kalorama mansion. With empathy and breadth, MacGillis demonstrates the hidden human costs of the other inequality—not the growing gap between rich and poor, but the gap between the country’s winning and losing regions. The result is an intimate account of contemporary capitalism: its drive to innovate, its dark, pitiless magic, its remaking of America with every click. |
| amazon data science interview: Ethnography in the Open Science and Digital Age: New Debates, Dilemmas, and Issues Colin Jerolmack, Alexandra Murphy , Victoria Reyes, 2024-06-19 In the current moment, ethnography is caught up in a number of debates that have led ethnographers to reflect on classic methodological and ethical dilemmas in new ways. The “replication crisis” had led to a movement for “open science” (e.g., registering hypotheses in advance; sharing codes and data), but it seems unclear that recommended best practices are appropriate to ethnography. It’s even up for debate whether ethnography is more of a social science or a genre. The fact that many ethnographies are widely read invites questions and criticisms from beyond the ivory tower–including our subjects–about the ethics of representation (e.g., who has license to write about whom) and the extent to which journalistic standards of data verification and transparency (e.g., fact checking, naming sources) should apply to qualitative research. Some ethnographers are calling for more open, critical discussions about the embodied dimensions of fieldwork, including not only emotions but also issues like sexual intimacy and harassment. There’s also a growing expectation that ethnographers empower our subjects to represent and analyze themselves. What’s more, as more of social life is lived online, it becomes increasingly unclear where the boundaries of the “field site” should be drawn and whether ethnographic conventions can be applied wholesale to the study of digital spaces. |
| amazon data science interview: Data Scientists at Work Sebastian Gutierrez, 2014-12-12 Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. Data scientist is the sexiest job in the 21st century, according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report. Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (André Karpištšenko, Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). The book features a stimulating foreword by Google's Director of Research, Peter Norvig. Each of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. Data Scientists at Work parts the curtain on the interviewees’ earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients. |
| amazon data science interview: Data Scientist Diploma (master's level) - City of London College of Economics - 6 months - 100% online / self-paced City of London College of Economics, Overview This diploma course covers all aspects you need to know to become a successful Data Scientist. Content - Getting Started with Data Science - Data Analytic Thinking - Business Problems and Data Science Solutions - Introduction to Predictive Modeling: From Correlation to Supervised Segmentation - Fitting a Model to Data - Overfitting and Its Avoidance - Similarity, Neighbors, and Clusters Decision Analytic Thinking I: What Is a Good Model? - Visualizing Model Performance - Evidence and Probabilities - Representing and Mining Text - Decision Analytic Thinking II: Toward Analytical Engineering - Other Data Science Tasks and Techniques - Data Science and Business Strategy - Machine Learning: Learning from Data with Your Machine. - And much more Duration 6 months Assessment The assessment will take place on the basis of one assignment at the end of the course. Tell us when you feel ready to take the exam and we’ll send you the assignment questions. Study material The study material will be provided in separate files by email / download link. |
| amazon data science interview: Modern Post Scott Arundale, Tashi Trieu, 2014-08-01 With the shift from film to digital, today’s filmmakers are empowered by an arsenal of powerful, creative options with which to tell their story. Modern Post examines and demystifies these tools and workflows and demonstrates how these decisions can empower your storytelling. Using non-technical language, authors Scott Arundale and Tashi Trieu guide you through everything you should consider before you start shooting. They begin with a look to past methodologies starting with traditional film techniques and how they impact current trends. Next they offer a look at the latest generation of digital camera and capture systems. The authors move on to cover: * Preproduction- what camera is best for telling your story and why, budgeting for post * Production- on-set data management, dailies, green screen, digital cinematography * Postproduction- RAW vs. compressed footage, editing, visual effects, color correction, sound and deliverables including DCP creation The book features cutting-edge discussion about the role of the digital imaging technician (DIT), how you can best use the Cloud, motion graphics, sound design, and much more. Case studies show you these solutions being applied in real-world situations, and the companion website features videos of techniques discussed in the book, as well as timely updates about technological changes in the landscape. www.focalpress.com/cw/arundale |
| amazon data science interview: Data Cartels Sarah Lamdan, 2022-11-08 In our digital world, data is power. Information hoarding businesses reign supreme, using intimidation, aggression, and force to maintain influence and control. Sarah Lamdan brings us into the unregulated underworld of these data cartels, demonstrating how the entities mining, commodifying, and selling our data and informational resources perpetuate social inequalities and threaten the democratic sharing of knowledge. Just a few companies dominate most of our critical informational resources. Often self-identifying as data analytics or business solutions operations, they supply the digital lifeblood that flows through the circulatory system of the internet. With their control over data, they can prevent the free flow of information, masterfully exploiting outdated information and privacy laws and curating online information in a way that amplifies digital racism and targets marginalized communities. They can also distribute private information to predatory entities. Alarmingly, everything they're doing is perfectly legal. In this book, Lamdan contends that privatization and tech exceptionalism have prevented us from creating effective legal regulation. This in turn has allowed oversized information oligopolies to coalesce. In addition to specific legal and market-based solutions, Lamdan calls for treating information like a public good and creating digital infrastructure that supports our democratic ideals. |
| amazon data science interview: R for Cloud Computing A Ohri, 2014-11-14 R for Cloud Computing looks at some of the tasks performed by business analysts on the desktop (PC era) and helps the user navigate the wealth of information in R and its 4000 packages as well as transition the same analytics using the cloud. With this information the reader can select both cloud vendors and the sometimes confusing cloud ecosystem as well as the R packages that can help process the analytical tasks with minimum effort, cost and maximum usefulness and customization. The use of Graphical User Interfaces (GUI) and Step by Step screenshot tutorials is emphasized in this book to lessen the famous learning curve in learning R and some of the needless confusion created in cloud computing that hinders its widespread adoption. This will help you kick-start analytics on the cloud including chapters on both cloud computing, R, common tasks performed in analytics including the current focus and scrutiny of Big Data Analytics, setting up and navigating cloud providers. Readers are exposed to a breadth of cloud computing choices and analytics topics without being buried in needless depth. The included references and links allow the reader to pursue business analytics on the cloud easily. It is aimed at practical analytics and is easy to transition from existing analytical set up to the cloud on an open source system based primarily on R. This book is aimed at industry practitioners with basic programming skills and students who want to enter analytics as a profession. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. It will also help researchers and academics but at a practical rather than conceptual level. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy. The cloud computing paradigm is firmly established as the next generation of computing from microprocessors to desktop PCs to cloud. |
| amazon data science interview: 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. |
| amazon data science interview: Assembling and Governing Habits Tony Bennett, Ben Dibley, Gay Hawkins, Greg Noble, 2021-07-22 The increasing significance of managing or changing habits is evident across a range of pressing contemporary issues: climate change, waste management, travel practices, and crowd control. Assembling and Governing Habits engages with the diverse ways in which habits are governed through the knowledge practices and technologies that have been brought to bear on them. The volume addresses three main concerns. The first focuses on how the habit discourses proposed by a range of disciplines have informed the ways in which different forms of expertise have shaped the ways in which habits have been managed or changed to bring about specific social objectives. The second concerns the ways in which habits are acted on as aspects of infrastructures which constitute the interfaces through which technical systems, human conducts and environments are acted on simultaneously. The third concerns the specific ways in which habit discourses and habit infrastructures are brought together in the regulation of ‘city habits’: that is, habits which have specific qualities arising out of the specific conditions – the rhythms and densities – of urban life and ones which, in the wake of the COVID-19 pandemic, have been profoundly disrupted. Written in a clear and direct style, the book will appeal to students and scholars with an interest in cultural studies, sociology, cultural geography, history of the sciences, and posthuman studies. |
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