Advertisement
| facebook data science intern: Ace the Data Science Interview Kevin Huo, Nick Singh, 2021 |
| facebook data science intern: 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. |
| facebook data science intern: Fifty Challenging Problems in Probability with Solutions Frederick Mosteller, 2012-04-26 Remarkable puzzlers, graded in difficulty, illustrate elementary and advanced aspects of probability. These problems were selected for originality, general interest, or because they demonstrate valuable techniques. Also includes detailed solutions. |
| facebook data science intern: 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. |
| facebook data science intern: Social Data Science Xennials Gian Marco Campagnolo, 2020-11-30 This book explores the tension between analogue and digital as part of an evolving research programme and focuses on the sequencing of methods within it. The book will be an invaluable reference for scholars who routinely engage in critical sociological analysis of the digital workplace and find it easier to treat the digital as an object of study. It describes how the transformations taking place in the 10-year arc of a career spent doing fieldwork in the IT sector led the author to progressively embrace new forms of data and methods. In a time where sociological imagination takes the shape of whatever new phenomenon can be studied by transactional data and machine learning methods, it is a reminder that longstanding engagement with a particular field of practice is the basis of empirical social science expertise. ‘This short book by Gian Marco Campagnolo is remarkably wide-ranging. It draws on theoretical perspectives as varied as Harold Garfinkel’s ethnomethodology and Andrew Abbott’s ‘linked ecologies’ to discuss topics as diverse as the adoption of packaged enterprise software in the public sector in Italy and the careers of often influential industry analysts. Campagnolo’s methods are primarily qualitative and ethnographic, but he shows a proper appreciation for quantitative methods such as text mining and sequence analysis. The book ends with a discussion of the famously difficult issue of achieving ‘explainability’ in machine learning. Campagnolo tantalisingly suggests the usefulness here of how ethnomethodologists view ‘accountability’: as a practical accomplishment that is hampered, rather than fostered, by efforts to give full explanations.’ —Donald MacKenzie, Professor of Sociology, Edinburgh University, Scotland ‘The author adopts a ‘processual’ perspective on social data science as means of exploring and reflecting on the emergence of an academic career within this new domain of interdisciplinary inquiry. This is certainly a novel and interesting approach given the fact that ‘data science’ is work in progress and is characterized by a number of competing occupational groups that are struggling to define this emerging field.’ —William Housley, Professor, University of Cardiff, UK ‘Having myself written about the relationships between ethnography and computer science, I see this book as a timely contribution in that it extends the existing debate to data science. Data science is an emerging discipline that is gaining central stage in industry and in the public discourse. The aim of this book to indicate the importance of interdisciplinarity in this field is commendable.’ —Giolo Fele, Professor, University of Trento, Italy 'This book provides two entwined accounts: a reflective personal journey across different projects and methods and a grounded, genealogically sound analysis of the approaches and contributions of social science to understanding the digital society. These dual accounts are adroitly communicated. Their bold combination yields a unique and invaluable contribution to fundamental discussions in the social sciences, as well as an exemplar for how to combine ethnographic and data-driven analysis in a theoretically and epistemologically informed manner. With this book, Campagnolo brings us close to the methods and opens up an inspiring and challenging agenda for combining old and new forms of inquiry into sociological problems.' —Anne Beaulieu, Director Data Research Centre, University of Groningen, Netherlands |
| facebook data science intern: Understanding the Digital World Brian W. Kernighan, 2021-03-30 A brand-new edition of the popular introductory textbook that explores how computer hardware, software, and networks work Computers are everywhere. Some are highly visible, in laptops, tablets, cell phones, and smart watches. But most are invisible, like those in appliances, cars, medical equipment, transportation systems, power grids, and weapons. We never see the myriad computers that quietly collect, share, and sometimes leak personal data about us. Governments and companies increasingly use computers to monitor what we do. Social networks and advertisers know more about us than we should be comfortable with. Criminals have all-too-easy access to our data. Do we truly understand the power of computers in our world? In this updated edition of Understanding the Digital World, Brian Kernighan explains how computer hardware, software, and networks work. Topics include how computers are built and how they compute; what programming is; how the Internet and web operate; and how all of these affect security, privacy, property, and other important social, political, and economic issues. Kernighan touches on fundamental ideas from computer science and some of the inherent limitations of computers, and new sections in the book explore Python programming, big data, machine learning, and much more. Numerous color illustrations, notes on sources for further exploration, and a glossary explaining technical terms and buzzwords are included. Understanding the Digital World is a must-read for readers of all backgrounds who want to know more about computers and communications. |
| facebook data science intern: Ask a Manager Alison Green, 2018-05-01 From the creator of the popular website Ask a Manager and New York’s work-advice columnist comes a witty, practical guide to 200 difficult professional conversations—featuring all-new advice! There’s a reason Alison Green has been called “the Dear Abby of the work world.” Ten years as a workplace-advice columnist have taught her that people avoid awkward conversations in the office because they simply don’t know what to say. Thankfully, Green does—and in this incredibly helpful book, she tackles the tough discussions you may need to have during your career. You’ll learn what to say when • coworkers push their work on you—then take credit for it • you accidentally trash-talk someone in an email then hit “reply all” • you’re being micromanaged—or not being managed at all • you catch a colleague in a lie • your boss seems unhappy with your work • your cubemate’s loud speakerphone is making you homicidal • you got drunk at the holiday party Praise for Ask a Manager “A must-read for anyone who works . . . [Alison Green’s] advice boils down to the idea that you should be professional (even when others are not) and that communicating in a straightforward manner with candor and kindness will get you far, no matter where you work.”—Booklist (starred review) “The author’s friendly, warm, no-nonsense writing is a pleasure to read, and her advice can be widely applied to relationships in all areas of readers’ lives. Ideal for anyone new to the job market or new to management, or anyone hoping to improve their work experience.”—Library Journal (starred review) “I am a huge fan of Alison Green’s Ask a Manager column. This book is even better. It teaches us how to deal with many of the most vexing big and little problems in our workplaces—and to do so with grace, confidence, and a sense of humor.”—Robert Sutton, Stanford professor and author of The No Asshole Rule and The Asshole Survival Guide “Ask a Manager is the ultimate playbook for navigating the traditional workforce in a diplomatic but firm way.”—Erin Lowry, author of Broke Millennial: Stop Scraping By and Get Your Financial Life Together |
| facebook data science intern: Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions Matt Taddy, 2019-08-23 Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling Understand how use ML tools in real world business problems, where causation matters more that correlation Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science. |
| facebook data science intern: Roundtable on Data Science Postsecondary Education National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Division on Engineering and Physical Sciences, Board on Science Education, Computer Science and Telecommunications Board, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, 2020-10-02 Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting. |
| facebook data science intern: The Facebook Effect David Kirkpatrick, 2011-02 Kirkpatrick tells us how Facebook was created, why it has flourished, and where it is going next. He chronicles its successes and missteps. |
| facebook data science intern: Advice To A Young Scientist P. B. Medawar, 2008-08-01 To those interested in a life in science, Sir Peter Medawar, Nobel laureate, deflates the myths of invincibility, superiority, and genius; instead, he demonstrates it is common sense and an inquiring mind that are essential to the scientist's calling. He deflates the myths surrounding scientists -- invincibility, superiority, and genius; instead, he argues that it is common sense and an inquiring mind that are essential to the makeup of a scientist. He delivers many wry observations on how to choose a research topic, how to get along wih collaborators and older scientists and administrators, how (and how not) to present a scientific paper, and how to cope with culturally superior specialists in the arts and humanities. |
| facebook data science intern: Intern Talk Anthony Louis, 2020-09-15 From navigating interviews and crafting r sum s to effective networking and personal branding, Intern Talk is a career coach and adviser disguised as a book. It not only guides students in the pursuit of professional opportunities but also offers a somewhat novel approach to achieving a lifetime of career success. |
| facebook data science intern: Adventures In Financial Data Science: The Empirical Properties Of Financial And Economic Data (Second Edition) Graham L Giller, 2022-06-27 This book provides insights into the true nature of financial and economic data, and is a practical guide on how to analyze a variety of data sources. The focus of the book is on finance and economics, but it also illustrates the use of quantitative analysis and data science in many different areas. Lastly, the book includes practical information on how to store and process data and provides a framework for data driven reasoning about the world.The book begins with entertaining tales from Graham Giller's career in finance, starting with speculating in UK government bonds at the Oxford Post Office, accidentally creating a global instant messaging system that went 'viral' before anybody knew what that meant, on being the person who forgot to hit 'enter' to run a hundred-million dollar statistical arbitrage system, what he decoded from his brief time spent with Jim Simons, and giving Michael Bloomberg a tutorial on Granger Causality.The majority of the content is a narrative of analytic work done on financial, economics, and alternative data, structured around both Dr Giller's professional career and some of the things that just interested him. The goal is to stimulate interest in predictive methods, to give accurate characterizations of the true properties of financial, economic and alternative data, and to share what Richard Feynman described as 'The Pleasure of Finding Things Out.' |
| facebook data science intern: Pristine Seas Enric Sala, Leonardo DiCaprio, 2015 National Geographic Explorer-in-Residence Enric Sala takes readers on an unforgettable journey to 10 places where the ocean is virtually untouched by man, offering a fascinating glimpse into our past and an inspiring vision for the future. From the shark-rich waters surrounding Coco Island, Costa Rica, to the iceberg-studded sea off Franz Josef Land, Russia, this incredible photographic collection showcases the thriving marine ecosystems that Sala is working to protect. Offering a rare glimpse into the world's underwater Edens, more than 200 images take you to the frontier of the Pristine Seas expeditions, where Sala's teams explore the breathtaking wildlife and habitats from the depths to the surface--thriving ecosystems with healthy corals and a kaleidoscopic variety of colorful fish and stunning creatures that have been protected from human interference. With this dazzling array of photographs that capture the beauty of the water and the incredible wildlife within it, this book shows us the brilliance of the sea in its natural state.-- |
| facebook data science intern: Hacking the Electorate Eitan Hersh, 2015-06-09 Hacking the Electorate focuses on the consequences of campaigns using microtargeting databases to mobilize voters in elections. Eitan Hersh shows that most of what campaigns know about voters comes from a core set of public records, and the content of public records varies from state to state. This variation accounts for differences in campaign strategies and voter coalitions across the nation. |
| facebook data science intern: Outnumbered David Sumpter, 2018-04-19 'Fascinating' Financial Times Algorithms are running our society, and as the Cambridge Analytica story has revealed, we don't really know what they are up to. Our increasing reliance on technology and the internet has opened a window for mathematicians and data researchers to gaze through into our lives. Using the data they are constantly collecting about where we travel, where we shop, what we buy and what interests us, they can begin to predict our daily habits. But how reliable is this data? Without understanding what mathematics can and can't do, it is impossible to get a handle on how it is changing our lives. In this book, David Sumpter takes an algorithm-strewn journey to the dark side of mathematics. He investigates the equations that analyse us, influence us and will (maybe) become like us, answering questions such as: Who are Cambridge Analytica? And what are they doing with our data? How does Facebook build a 100-dimensional picture of your personality? Are Google algorithms racist and sexist? Why do election predictions fail so drastically? Are algorithms that are designed to find criminals making terrible mistakes? What does the future hold as we relinquish our decision-making to machines? Featuring interviews with those working at the cutting edge of algorithm research, including Alex Kogan from the Cambridge Analytica story, along with a healthy dose of mathematical self-experiment, Outnumbered will explain how mathematics and statistics work in the real world, and what we should and shouldn't worry about. A lot of people feel outnumbered by algorithms – don't be one of them. |
| facebook data science intern: Survey of Text Mining Michael W. Berry, 2013-03-14 Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text. |
| facebook data science intern: 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. |
| facebook data science intern: The Self-Taught Programmer Cory Althoff, 2022-01-13 |
| facebook data science intern: Ask, Measure, Learn Lutz Finger, Soumitra Dutta, 2014-01-23 You can measure practically anything in the age of social media, but if you don’t know what you’re looking for, collecting mountains of data won’t yield a grain of insight. This non-technical guide shows you how to extract significant business value from big data with Ask-Measure-Learn, a system that helps you ask the right questions, measure the right data, and then learn from the results. Authors Lutz Finger and Soumitra Dutta originally devised this system to help governments and NGOs sift through volumes of data. With this book, these two experts provide business managers and analysts with a high-level overview of the Ask-Measure-Learn system, and demonstrate specific ways to apply social media analytics to marketing, sales, public relations, and customer management, using examples and case studies. |
| facebook data science intern: The Great Cloud Migration Michael C. Daconta, 2013 - Learn how to migrate your applications to the cloud! - Learn how to overcome your senior management's concerns about Cloud Security and Interoperability! - Learn how to explain cloud computing, big data and linked data to your organization! - Learn how to develop a robust Cloud Implementation Strategy! - Learn how a Technical Cloud Broker can ease your migration to the cloud! This book will answer the key questions that every organization is asking about emerging technologies like Cloud Computing, Big Data and Linked Data. Written by a seasoned expert and author/co-author of 11 other technical books, this book deftly guides you with real-world experience, case studies, illustrative diagrams and in-depth analysis. * How do you migrate your software applications to the cloud? This book is your definitive guide to migrating applications to the cloud! It explains all the options, tradeoffs, challenges and obstacles to the migration. It provides a migration lifecycle and process you can follow to migrate each application. It provides in-depth case studies: an Infrastructure-as-a-Service case study and a Platform-as-a-Service case study. It covers the difference between application migration and data migration to the cloud and walks you through how to do both well. It covers migration to all the major cloud providers to include Amazon Web Services (AWS), Google AppEngine and Microsoft Azure. * How do you develop a sound implementation strategy for the migration to the cloud? This book leverages Mr. Daconta's 25 years of leadership experience, from the Military to Corporate Executive teams to the Office of the CIO in the Department of Homeland Security, to guide you through the development of a practical and sound implementation strategy. The book's Triple-A Strategy: Assessment, Architecture then Action is must reading for every project lead and IT manager! * This book covers twenty migration scenarios! Application and data migration to the cloud |
| facebook data science intern: Foolproof: Why Misinformation Infects Our Minds and How to Build Immunity Sander van der Linden, 2023-03-21 Winner of the SPSP Book Prize for the Promotion of Social and Personality Science • Winner of the 2024 APA William James Book Award • Winner of the 2024 Harvard Goldsmith Book Prize • Winner of the 2024 Nautilus Book Award • A Next Big Idea Club Must-Read • A Financial Times Best Book of the Year • One of Nature’s best science picks • One of Behavioral Scientist’s Notable Books of 2023 Informed by decades of research and on-the-ground experience advising governments and tech companies, Foolproof is the definitive guide to navigating the misinformation age. From fake news to conspiracy theories, from inflammatory memes to misleading headlines, misinformation has swiftly become the defining problem of our era. The crisis threatens the integrity of our democracies, our ability to cultivate trusting relationships, even our physical and psychological well-being—yet most attempts to combat it have proven insufficient. In Foolproof, one of the world’s leading experts on misinformation lays out a crucial new paradigm for understanding and defending ourselves against the worldwide infodemic. With remarkable clarity, Sander van der Linden explains why our brains are so vulnerable to misinformation, how it spreads across social networks, and what we can do to protect ourselves and others. Like a virus, misinformation infects our minds, exploiting shortcuts in how we see and process information to alter our beliefs, modify our memories, and replicate at astonishing rates. Once the virus takes hold, it’s very hard to cure. Strategies like fact-checking and debunking can leave a falsehood still festering or, at worst, even strengthen its hold. But we aren’t helpless. As van der Linden shows based on award-winning original research, we can cultivate immunity through the innovative science of “prebunking”: inoculating people against false information by preemptively exposing them to a weakened dose, thus empowering them to identify and fend off its manipulative tactics. Deconstructing the characteristic techniques of conspiracies and misinformation, van der Linden gives readers practical tools to defend themselves and others against nefarious persuasion—whether at scale or around their own dinner table. |
| facebook data science intern: The Signal and the Noise Nate Silver, 2015-02-03 One of the more momentous books of the decade. —The New York Times Book Review Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of the website FiveThirtyEight. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future. In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball to global pandemics, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good—or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science. Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise. With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read. |
| facebook data science intern: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track Yuxiao Dong, Nicolas Kourtellis, Barbara Hammer, Jose A. Lozano, 2021-09-09 The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media. |
| facebook data science intern: Facebook Nation Newton Lee, 2022-02-02 This book explores total information awareness empowered by social media. At the FBI Citizens Academy in February 2021, I asked the FBI about the January 6 Capitol riot organized on social media that led to the unprecedented ban of a sitting U.S. President by all major social networks. In March 2021, Facebook CEO Mark Zuckerberg, Google CEO Sundar Pichai, and Twitter CEO Jack Dorsey appeared before Congress to face criticism about their handling of misinformation and online extremism that culminated in the storming of Capitol Hill. With more than three billion monthly active users, Facebook family of apps is by far the world's largest social network. Facebook as a nation is bigger than the top three most populous countries in the world: China, India, and the United States. Social media has enabled its users to inform and misinform the public, to appease and disrupt Wall Street, to mitigate and exacerbate the COVID-19 pandemic, and to unite and divide a country. Mark Zuckerberg once said, We exist at the intersection of technology and social issues. He should have heeded his own words. In October 2021, former Facebook manager-turned-whistleblower Frances Haugen testified at the U.S. Senate that Facebook's products harm children, stoke division, and weaken our democracy. This book offers discourse and practical advice on information and misinformation, cybersecurity and privacy issues, cryptocurrency and business intelligence, social media marketing and caveats, e-government and e-activism, as well as the pros and cons of total information awareness including the Edward Snowden leaks. Highly recommended. - T. D. Richardson, Choice Magazine A great book for social media experts. - Will M., AdWeek Parents in particular would be well advised to make this book compulsory reading for their teenage children... - David B. Henderson, ACM Computing Reviews |
| facebook data science intern: Hadoop For Dummies Dirk deRoos, 2014-04-14 Let Hadoop For Dummies help harness the power of your data and rein in the information overload Big data has become big business, and companies and organizations of all sizes are struggling to find ways to retrieve valuable information from their massive data sets with becoming overwhelmed. Enter Hadoop and this easy-to-understand For Dummies guide. Hadoop For Dummies helps readers understand the value of big data, make a business case for using Hadoop, navigate the Hadoop ecosystem, and build and manage Hadoop applications and clusters. Explains the origins of Hadoop, its economic benefits, and its functionality and practical applications Helps you find your way around the Hadoop ecosystem, program MapReduce, utilize design patterns, and get your Hadoop cluster up and running quickly and easily Details how to use Hadoop applications for data mining, web analytics and personalization, large-scale text processing, data science, and problem-solving Shows you how to improve the value of your Hadoop cluster, maximize your investment in Hadoop, and avoid common pitfalls when building your Hadoop cluster From programmers challenged with building and maintaining affordable, scaleable data systems to administrators who must deal with huge volumes of information effectively and efficiently, this how-to has something to help you with Hadoop. |
| facebook data science intern: Big Data Ramón Reichert, 2014-09-30 Ob die Überwachungspraktiken der NSA oder die Geschäftsmodelle von Google, Facebook & Co.: Sie alle basieren auf »Big Data«, der ungeahnten Möglichkeit, riesige Datenmengen wie nie zuvor in der Geschichte zu erheben, zu sammeln und zu analysieren. »Big Data« beschreibt damit nicht nur neuartige wissenschaftliche Datenpraktiken, sondern steht für eine tektonische Verschiebung von Wissen, Medien, Macht und Ökonomie. Im Unterschied zum Medienhype um »Big Data« schafft der Band einen Reflexionsraum zur differenzierten Auseinandersetzung mit dem datenbasierten Medienumbruch der Gegenwart. International führende Theoretiker der Digital Humanities stellen einen fachübergreifenden Theorierahmen zur Verfügung, der es erlaubt, »Big Data« in seiner gesamten sozialen, kulturellen, ökonomischen und politischen Bandbreite zeitdiagnostisch zu thematisieren. Mit Beiträgen von David M. Berry, Jean Burgess, Alexander R. Galloway, Lev Manovich, Richard Rogers, Daniel Rosenberg, Bernard Stiegler, Theo Röhle, Eugene Thacker u.a.m. |
| facebook data science intern: Silicon Valley for Foreigners Reinaldo Normand, 2017-06-03 Written by a San Francisco based, foreign born entrepreneur, this book offers a unique perspective to decode the business etiquette and cultural traits of the Silicon Valley ecosystem. Recommended for entrepreneurs, executives, students and investors living outside the San Francisco Bay Area and interested in startups and innovation. |
| facebook data science intern: Beautiful Data Toby Segaran, Jeff Hammerbacher, 2009-07-14 In this insightful book, you'll learn from the best data practitioners in the field just how wide-ranging -- and beautiful -- working with data can be. Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video. With Beautiful Data, you will: Explore the opportunities and challenges involved in working with the vast number of datasets made available by the Web Learn how to visualize trends in urban crime, using maps and data mashups Discover the challenges of designing a data processing system that works within the constraints of space travel Learn how crowdsourcing and transparency have combined to advance the state of drug research Understand how new data can automatically trigger alerts when it matches or overlaps pre-existing data Learn about the massive infrastructure required to create, capture, and process DNA data That's only small sample of what you'll find in Beautiful Data. For anyone who handles data, this is a truly fascinating book. Contributors include: Nathan Yau Jonathan Follett and Matt Holm J.M. Hughes Raghu Ramakrishnan, Brian Cooper, and Utkarsh Srivastava Jeff Hammerbacher Jason Dykes and Jo Wood Jeff Jonas and Lisa Sokol Jud Valeski Alon Halevy and Jayant Madhavan Aaron Koblin with Valdean Klump Michal Migurski Jeff Heer Coco Krumme Peter Norvig Matt Wood and Ben Blackburne Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen Lukas Biewald and Brendan O'Connor Hadley Wickham, Deborah Swayne, and David Poole Andrew Gelman, Jonathan P. Kastellec, and Yair Ghitza Toby Segaran |
| facebook data science intern: 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. |
| facebook data science intern: Machine Learning with Python for Everyone Mark Fenner, 2019-07-30 The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use. Understand machine learning algorithms, models, and core machine learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of machine learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply machine learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python scikit-learn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| facebook data science intern: 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. |
| facebook data science intern: Surprising Quantum Bounces Valery Nesvizhevsky, Alexei Voronin, 2015-04-14 This unique book demonstrates the undivided unity and infinite diversity of quantum mechanics using a single phenomenon: quantum bounces of ultra-cold particles.Various examples of such 'quantum bounces' are: gravitational quantum states of ultra-cold neutrons (the first observed quantum states of matter in a gravitational field), the neutron whispering gallery (an observed matter-wave analog of the whispering gallery effect well known in acoustics and for electromagnetic waves), and gravitational and whispering gallery states for anti-matter atoms that remain to be observed.These quantum states are an invaluable tool in the search for additional fundamental short-range forces, for exploring the gravitational interaction and quantum effects of gravity, for probing physics beyond the standard model, and for furthering studies into the foundations of quantum mechanics, quantum optics, and surface science. |
| facebook data science intern: Data Science and Digital Business Fausto Pedro García Márquez, Benjamin Lev, 2019-01-04 This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business. |
| facebook data science intern: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| facebook data science intern: Data Mining and Predictive Analytics Daniel T. Larose, 2015-02-19 Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives. |
| facebook data science intern: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
| facebook data science intern: Communicating with Data Deborah Nolan, Sara Stoudt, 2021-03-25 Communication is a critical yet often overlooked part of data science. Communicating with Data aims to help students and researchers write about their insights in a way that is both compelling and faithful to the data. General advice on science writing is also provided, including how to distill findings into a story and organize and revise the story, and how to write clearly, concisely, and precisely. This is an excellent resource for students who want to learn how to write about scientific findings, and for instructors who are teaching a science course in communication or a course with a writing component. Communicating with Data consists of five parts. Part I helps the novice learn to write by reading the work of others. Part II delves into the specifics of how to describe data at a level appropriate for publication, create informative and effective visualizations, and communicate an analysis pipeline through well-written, reproducible code. Part III demonstrates how to reduce a data analysis to a compelling story and organize and write the first draft of a technical paper. Part IV addresses revision; this includes advice on writing about statistical findings in a clear and accurate way, general writing advice, and strategies for proof reading and revising. Part V offers advice about communication strategies beyond the page, which include giving talks, building a professional network, and participating in online communities. This book also provides 22 portfolio prompts that extend the guidance and examples in the earlier parts of the book and help writers build their portfolio of data communication. |
| facebook data science intern: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| facebook data science intern: Wisdom at Work Chip Conley, 2018-09-18 Experience is making a comeback. Learn how to repurpose your wisdom. At age 52, after selling the company he founded and ran as CEO for 24 years, rebel boutique hotelier Chip Conley was looking at an open horizon in midlife. Then he received a call from the young founders of Airbnb, asking him to help grow their disruptive start-up into a global hospitality giant. He had the industry experience, but Conley was lacking in the digital fluency of his 20-something colleagues. He didn't write code, or have an Uber or Lyft app on his phone, was twice the age of the average Airbnb employee, and would be reporting to a CEO young enough to be his son. Conley quickly discovered that while he'd been hired as a teacher and mentor, he was also in many ways a student and intern. What emerged is the secret to thriving as a mid-life worker: learning to marry wisdom and experience with curiosity, a beginner's mind, and a willingness to evolve, all hallmarks of the Modern Elder. In a world that venerates the new, bright, and shiny, many of us are left feeling invisible, undervalued, and threatened by the digital natives nipping at our heels. But Conley argues that experience is on the brink of a comeback. Because at a time when power is shifting younger, companies are finally waking up to the value of the humility, emotional intelligence, and wisdom that come with age. And while digital skills might have only the shelf life of the latest fad or gadget, the human skills that mid-career workers possess--like good judgment, specialized knowledge, and the ability to collaborate and coach - never expire. Part manifesto and part playbook, Wisdom@Work ignites an urgent conversation about ageism in the workplace, calling on us to treat age as we would other type of diversity. In the process, Conley liberates the term elder from the stigma of elderly, and inspires us to embrace wisdom as a path to growing whole, not old. Whether you've been forced to make a mid-career change, are choosing to work past retirement age, or are struggling to keep up with the millennials rising up the ranks, Wisdom@Work will help you write your next chapter. |
Facebook share link - can you customize the message body text?
Feb 17, 2011 · Facebook will not allow developers pre-fill messages. Developers may customize the story by providing OG meta tags, but it's up to the user to fill the message. This is only …
Facebook Login - Microsoft Community
5 days ago · I have just reinstalled the Facebook on my Laptop, Win10 and Edge with latest updates. First time to login worked fine. Next time I get a message this page isn't available link …
How to resolve Facebook Login is currently unavailable for this …
Jul 28, 2021 · In the facebook developers console for your app, go to App Review-> Permissions and Features. Set the public_profile and email to have advanced access. This will allow all …
Facebook login problem with Win 11 - Microsoft Community
Dec 20, 2021 · -Do a clean boot and try to log in as your username on Facebook, if the problem persists, when typing your username on Facebook, use the shortcut Windows+Ctrl+O to type …
Facebook On Microsoft Edge
Mar 6, 2021 · Why isn't Facebook working properly on Microsoft Edge? When I open it, I get my page with the latest post and no more. Won't let me click on anything to open. Apps, notices, …
How do I uninstall Facebook from my windows 11 computer
Oct 14, 2023 · To uninstall Facebook from your Windows 11 computer, you have a couple of options based on how you installed it. If you got it from the Microsoft Store as a widget, simply …
Solved: Unauthorized payment to Meta Platforms (Facebook i.
Nov 13, 2022 · The person said that the initial dispute resolution was "automated". Anyway, I am waiting a call-back or email as they are currently reviewing the case. I did a quick Google …
How can I bring up my saved passwords list? - Microsoft Community
Sep 19, 2023 · Hello there, I'm Gowtham, I'll be happy to help you! I apologize for the issue you are experiencing. Please be assured that I will do my best to provide a satisfactory response …
How to extract the direct facebook video url - Stack Overflow
Well i have not tried this in PHP, as per the facebook they have removed option in API to return source for the video, so i got it working using Python ;)
Using PayPal on Facebook Marketplace via Payment Request
Sep 19, 2019 · Hi, I am looking to make a purchase on FB marketplace from an individual seller. I linked my PayPal to my FB account. He requested payment through the Messenger on FB …
Facebook share link - can you customize the message body text?
Feb 17, 2011 · Facebook will not allow developers pre-fill messages. Developers may customize the story by providing OG meta tags, but it's up to the user to fill the message. This is only …
Facebook Login - Microsoft Community
5 days ago · I have just reinstalled the Facebook on my Laptop, Win10 and Edge with latest updates. First time to login worked fine. Next time I get a message this page isn't available link …
How to resolve Facebook Login is currently unavailable for this …
Jul 28, 2021 · In the facebook developers console for your app, go to App Review-> Permissions and Features. Set the public_profile and email to have advanced access. This will allow all …
Facebook login problem with Win 11 - Microsoft Community
Dec 20, 2021 · -Do a clean boot and try to log in as your username on Facebook, if the problem persists, when typing your username on Facebook, use the shortcut Windows+Ctrl+O to type …
Facebook On Microsoft Edge
Mar 6, 2021 · Why isn't Facebook working properly on Microsoft Edge? When I open it, I get my page with the latest post and no more. Won't let me click on anything to open. Apps, notices, …
How do I uninstall Facebook from my windows 11 computer
Oct 14, 2023 · To uninstall Facebook from your Windows 11 computer, you have a couple of options based on how you installed it. If you got it from the Microsoft Store as a widget, simply …
Solved: Unauthorized payment to Meta Platforms (Facebook i.
Nov 13, 2022 · The person said that the initial dispute resolution was "automated". Anyway, I am waiting a call-back or email as they are currently reviewing the case. I did a quick Google …
How can I bring up my saved passwords list? - Microsoft Community
Sep 19, 2023 · Hello there, I'm Gowtham, I'll be happy to help you! I apologize for the issue you are experiencing. Please be assured that I will do my best to provide a satisfactory response …
How to extract the direct facebook video url - Stack Overflow
Well i have not tried this in PHP, as per the facebook they have removed option in API to return source for the video, so i got it working using Python ;)
Using PayPal on Facebook Marketplace via Payment Request
Sep 19, 2019 · Hi, I am looking to make a purchase on FB marketplace from an individual seller. I linked my PayPal to my FB account. He requested payment through the Messenger on FB …
