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| faang data science interview prep: Ace the Data Science Interview Kevin Huo, Nick Singh, 2021 |
| faang data science interview prep: 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. |
| faang data science interview prep: RocketPrep Ace Your Data Science Interview 300 Practice Questions and Answers: Machine Learning, Statistics, Databases and More Zack Austin, 2017-12-09 Here's what you get in this book: - 300 practice questions and answers spanning the breadth of topics under the data science umbrella - Covers statistics, machine learning, SQL, NoSQL, Hadoop and bioinformatics - Emphasis on real-world application with a chapter on Python libraries for machine learning - Focus on the most frequently asked interview questions. Avoid information overload - Compact format: easy to read, easy to carry, so you can study on-the-go Now, you finally have what you need to crush your data science interview, and land that dream job. About The Author Zack Austin has been building large scale enterprise systems for clients in the media, telecom, financial services and publishing since 2001. He is based in New York City. |
| faang data science interview prep: 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 |
| faang data science interview prep: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-24 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| faang data science interview prep: Cracking The Machine Learning Interview Nitin Suri, 2018-12-18 A breakthrough in machine learning would be worth ten Microsofts. -Bill Gates Despite being one of the hottest disciplines in the Tech industry right now, Artificial Intelligence and Machine Learning remain a little elusive to most.The erratic availability of resources online makes it extremely challenging for us to delve deeper into these fields. Especially when gearing up for job interviews, most of us are at a loss due to the unavailability of a complete and uncondensed source of learning. Cracking the Machine Learning Interview Equips you with 225 of the best Machine Learning problems along with their solutions. Requires only a basic knowledge of fundamental mathematical and statistical concepts. Assists in learning the intricacies underlying Machine Learning concepts and algorithms suited to specific problems. Uniquely provides a manifold understanding of both statistical foundations and applied programming models for solving problems. Discusses key points and concrete tips for approaching real life system design problems and imparts the ability to apply them to your day to day work. This book covers all the major topics within Machine Learning which are frequently asked in the Interviews. These include: Supervised and Unsupervised Learning Classification and Regression Decision Trees Ensembles K-Nearest Neighbors Logistic Regression Support Vector Machines Neural Networks Regularization Clustering Dimensionality Reduction Feature Extraction Feature Engineering Model Evaluation Natural Language Processing Real life system design problems Mathematics and Statistics behind the Machine Learning Algorithms Various distributions and statistical tests This book can be used by students and professionals alike. It has been drafted in a way to benefit both, novices as well as individuals with substantial experience in Machine Learning. Following Cracking The Machine Learning Interview diligently would equip you to face any Machine Learning Interview. |
| faang data science interview prep: 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. |
| faang data science interview prep: 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. |
| faang data science interview prep: Data Science with Machine Learning Narayanan Vishwanathan, 2019-09-20 Starts with statistics then goes towards Core Python followed by numpy to pandas to scipy and sklearnKey features Easy to learn, step by step explanation of examples. Questions related to core/basic Python, Excel, basic and advanced statistics are included. Covers numpy, scipy, sklearn and pandas to a greater detail with good number of examples Description The book e;Data science with Machine learning- Python interview questionse; is a true companion of people aspiring for data science and machine learning and provides answers to mostly asked questions in a easy to remember and presentable form.Data science is one of the hottest topics mainly because of the application areas it is involved and things which were once upon of time, impossible with earlier software has been made easy. This book is mainly intended to be used as last-minute revision, before interview, as all the important concepts have been given in simple and understand format. Many examples have been provided so that same can be used while giving answers in interview.This book tries to include various terminologies and logic used both as a part of Data Science and Machine learning for last minute revision. As such you can say that this book acts as a companion whenever you want to go for interview.Simple to use words have been used in the answers for the questions to help ease of remembering and representation of same. Examples where ever deemed necessary have been provided so that same can be used while giving answers in interview. Author tried to consolidate whatever he came across, on multiple interviews that he attended and put the same in words so that it becomes easy for the reader of the book to give direction on how the interview would be.With the number of data science jobs increasing, Author is sure that everyone who wants to pursue this field would like to keep this book as a constant companion. What will you learn You can learn the basic concept and terms related to Data Science You will get to learn how to program in python You can learn the basic questions of python programming By reading this book you can get to know the basics of Numpy You will get familiarity with the questions asked in interview related to Pandas. You will learn the concepts of Scipy, Matplotib, and Statistics with Excel Sheet Who this book is forThe book is intended for anyone wish to learn Python Data Science, Numpy, Pandas, Scipy, Matplotib and Statistics with Excel Sheet. This book content also covers the basic questions which are asked during an interview. This book is mainly intended to help people represent their answer in a sensible way to the interviewer. The answers have been carefully rendered in a way to make things quite simple and yet represent the seriousness and complexity of matter. Since data science is incomplete without mathematics we have also included a part of the book dedicated to statistics. Table of contents1. Data Science Basic Questions and Terms2. Python Programming Questions3. Numpy Interview Questions4. Pandas Interview Questions5. Scipy and its Applications6. Matplotlib Samples to Remember7. Statistics with Excel Sheet About the authorMr Vishwanathan has twenty years of hard code experience in software industry spanning across many multinational companies and domains. Playing with data to derive meaningful insights has been his domain and that is what took him towards data science and machine learning. |
| faang data science interview prep: 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. |
| faang data science interview prep: Cracking the Coding Interview Gayle Laakmann McDowell, 2011 Now in the 5th edition, Cracking the Coding Interview gives you the interview preparation you need to get the top software developer jobs. This book provides: 150 Programming Interview Questions and Solutions: From binary trees to binary search, this list of 150 questions includes the most common and most useful questions in data structures, algorithms, and knowledge based questions. 5 Algorithm Approaches: Stop being blind-sided by tough algorithm questions, and learn these five approaches to tackle the trickiest problems. Behind the Scenes of the interview processes at Google, Amazon, Microsoft, Facebook, Yahoo, and Apple: Learn what really goes on during your interview day and how decisions get made. Ten Mistakes Candidates Make -- And How to Avoid Them: Don't lose your dream job by making these common mistakes. Learn what many candidates do wrong, and how to avoid these issues. Steps to Prepare for Behavioral and Technical Questions: Stop meandering through an endless set of questions, while missing some of the most important preparation techniques. Follow these steps to more thoroughly prepare in less time. |
| faang data science interview prep: Practical Statistics for Data Scientists Peter Bruce, Andrew Bruce, 2017-05-10 Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data |
| faang data science interview prep: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| faang data science interview prep: Data Science from Scratch Joel Grus, 2015-04-14 Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases |
| faang data science interview prep: The Data Science Handbook Carl Shan, Henry Wang, William Chen, Max Song, 2015-05-03 The Data Science Handbook is a curated collection of 25 candid, honest and insightful interviews conducted with some of the world's top data scientists.In this book, you'll hear how the co-creator of the term 'data scientist' thinks about career and personal success. You'll hear from a young woman who created her own data scientist curriculum, subsequently landing her a role in the field. Readers of this book will be left with war stories, wisdom and |
| faang data science interview prep: A Collection of Data Science Interview Questions Solved in Python and Spark Antonio Gulli, 2015-09-22 BigData and Machine Learning in Python and Spark |
| faang data science interview prep: How to Lead in Data Science Jike Chong, Yue Cathy Chang, 2021-12-28 A field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples. In How To Lead in Data Science you will learn: Best practices for leading projects while balancing complex trade-offs Specifying, prioritizing, and planning projects from vague requirements Navigating structural challenges in your organization Working through project failures with positivity and tenacity Growing your team with coaching, mentoring, and advising Crafting technology roadmaps and championing successful projects Driving diversity, inclusion, and belonging within teams Architecting a long-term business strategy and data roadmap as an executive Delivering a data-driven culture and structuring productive data science organizations How to Lead in Data Science is full of techniques for leading data science at every seniority level—from heading up a single project to overseeing a whole company's data strategy. Authors Jike Chong and Yue Cathy Chang share hard-won advice that they've developed building data teams for LinkedIn, Acorns, Yiren Digital, large asset-management firms, Fortune 50 companies, and more. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and highlight development areas. About the technology Lead your data science teams and projects to success! To make a consistent, meaningful impact as a data science leader, you must articulate technology roadmaps, plan effective project strategies, support diversity, and create a positive environment for professional growth. This book delivers the wisdom and practical skills you need to thrive as a data science leader at all levels, from team member to the C-suite. About the book How to Lead in Data Science shares unique leadership techniques from high-performance data teams. It’s filled with best practices for balancing project trade-offs and producing exceptional results, even when beginning with vague requirements or unclear expectations. You’ll find a clearly presented modern leadership framework based on current case studies, with insights reaching all the way to Aristotle and Confucius. As you read, you’ll build practical skills to grow and improve your team, your company’s data culture, and yourself. What's inside How to coach and mentor team members Navigate an organization’s structural challenges Secure commitments from other teams and partners Stay current with the technology landscape Advance your career About the reader For data science practitioners at all levels. About the author Dr. Jike Chong and Yue Cathy Chang build, lead, and grow high-performing data teams across industries in public and private companies, such as Acorns, LinkedIn, large asset-management firms, and Fortune 50 companies. Table of Contents 1 What makes a successful data scientist? PART 1 THE TECH LEAD: CULTIVATING LEADERSHIP 2 Capabilities for leading projects 3 Virtues for leading projects PART 2 THE MANAGER: NURTURING A TEAM 4 Capabilities for leading people 5 Virtues for leading people PART 3 THE DIRECTOR: GOVERNING A FUNCTION 6 Capabilities for leading a function 7 Virtues for leading a function PART 4 THE EXECUTIVE: INSPIRING AN INDUSTRY 8 Capabilities for leading a company 9 Virtues for leading a company PART 5 THE LOOP AND THE FUTURE 10 Landscape, organization, opportunity, and practice 11 Leading in data science and a future outlook |
| faang data science interview prep: Programming Interviews Exposed John Mongan, Noah Suojanen Kindler, Eric Giguère, 2011-08-10 The pressure is on during the interview process but with the right preparation, you can walk away with your dream job. This classic book uncovers what interviews are really like at America's top software and computer companies and provides you with the tools to succeed in any situation. The authors take you step-by-step through new problems and complex brainteasers they were asked during recent technical interviews. 50 interview scenarios are presented along with in-depth analysis of the possible solutions. The problem-solving process is clearly illustrated so you'll be able to easily apply what you've learned during crunch time. You'll also find expert tips on what questions to ask, how to approach a problem, and how to recover if you become stuck. All of this will help you ace the interview and get the job you want. What you will learn from this book Tips for effectively completing the job application Ways to prepare for the entire programming interview process How to find the kind of programming job that fits you best Strategies for choosing a solution and what your approach says about you How to improve your interviewing skills so that you can respond to any question or situation Techniques for solving knowledge-based problems, logic puzzles, and programming problems Who this book is for This book is for programmers and developers applying for jobs in the software industry or in IT departments of major corporations. Wrox Beginning guides are crafted to make learning programming languages and technologies easier than you think, providing a structured, tutorial format that will guide you through all the techniques involved. |
| faang data science interview prep: Grokking the System Design Interview Design Gurus, 2021-12-18 This book (also available online at www.designgurus.org) by Design Gurus has helped 60k+ readers to crack their system design interview (SDI). System design questions have become a standard part of the software engineering interview process. These interviews determine your ability to work with complex systems and the position and salary you will be offered by the interviewing company. Unfortunately, SDI is difficult for most engineers, partly because they lack experience developing large-scale systems and partly because SDIs are unstructured in nature. Even engineers who've some experience building such systems aren't comfortable with these interviews, mainly due to the open-ended nature of design problems that don't have a standard answer. This book is a comprehensive guide to master SDIs. It was created by hiring managers who have worked for Google, Facebook, Microsoft, and Amazon. The book contains a carefully chosen set of questions that have been repeatedly asked at top companies. What's inside? This book is divided into two parts. The first part includes a step-by-step guide on how to answer a system design question in an interview, followed by famous system design case studies. The second part of the book includes a glossary of system design concepts. Table of Contents First Part: System Design Interviews: A step-by-step guide. Designing a URL Shortening service like TinyURL. Designing Pastebin. Designing Instagram. Designing Dropbox. Designing Facebook Messenger. Designing Twitter. Designing YouTube or Netflix. Designing Typeahead Suggestion. Designing an API Rate Limiter. Designing Twitter Search. Designing a Web Crawler. Designing Facebook's Newsfeed. Designing Yelp or Nearby Friends. Designing Uber backend. Designing Ticketmaster. Second Part: Key Characteristics of Distributed Systems. Load Balancing. Caching. Data Partitioning. Indexes. Proxies. Redundancy and Replication. SQL vs. NoSQL. CAP Theorem. PACELC Theorem. Consistent Hashing. Long-Polling vs. WebSockets vs. Server-Sent Events. Bloom Filters. Quorum. Leader and Follower. Heartbeat. Checksum. About the Authors Designed Gurus is a platform that offers online courses to help software engineers prepare for coding and system design interviews. Learn more about our courses at www.designgurus.org. |
| faang data science interview prep: Programming Interviews Exposed John Mongan, Noah Suojanen Kindler, Eric Giguère, 2018-04-17 Ace technical interviews with smart preparation Programming Interviews Exposed is the programmer’s ideal first choice for technical interview preparation. Updated to reflect changing techniques and trends, this new fourth edition provides insider guidance on the unique interview process that today's programmers face. Online coding contests are being used to screen candidate pools of thousands, take-home projects have become commonplace, and employers are even evaluating a candidate's public code repositories at GitHub—and with competition becoming increasingly fierce, programmers need to shape themselves into the ideal candidate well in advance of the interview. This book doesn't just give you a collection of questions and answers, it walks you through the process of coming up with the solution so you learn the skills and techniques to shine on whatever problems you’re given. This edition combines a thoroughly revised basis in classic questions involving fundamental data structures and algorithms with problems and step-by-step procedures for new topics including probability, data science, statistics, and machine learning which will help you fully prepare for whatever comes your way. Learn what the interviewer needs to hear to move you forward in the process Adopt an effective approach to phone screens with non-technical recruiters Examine common interview problems and tests with expert explanations Be ready to demonstrate your skills verbally, in contests, on GitHub, and more Technical jobs require the skillset, but you won’t get hired unless you are able to effectively and efficiently demonstrate that skillset under pressure, in competition with hundreds of others with the same background. Programming Interviews Exposed teaches you the interview skills you need to stand out as the best applicant to help you get the job you want. |
| faang data science interview prep: T-SQL Window Functions Itzik Ben-Gan, 2019-10-18 Use window functions to write simpler, better, more efficient T-SQL queries Most T-SQL developers recognize the value of window functions for data analysis calculations. But they can do far more, and recent optimizations make them even more powerful. In T-SQL Window Functions, renowned T-SQL expert Itzik Ben-Gan introduces breakthrough techniques for using them to handle many common T-SQL querying tasks with unprecedented elegance and power. Using extensive code examples, he guides you through window aggregate, ranking, distribution, offset, and ordered set functions. You’ll find a detailed section on optimization, plus an extensive collection of business solutions — including novel techniques available in no other book. Microsoft MVP Itzik Ben-Gan shows how to: • Use window functions to improve queries you previously built with predicates • Master essential SQL windowing concepts, and efficiently design window functions • Effectively utilize partitioning, ordering, and framing • Gain practical in-depth insight into window aggregate, ranking, offset, and statistical functions • Understand how the SQL standard supports ordered set functions, and find working solutions for functions not yet available in the language • Preview advanced Row Pattern Recognition (RPR) data analysis techniques • Optimize window functions in SQL Server and Azure SQL Database, making the most of indexing, parallelism, and more • Discover a full library of window function solutions for common business problems About This Book • For developers, DBAs, data analysts, data scientists, BI professionals, and power users familiar with T-SQL queries • Addresses any edition of the SQL Server 2019 database engine or later, as well as Azure SQL Database Get all code samples at: MicrosoftPressStore.com/TSQLWindowFunctions/downloads |
| faang data science interview prep: 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. |
| faang data science interview prep: 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. |
| faang data science interview prep: Elements of Programming Interviews Adnan Aziz, Tsung-Hsien Lee, Amit Prakash, 2012 The core of EPI is a collection of over 300 problems with detailed solutions, including 100 figures, 250 tested programs, and 150 variants. The problems are representative of questions asked at the leading software companies. The book begins with a summary of the nontechnical aspects of interviewing, such as common mistakes, strategies for a great interview, perspectives from the other side of the table, tips on negotiating the best offer, and a guide to the best ways to use EPI. The technical core of EPI is a sequence of chapters on basic and advanced data structures, searching, sorting, broad algorithmic principles, concurrency, and system design. Each chapter consists of a brief review, followed by a broad and thought-provoking series of problems. We include a summary of data structure, algorithm, and problem solving patterns. |
| faang data science interview prep: Some Of Myself Suzanne D Williams, 2022-02-14 I can't do this again, she cried. I can't. It'll be like last time, and my life will be ruined. I just wanted to start over. Shh. No, it won't. You have me. The last thing Eden Riske expected when she came home was the discernment of fellow teacher Austin Lowell. Football coach, history teacher, fitness buff, Austin is strength and patience in a handsome package. However, it seems even his presence can't stop the rumors swirling around her or the hatred of someone determined to do her harm. But this job is supposed to be her salvation, her way out of her troubled past. Except now, everything is falling apart, and the one thing that might destroy her is the very secret she's held inside for so long. |
| faang data science interview prep: How Smart Machines Think Sean Gerrish, 2018-10-30 Everything you've always wanted to know about self-driving cars, Netflix recommendations, IBM's Watson, and video game-playing computer programs. The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM's Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today's machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world—and to play Atari video games better than humans. He explains Watson's famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution—at least for now. Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people. |
| faang data science interview prep: System Design Interview - An Insider's Guide Alex Xu, 2020-06-12 The system design interview is considered to be the most complex and most difficult technical job interview by many. Those questions are intimidating, but don't worry. It's just that nobody has taken the time to prepare you systematically. We take the time. We go slow. We draw lots of diagrams and use lots of examples. You'll learn step-by-step, one question at a time.Don't miss out.What's inside?- An insider's take on what interviewers really look for and why.- A 4-step framework for solving any system design interview question.- 16 real system design interview questions with detailed solutions.- 188 diagrams to visually explain how different systems work. |
| faang data science interview prep: 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. |
| faang data science interview prep: The Google Resume Gayle Laakmann McDowell, 2011-01-25 The Google Resume is the only book available on how to win a coveted spot at Google, Microsoft, Apple, or other top tech firms. Gayle Laakmann McDowell worked in Google Engineering for three years, where she served on the hiring committee and interviewed over 120 candidates. She interned for Microsoft and Apple, and interviewed with and received offers from ten tech firms. If you’re a student, you’ll learn what to study and how to prepare while in school, as well as what career paths to consider. If you’re a job seeker, you’ll get an edge on your competition by learning about hiring procedures and making yourself stand out from other candidates. Covers key concerns like what to major in, which extra-curriculars and other experiences look good, how to apply, how to design and tailor your resume, how to prepare for and excel in the interview, and much more Author was on Google’s hiring committee; interned at Microsoft and Apple; has received job offers from more than 10 tech firms; and runs CareerCup.com, a site devoted to tech jobs Get the only comprehensive guide to working at some of America’s most dynamic, innovative, and well-paying tech companies with The Google Resume. |
| faang data science interview prep: Deep Learning and the Game of Go Kevin Ferguson, Max Pumperla, 2019-01-06 Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning |
| faang data science interview prep: 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. |
| faang data science interview prep: Freelance Newbie RealToughCandy, 2019-02-01 Are you ready to jump-start your freelance career? Freelance Newbie has you covered! In this book, you’ll learn practical, actionable steps you can start using today to get your first client by the end of the week. Featuring all the methods, techniques, tips, tricks, and insights you need to succeed, Freelance Newbie was written by a working freelancer whose mission is to help people like you find personal success and financial independence. The material you’ll read here has never been featured at a lower price — you simply cannot get this kind of value for less. We go through everything step-by-step with real-world examples so you know exactly what you need to do to become a successful freelancer. In Freelance Newbie, you’ll learn how to: :• Develop a business plan from scratch• Establish a suitable work environment • Configure your own freelance website to generate quality leads • Determine what services to offer (and what to do if you don't know how to do something) • Figure out an appropriate pricing scheme for your services • Find “starter” clients that pave the way for 5-star social proof and full-paying, long-term clients • Draft effective proposals and contracts• Advertise for free (or very, very cheaply) • Deliver above-average customer service • Efficiently complete client projects — time runs out FAST • And much, much more! This book can also be used as the perfect companion manual to the video course available on Udemy by RealToughCandy. |
| faang data science interview prep: Cracking the PM Interview Gayle Laakmann McDowell, Jackie Bavaro, 2013 How many pizzas are delivered in Manhattan? How do you design an alarm clock for the blind? What is your favorite piece of software and why? How would you launch a video rental service in India? This book will teach you how to answer these questions and more. Cracking the PM Interview is a comprehensive book about landing a product management role in a startup or bigger tech company. Learn how the ambiguously-named PM (product manager / program manager) role varies across companies, what experience you need, how to make your existing experience translate, what a great PM resume and cover letter look like, and finally, how to master the interview: estimation questions, behavioral questions, case questions, product questions, technical questions, and the super important pitch. |
| faang data science interview prep: The Boston Consulting Group on Strategy Carl W. Stern, Michael S. Deimler, 2012-06-14 A collection of the best thinking from one of the most innovative management consulting firms in the world For more than forty years, The Boston Consulting Group has been shaping strategic thinking in business. The Boston Consulting Group on Strategy offers a broad and up-to-date selection of the firm's best ideas on strategy with fresh ideas, insights, and practical lessons for managers, executives, and entrepreneurs in every industry. Here's a sampling of the provocative thinking you'll find inside: You have to be the scientist of your own life and be astonished four times:at what is, what always has been, what once was, and what could be. The majority of products in most companies are cash traps . . . .[They] are not only worthless, but a perpetual drain on corporate resources. Use more debt than your competition or get out of the business. When information flows freely, reputation, more than reciprocity,becomes the basis for trust. As a strategic weapon, time is the equivalent of money, productivity,quality, even innovation. When brands become business systems, brand management becomes far too important to leave to the marketing department. The winning organization of the future will look more like a collection ofjazz ensembles than a symphony orchestra. Most of our organizations today derive from a model whose original purpose was to control creativity. Rather than being an obstacle, uncertainty is the very engine of transformation in a business, a continuous source of new opportunities. IP assets lack clear property lines. Every bit of intellectual property you can own comes with connections to other valuable innovations. |
| faang data science interview prep: R for Everyone Jared P. Lander, 2017-06-13 Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includes Explore R, RStudio, and R packages Use R for math: variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using R’s facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalized linear, and nonlinear models Program basic statistics: mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyze univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp Register your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available. |
| faang data science interview prep: How to Get a Job in Web Development RealToughCandy, 2018-07-31 How to Get a Job in Web Development is designed for junior web developers. Whether you’re coming from a coding bootcamp, are completely self-taught, or graduated from college with a tech-related degree, this book is for you. Written by RealToughCandy. In this book, you will learn how to: • Expertly craft the ‘holy clover’ of application materials: your resume, cover letter, GitHub page, and portfolio. • Leverage the power of LinkedIn, Meetups, and social media. • Handle follow-up emails and phone calls. • Prepare for the multiple types of interviews you will encounter, whether via phone, video conference, or in person. • Strategically apply to jobs so you can maximize your salary demands during negotiation. • Efficiently organize and prioritize the jobs you’ve applied to. • Craft results-driven email check-ins with your potential employer. • Reduce your vulnerabilities for discrimination. • And much, much more! No awkward whiteboard interviews. No hour-long explanation of Big O notation. Just practical, actionable steps that will put you far ahead of the pack when it comes to getting a job in web development. Now let's go get that job! Just finished reading your book and all I can say is WOW! Mind you since May of 2016 I have taken about 6 online courses specifically looking for employment and around three of them were specifically for either how to get an IT or Web Developer job. These courses cannot hold a candle to the majority of the information you put in this book! -George M., Web Developer WHY I WROTE THIS BOOK: When I started my web development journey, I was a lost hiker in the digital woods. I knew I wanted to build web apps, but didn’t know what those people called themselves. Were they website builders? Programmers? The term ‘software engineer’ floated around a lot online – was that my aspiration? Since I didn’t know exactly what I was looking for, I spent a lot of time reading and watching materials that were nothing but discouraging: mock Google coding interviews with whiteboards and markers. Lots of articles and videos that name-dropped things like binary trees, Big O notation, and time complexity. Forum post upon forum post that gave away actual coding interview questions from the biggest tech companies in the world like Facebook, Google, and Microsoft. Making things worse, some web developers I had discovered on YouTube were talking about a really good, popular book for coding interviews. I checked it out and once again my stomach sank. “I’m never going to make it in this field,” I said to myself. “I’ve been studying and practicing and building projects for months, and I still have no idea what these people are talking about.” What they didn’t tell me was that the book is geared towards senior software engineers trying to get a job with Amazon and Google. I wanted to quit my coding journey. In fact, I did quit. The difference was, I didn’t stay quit. Something told me to keep pushing forward, keep building projects to put in my portfolio and Github, keep reaching out and trying to find clients who needed websites. I kept pushing until I got a job as a fullstack web developer at a data company. As it turns out, the internet isn’t very generous to our career field. Beginners are especially marginalized. There aren’t any quality one-stop resources for discovering one of the most important questions – if not the most important question – web developers have. “How do I get a job in this field?” I wanted to change the junior web developer tech landscape with this book. My goal is for every junior developer who reads this to find a job. And if you take the recommended actions in this book, you can do it. |
| faang data science interview prep: Smarter Data Science Neal Fishman, Cole Stryker, 2020-04-14 Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how. Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments. When an organization manages its data effectively, its data science program becomes a fully scalable function that’s both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise. By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements: Improving time-to-value with infused AI models for common use cases Optimizing knowledge work and business processes Utilizing AI-based business intelligence and data visualization Establishing a data topology to support general or highly specialized needs Successfully completing AI projects in a predictable manner Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations. |
| faang data science interview prep: The Analytics Edge Dimitris Bertsimas, Allison K. O'Hair, William R. Pulleyblank, 2016 Provides a unified, insightful, modern, and entertaining treatment of analytics. The book covers the science of using data to build models, improve decisions, and ultimately add value to institutions and individuals--Back cover. |
| faang data science interview prep: Machine Learning: Theory and Applications , 2013-05-16 Statistical learning and analysis techniques have become extremely important today, given the tremendous growth in the size of heterogeneous data collections and the ability to process it even from physically distant locations. Recent advances made in the field of machine learning provide a strong framework for robust learning from the diverse corpora and continue to impact a variety of research problems across multiple scientific disciplines. The aim of this handbook is to familiarize beginners as well as experts with some of the recent techniques in this field.The Handbook is divided in two sections: Theory and Applications, covering machine learning, data analytics, biometrics, document recognition and security. - Very relevant to current research challenges faced in various fields - Self-contained reference to machine learning - Emphasis on applications-oriented techniques |
| faang data science interview prep: Swipe to Unlock Neel Mehta, Parth Detroja, Aditya Agashe, 2017 WANT A NON-CODING JOB AT A TECH COMPANY? Interested in product management, marketing, strategy, or business development? The tech industry is the place to be: nontechnical employees at tech companies outnumber their engineering counterparts almost 3 to 1 (Forbes, 2017). You might be worried that your lack of coding skills or tech industry knowledge will hold you back. But here's the secret: you don't need to learn how to code to break into the tech industry. Written by three former Microsoft PMs, Swipe to Unlock gives you a breakdown of the concepts you need to know to crush your interviews, like software development, big data, and internet security. We'll explain how Google's ad targeting algorithm works, but Google probably won't ask you how to explain it in a non-technical interview. But they might ask you how you could increase ad revenue from a particular market segment. And if you know how Google's ad platform works, you'll be in a far stronger position to come up with good growth strategies. We'll show you how Robinhood, an app that lets you trade stocks without commission, makes money by earning interest on the unspent money that users keep in their accounts. No one will ask you to explain this. But if someone asks you to come up with a new monetization strategy for Venmo (which lets you send and receive money without fees), you could pull out the Robinhood anecdote to propose that Venmo earn interest off the money sitting in users' accounts. We'll talk about some business cases like why Microsoft acquired LinkedIn. Microsoft interviewers probably won't ask you about the motive of the purchase, but they might ask you for ideas to improve Microsoft Outlook. From our case study, you'll learn how the Microsoft and LinkedIn ecosystems could work together, which can help you craft creative, impactful answers. You could propose that Outlook use LinkedIn's social graph to give salespeople insights about clients before meeting them. Or you could suggest linking Outlook's organizational tree to LinkedIn to let HR managers analyze their company's hierarchy and figure out what kind of talent they need to add. (We'll further explore both ideas in the book.) Either way, you're sure to impress. Learn the must know concepts of tech from authors who have received job offers for Facebook's Rotational Product Manager, Google's Associate Product Marketing Manager, and Microsoft's Program Manager to get a competitive edge at your interviews! |
FAANG companies: what is the attraction? : r/recruitinghell - Reddit
Going to a FAANG-tier company, I make 160k to do the same. Exact. Thing. Also getting to participate in an employee stock purchase plan where I purchase FAANG stock at a discount, on …
Understanding FAANG Leveling : r/leetcode - Reddit
Oct 30, 2023 · Understanding FAANG Leveling Every time I mention leveling in this subreddit, either L{n}, E{n}, or junior-principle, I get questions asking for clarity on what these terms mean. Using …
[AMA] I'm a data science manager in FAANG : r/datascience - Reddit
I know someone who applied to 35 roles in the same company before he got in. I personally couldn't get an interview in FAANG so I started applying for internships in FAANG (even though I had just …
What colleges does FAANG/big tech actively recruit from?
Absolutely not UMBC. I’m in FAANG and graduated from UMBC but not bc of the school. I landed a FAANG swe position only bc I worked at a virtually unknown company after graduation (including …
Petition to change the “n” in FAANG to NVIDIA : r/csMajors - Reddit
Mar 25, 2024 · FAANG, though originally coined as a term for growth tech stocks, is widely used in this sub as a group of high paying companies with great benefits. In that regard I don't think …
A guideline to land a FAANG job in 2 years for someone ... - Reddit
Jul 16, 2021 · Learn about some complicated data structures. Learn to do basically everything with graphs and linked lists that one might want to do. And then when you think you're ready, apply to …
Why does everyone want to work at FAANG? : r/dataengineering
May 3, 2022 · Cracking FAANG interviews are tough and having FAANG and other big tech on your resume/cv assures that you will not be denied future relevant opportunities based on work …
How much does a software engineer make at FAANG? : r/bayarea
Back in 2016, my offer from FB (er, Meta) at E5 was $210K base, $50K sign-on, and $825K initial grant. I ended up going to a FAANG adjacent company with even higher TC. In today’s climate, …
Equivalent of FAANG in cyber : r/cybersecurity - Reddit
I purchased the Exponent course it was absolutely 100% fantastic value and would recommend it to anyone interviewing at a FAANG. I think it was about $110 for a year subscription, but they had …
People who got FAANG+ internships (or any coveted internship
idk how “coveted” my internship is (not FAANG but above min wage (Cali) and remote), but i wrote a Discord bot in Node.js, it uses an API and SQLite to store some data, including a date and user …
FAANG companies: what is the attraction? : r/recruitinghell - Reddit
Going to a FAANG-tier company, I make 160k to do the same. Exact. Thing. Also getting to participate in an employee stock purchase plan where I purchase FAANG stock at a discount, …
Understanding FAANG Leveling : r/leetcode - Reddit
Oct 30, 2023 · Understanding FAANG Leveling Every time I mention leveling in this subreddit, either L{n}, E{n}, or junior-principle, I get questions asking for clarity on what these terms …
[AMA] I'm a data science manager in FAANG : r/datascience - Reddit
I know someone who applied to 35 roles in the same company before he got in. I personally couldn't get an interview in FAANG so I started applying for internships in FAANG (even …
What colleges does FAANG/big tech actively recruit from?
Absolutely not UMBC. I’m in FAANG and graduated from UMBC but not bc of the school. I landed a FAANG swe position only bc I worked at a virtually unknown company after graduation …
Petition to change the “n” in FAANG to NVIDIA : r/csMajors - Reddit
Mar 25, 2024 · FAANG, though originally coined as a term for growth tech stocks, is widely used in this sub as a group of high paying companies with great benefits. In that regard I don't think …
A guideline to land a FAANG job in 2 years for someone ... - Reddit
Jul 16, 2021 · Learn about some complicated data structures. Learn to do basically everything with graphs and linked lists that one might want to do. And then when you think you're ready, …
Why does everyone want to work at FAANG? : r/dataengineering
May 3, 2022 · Cracking FAANG interviews are tough and having FAANG and other big tech on your resume/cv assures that you will not be denied future relevant opportunities based on work …
How much does a software engineer make at FAANG? : r/bayarea
Back in 2016, my offer from FB (er, Meta) at E5 was $210K base, $50K sign-on, and $825K initial grant. I ended up going to a FAANG adjacent company with even higher TC. In today’s climate, …
Equivalent of FAANG in cyber : r/cybersecurity - Reddit
I purchased the Exponent course it was absolutely 100% fantastic value and would recommend it to anyone interviewing at a FAANG. I think it was about $110 for a year subscription, but they …
People who got FAANG+ internships (or any coveted internship
idk how “coveted” my internship is (not FAANG but above min wage (Cali) and remote), but i wrote a Discord bot in Node.js, it uses an API and SQLite to store some data, including a date …
