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| data science intern interview questions: Ace the Data Science Interview Kevin Huo, Nick Singh, 2021 |
| data science intern interview questions: Machine Learning Bookcamp Alexey Grigorev, 2021-11-23 The only way to learn is to practice! In Machine Learning Bookcamp, you''ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you''ve learned in previous chapters. By the end of the bookcamp, you''ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technology Machine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that''s exactly what you''ll be doing in Machine Learning Bookcamp. about the book In Machine Learning Bookcamp you''ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you''ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You''ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you''re done working through these fun and informative projects, you''ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems. what''s inside Code fundamental ML algorithms from scratch Collect and clean data for training models Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images and text Deploy ML models to a production-ready environment about the reader For readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning. |
| data science intern interview questions: Quant Job Interview Questions and Answers Mark Joshi, Nick Denson, Nicholas Denson, Andrew Downes, 2013 The quant job market has never been tougher. Extensive preparation is essential. Expanding on the successful first edition, this second edition has been updated to reflect the latest questions asked. It now provides over 300 interview questions taken from actual interviews in the City and Wall Street. Each question comes with a full detailed solution, discussion of what the interviewer is seeking and possible follow-up questions. Topics covered include option pricing, probability, mathematics, numerical algorithms and C++, as well as a discussion of the interview process and the non-technical interview. All three authors have worked as quants and they have done many interviews from both sides of the desk. Mark Joshi has written many papers and books including the very successful introductory textbook, The Concepts and Practice of Mathematical Finance. |
| data science intern interview questions: Cracking the Data Science Interview Maverick Lin, 2019-12-17 Cracking the Data Science Interview is the first book that attempts to capture the essence of data science in a concise, compact, and clean manner. In a Cracking the Coding Interview style, Cracking the Data Science Interview first introduces the relevant concepts, then presents a series of interview questions to help you solidify your understanding and prepare you for your next interview. Topics include: - Necessary Prerequisites (statistics, probability, linear algebra, and computer science) - 18 Big Ideas in Data Science (such as Occam's Razor, Overfitting, Bias/Variance Tradeoff, Cloud Computing, and Curse of Dimensionality) - Data Wrangling (exploratory data analysis, feature engineering, data cleaning and visualization) - Machine Learning Models (such as k-NN, random forests, boosting, neural networks, k-means clustering, PCA, and more) - Reinforcement Learning (Q-Learning and Deep Q-Learning) - Non-Machine Learning Tools (graph theory, ARIMA, linear programming) - Case Studies (a look at what data science means at companies like Amazon and Uber) Maverick holds a bachelor's degree from the College of Engineering at Cornell University in operations research and information engineering (ORIE) and a minor in computer science. He is the author of the popular Data Science Cheatsheet and Data Engineering Cheatsheet on GCP and has previous experience in data science consulting for a Fortune 500 company focusing on fraud analytics. |
| data science intern interview questions: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-24 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| data science intern interview questions: Ask a Manager Alison Green, 2018-05-01 From the creator of the popular website Ask a Manager and New York’s work-advice columnist comes a witty, practical guide to 200 difficult professional conversations—featuring all-new advice! There’s a reason Alison Green has been called “the Dear Abby of the work world.” Ten years as a workplace-advice columnist have taught her that people avoid awkward conversations in the office because they simply don’t know what to say. Thankfully, Green does—and in this incredibly helpful book, she tackles the tough discussions you may need to have during your career. You’ll learn what to say when • coworkers push their work on you—then take credit for it • you accidentally trash-talk someone in an email then hit “reply all” • you’re being micromanaged—or not being managed at all • you catch a colleague in a lie • your boss seems unhappy with your work • your cubemate’s loud speakerphone is making you homicidal • you got drunk at the holiday party Praise for Ask a Manager “A must-read for anyone who works . . . [Alison Green’s] advice boils down to the idea that you should be professional (even when others are not) and that communicating in a straightforward manner with candor and kindness will get you far, no matter where you work.”—Booklist (starred review) “The author’s friendly, warm, no-nonsense writing is a pleasure to read, and her advice can be widely applied to relationships in all areas of readers’ lives. Ideal for anyone new to the job market or new to management, or anyone hoping to improve their work experience.”—Library Journal (starred review) “I am a huge fan of Alison Green’s Ask a Manager column. This book is even better. It teaches us how to deal with many of the most vexing big and little problems in our workplaces—and to do so with grace, confidence, and a sense of humor.”—Robert Sutton, Stanford professor and author of The No Asshole Rule and The Asshole Survival Guide “Ask a Manager is the ultimate playbook for navigating the traditional workforce in a diplomatic but firm way.”—Erin Lowry, author of Broke Millennial: Stop Scraping By and Get Your Financial Life Together |
| data science intern interview questions: Programming Interviews Exposed John Mongan, Noah Suojanen Kindler, Eric Giguère, 2011-08-10 The pressure is on during the interview process but with the right preparation, you can walk away with your dream job. This classic book uncovers what interviews are really like at America's top software and computer companies and provides you with the tools to succeed in any situation. The authors take you step-by-step through new problems and complex brainteasers they were asked during recent technical interviews. 50 interview scenarios are presented along with in-depth analysis of the possible solutions. The problem-solving process is clearly illustrated so you'll be able to easily apply what you've learned during crunch time. You'll also find expert tips on what questions to ask, how to approach a problem, and how to recover if you become stuck. All of this will help you ace the interview and get the job you want. What you will learn from this book Tips for effectively completing the job application Ways to prepare for the entire programming interview process How to find the kind of programming job that fits you best Strategies for choosing a solution and what your approach says about you How to improve your interviewing skills so that you can respond to any question or situation Techniques for solving knowledge-based problems, logic puzzles, and programming problems Who this book is for This book is for programmers and developers applying for jobs in the software industry or in IT departments of major corporations. Wrox Beginning guides are crafted to make learning programming languages and technologies easier than you think, providing a structured, tutorial format that will guide you through all the techniques involved. |
| data science intern interview questions: Deep Learning Interviews Shlomo Kashani, 2020-12-09 The book's contents is a large inventory of numerous topics relevant to DL job interviews and graduate level exams. That places this work at the forefront of the growing trend in science to teach a core set of practical mathematical and computational skills. It is widely accepted that the training of every computer scientist must include the fundamental theorems of ML, and AI appears in the curriculum of nearly every university. This volume is designed as an excellent reference for graduates of such programs. |
| data science intern interview questions: The New Rules of Work Alexandra Cavoulacos, Kathryn Minshew, 2017 In this definitive guide to the ever-changing modern workplace, Kathryn Minshew and Alexandra Cavoulacos, the co-founders of popular career website TheMuse.com, show how to play the game by the New Rules. The Muse is known for sharp, relevant, and get-to-the-point advice on how to figure out exactly what your values and your skills are and how they best play out in the marketplace. Now Kathryn and Alex have gathered all of that advice and more in The New Rules of Work. Through quick exercises and structured tips, the authors will guide you as you sort through your countless options; communicate who you are and why you are valuable; and stand out from the crowd. The New Rules of Work shows how to choose a perfect career path, land the best job, and wake up feeling excited to go to work every day-- whether you are starting out in your career, looking to move ahead, navigating a mid-career shift, or anywhere in between-- |
| data science intern interview questions: Cracking the Coding Interview Gayle Laakmann McDowell, 2011 Now in the 5th edition, Cracking the Coding Interview gives you the interview preparation you need to get the top software developer jobs. This book provides: 150 Programming Interview Questions and Solutions: From binary trees to binary search, this list of 150 questions includes the most common and most useful questions in data structures, algorithms, and knowledge based questions. 5 Algorithm Approaches: Stop being blind-sided by tough algorithm questions, and learn these five approaches to tackle the trickiest problems. Behind the Scenes of the interview processes at Google, Amazon, Microsoft, Facebook, Yahoo, and Apple: Learn what really goes on during your interview day and how decisions get made. Ten Mistakes Candidates Make -- And How to Avoid Them: Don't lose your dream job by making these common mistakes. Learn what many candidates do wrong, and how to avoid these issues. Steps to Prepare for Behavioral and Technical Questions: Stop meandering through an endless set of questions, while missing some of the most important preparation techniques. Follow these steps to more thoroughly prepare in less time. |
| data science intern interview questions: How Smart Machines Think Sean Gerrish, 2018-10-30 Everything you've always wanted to know about self-driving cars, Netflix recommendations, IBM's Watson, and video game-playing computer programs. The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM's Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today's machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world—and to play Atari video games better than humans. He explains Watson's famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution—at least for now. Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people. |
| data science intern interview questions: Interview Questions and Answers Richard McMunn, 2013-05 |
| data science intern interview questions: Data Science Interviews Exposed Jane You, Yanping Huang, Iris Wang, Feng Cao (Computer scientist), Ian Gao, 2015 The era has come when data science is changing the world and everyone's life. Data Science Interviews Exposed is the first book in the industry that covers everything you need to know to prepare for a data science career: from job market overview to job roles description, from resume preparation to soft skill development, and most importantly, the real interview questions and detailed answers. We hope this book can help the candidates in the data science job market, as well as those who need guidance to begin a data science career.--Back cover. |
| data science intern interview questions: 500 Data Science Interview Questions and Answers Vamsee Puligadda, Get that job, you aspire for! Want to switch to that high paying job? Or are you already been preparing hard to give interview the next weekend? Do you know how many people get rejected in interviews by preparing only concepts but not focusing on actually which questions will be asked in the interview? Don't be that person this time. This is the most comprehensive Data Science interview questions book that you can ever find out. It contains: 500 most frequently asked and important Data Science interview questions and answers Wide range of questions which cover not only basics in Data Science but also most advanced and complex questions which will help freshers, experienced professionals, senior developers, testers to crack their interviews. |
| data science intern interview questions: A Practical Guide To Quantitative Finance Interviews Xinfeng Zhou, 2020-05-05 This book will prepare you for quantitative finance interviews by helping you zero in on the key concepts that are frequently tested in such interviews. In this book we analyze solutions to more than 200 real interview problems and provide valuable insights into how to ace quantitative interviews. The book covers a variety of topics that you are likely to encounter in quantitative interviews: brain teasers, calculus, linear algebra, probability, stochastic processes and stochastic calculus, finance and programming. |
| data science intern interview questions: 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. |
| data science intern interview questions: 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 |
| data science intern interview questions: Programming Pearls Jon Bentley, 2016-04-21 When programmers list their favorite books, Jon Bentley’s collection of programming pearls is commonly included among the classics. Just as natural pearls grow from grains of sand that irritate oysters, programming pearls have grown from real problems that have irritated real programmers. With origins beyond solid engineering, in the realm of insight and creativity, Bentley’s pearls offer unique and clever solutions to those nagging problems. Illustrated by programs designed as much for fun as for instruction, the book is filled with lucid and witty descriptions of practical programming techniques and fundamental design principles. It is not at all surprising that Programming Pearls has been so highly valued by programmers at every level of experience. In this revision, the first in 14 years, Bentley has substantially updated his essays to reflect current programming methods and environments. In addition, there are three new essays on testing, debugging, and timing set representations string problems All the original programs have been rewritten, and an equal amount of new code has been generated. Implementations of all the programs, in C or C++, are now available on the Web. What remains the same in this new edition is Bentley’s focus on the hard core of programming problems and his delivery of workable solutions to those problems. Whether you are new to Bentley’s classic or are revisiting his work for some fresh insight, the book is sure to make your own list of favorites. |
| data science intern interview questions: Cracking the PM Interview Gayle Laakmann McDowell, Jackie Bavaro, 2013 How many pizzas are delivered in Manhattan? How do you design an alarm clock for the blind? What is your favorite piece of software and why? How would you launch a video rental service in India? This book will teach you how to answer these questions and more. Cracking the PM Interview is a comprehensive book about landing a product management role in a startup or bigger tech company. Learn how the ambiguously-named PM (product manager / program manager) role varies across companies, what experience you need, how to make your existing experience translate, what a great PM resume and cover letter look like, and finally, how to master the interview: estimation questions, behavioral questions, case questions, product questions, technical questions, and the super important pitch. |
| data science intern interview questions: Frenemies Ken Auletta, 2019-06-04 An intimate and profound reckoning with the changes buffeting the $2 trillion global advertising and marketing business from the perspective of its most powerful players, by the bestselling author of Googled Advertising and marketing touches on every corner of our lives, and the industry is the invisible fuel powering almost all media. Complain about it though we might, without it the world would be a darker place. But of all the industries wracked by change in the digital age, few have been turned on their heads as dramatically as this one. Mad Men are turning into Math Men (and women--though too few), an instinctual art is transforming into a science, and we are a long way from the days of Don Draper. Frenemies is Ken Auletta's reckoning with an industry under existential assault. He enters the rooms of the ad world's most important players, meeting the old guard as well as new powers and power brokers, investigating their perspectives. It's essential reading, not simply because of what it reveals about this world, but because of the potential consequences: the survival of media as we know it depends on the money generated by advertising and marketing--revenue that is in peril in the face of technological changes and the fraying trust between the industry's key players. |
| data science intern interview questions: GMAT Official Advanced Questions GMAC (Graduate Management Admission Council), 2019-09-24 GMAT Official Advanced Questions Your GMAT Official Prep collection of only hard GMAT questions from past exams. Bring your best on exam day by focusing on the hard GMAT questions to help improve your performance. Get 300 additional hard verbal and quantitative questions to supplement your GMAT Official Guide collection. GMAT Official Advance Questions: Specifically created for those who aspire to earn a top GMAT score and want additional prep. Expand your practice with 300 additional hard verbal and quantitative questions from past GMAT exams to help you perform at your best. Learn strategies to solve hard questions by reviewing answer explanations from subject matter experts. Organize your studying with practice questions grouped by fundamental skills Help increase your test-taking performance and confidence on exam day knowing you studied the hard GMAT questions. PLUS! Your purchase includes online resources to further your practice: Online Question Bank: Create your own practice sets online with the same questions in GMAT Official Advance Questions to focus your studying on specific fundamental skills. Mobile App: Access your Online Question Bank through the mobile app to never miss a moment of practice. Study on-the-go and sync with your other devices. Download the Online Question Bank once on your app and work offline. This product includes: print book with a unique access code and instructions to the Online Question Bank accessible via your computer and Mobile App. |
| data science intern interview questions: 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 |
| data science intern interview questions: 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. |
| data science intern interview questions: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| data science intern interview questions: Case Interview Secrets Victor Cheng, 2012 Cheng, a former McKinsey management consultant, reveals his proven, insider'smethod for acing the case interview. |
| data science intern interview questions: Elements of Programming Interviews Adnan Aziz, Tsung-Hsien Lee, Amit Prakash, 2012 The core of EPI is a collection of over 300 problems with detailed solutions, including 100 figures, 250 tested programs, and 150 variants. The problems are representative of questions asked at the leading software companies. The book begins with a summary of the nontechnical aspects of interviewing, such as common mistakes, strategies for a great interview, perspectives from the other side of the table, tips on negotiating the best offer, and a guide to the best ways to use EPI. The technical core of EPI is a sequence of chapters on basic and advanced data structures, searching, sorting, broad algorithmic principles, concurrency, and system design. Each chapter consists of a brief review, followed by a broad and thought-provoking series of problems. We include a summary of data structure, algorithm, and problem solving patterns. |
| data science intern interview questions: Machine Learning Paul Wilmott, 2019-05-20 Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the following topics - K Nearest Neighbours; K Means Clustering; Naïve Bayes Classifier; Regression Methods; Support Vector Machines; Self-Organizing Maps; Decision Trees; Neural Networks; Reinforcement Learning |
| data science intern interview questions: 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. |
| data science intern interview questions: 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. |
| data science intern interview questions: Coding Interview Questions Narasimha Karumanchi, 2012-05 Coding Interview Questions is a book that presents interview questions in simple and straightforward manner with a clear-cut explanation. This book will provide an introduction to the basics. It comes handy as an interview and exam guide for computer scientists. Programming puzzles for interviews Campus Preparation Degree/Masters Course Preparation Big job hunters: Apple, Microsoft, Google, Amazon, Yahoo, Flip Kart, Adobe, IBM Labs, Citrix, Mentor Graphics, NetApp, Oracle, Webaroo, De-Shaw, Success Factors, Face book, McAfee and many more Reference Manual for working people Topics Covered: Programming BasicsIntroductionRecursion and BacktrackingLinked Lists Stacks Queues Trees Priority Queue and HeapsGraph AlgorithmsSortingSearching Selection Algorithms [Medians] Symbol TablesHashing String Algorithms Algorithms Design Techniques Greedy Algorithms Divide and Conquer Algorithms Dynamic Programming Complexity Classes Design Interview Questions Operating System Concepts Computer Networking Basics Database Concepts Brain Teasers NonTechnical Help Miscellaneous Concepts Note: If you already have Data Structures and Algorithms Made Easy no need to buy this. |
| data science intern interview questions: 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! |
| data science intern interview questions: 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. |
| data science intern interview questions: Think Data Structures Allen B. Downey, 2017-07-07 If you’re a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineering—data structures and algorithms—in a way that’s clearer, more concise, and more engaging than other materials. By emphasizing practical knowledge and skills over theory, author Allen Downey shows you how to use data structures to implement efficient algorithms, and then analyze and measure their performance. You’ll explore the important classes in the Java collections framework (JCF), how they’re implemented, and how they’re expected to perform. Each chapter presents hands-on exercises supported by test code online. Use data structures such as lists and maps, and understand how they work Build an application that reads Wikipedia pages, parses the contents, and navigates the resulting data tree Analyze code to predict how fast it will run and how much memory it will require Write classes that implement the Map interface, using a hash table and binary search tree Build a simple web search engine with a crawler, an indexer that stores web page contents, and a retriever that returns user query results Other books by Allen Downey include Think Java, Think Python, Think Stats, and Think Bayes. |
| data science intern interview questions: Data Structures and Algorithms in Python Michael T. Goodrich, Roberto Tamassia, Michael H. Goldwasser, 2013-06-17 Based on the authors' market leading data structures books in Java and C++, this book offers a comprehensive, definitive introduction to data structures in Python by authoritative authors. Data Structures and Algorithms in Python is the first authoritative object-oriented book available for Python data structures. Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++. Begins by discussing Python's conceptually simple syntax, which allows for a greater focus on concepts. Employs a consistent object-oriented viewpoint throughout the text. Presents each data structure using ADTs and their respective implementations and introduces important design patterns as a means to organize those implementations into classes, methods, and objects. Provides a thorough discussion on the analysis and design of fundamental data structures. Includes many helpful Python code examples, with source code provided on the website. Uses illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner. Provides hundreds of exercises that promote creativity, help readers learn how to think like programmers, and reinforce important concepts. Contains many Python-code and pseudo-code fragments, and hundreds of exercises, which are divided into roughly 40% reinforcement exercises, 40% creativity exercises, and 20% programming projects. |
| data science intern interview questions: Heard on The Street Timothy Falcon Crack, 2024-08-05 [Warning: Do not buy an old edition of Timothy Crack's books by mistake. Click on the Amazon author page link for a list of the latest editions .] THIS IS A MUST READ! It is the first and the original book of quantitative questions from finance job interviews. Painstakingly revised over 30 years and 25 editions, Heard on The Street has been shaped by feedback from hundreds of readers. With well over 75,000 copies in print, its readership is unmatched by any competing book. The revised 25th edition contains 242 quantitative questions collected from actual job interviews in investment banking, investment management, and options trading. The interviewers use the same questions year-after-year, and here they are with detailed solutions! This edition also includes 267 non-quantitative actual interview questions, giving a total of more than 500 actual finance job interview questions. Questions that appeared in (or are likely to appear in) traditional corporate finance or investment banking job interviews are indicated with a bank symbol in the margin (72 of the 242 quant questions and 196 of the 267 non-quant questions). This makes it easier for corporate finance candidates to go directly to the questions most relevant to them. Most of these questions also appeared in capital markets interviews and quant interviews. So, they should not be skipped over by capital markets or quant candidates unless they are obviously irrelevant. There is also a recently revised section on interview technique based on feedback from interviewers worldwide. The quant questions cover pure quant/logic, financial economics, derivatives, and statistics. They come from all types of interviews (corporate finance, sales and trading, quant research, etc.), and from all levels of interviews (undergraduate, MS, MBA, PhD). The first seven editions of Heard on the Street contained an appendix on option pricing. That appendix was carved out as a standalone book many years ago and it is now available in a recently revised edition: Basic Black-Scholes. Dr. Crack did PhD coursework at MIT and Harvard, and graduated with a PhD from MIT. He has won many teaching awards, and has publications in the top academic, practitioner, and teaching journals in finance. He has degrees/diplomas in Mathematics/Statistics, Finance, Financial Economics and Accounting/Finance. Dr. Crack taught at the university level for over 25 years including four years as a front line teaching assistant for MBA students at MIT, and four years teaching undergraduates, MBAs, and PhDs at Indiana University. He has worked as an independent consultant to the New York Stock Exchange and to a foreign government body investigating wrong doing in the financial markets. He previously held a practitioner job as the head of a quantitative active equity research team at what was the world's largest institutional money manager. |
| data science intern interview questions: Psychometric Tests (the Ultimate Guide) Richard McMunn, 2010-11 |
| data science intern interview questions: Data Mining: Concepts and Techniques Jiawei Han, Micheline Kamber, Jian Pei, 2011-06-09 Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. - Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects - Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields - Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data |
| data science intern interview questions: Internships in Psychology Carol Williams-Nickelson, Mitchell J. Prinstein, 2005-01-01 Designed specifically for doctoral-level psychology graduate students, this volume will act as a personal mentor with step-by-step instructions to land an internship placement. This resource is just one of several services provided for students by the American Psychological Association of Graduate Students. |
| data science intern interview questions: The Medical Interview Mack Jr. Lipkin, J.G. Carroll, R.M. Frankel, Samuel M. Putnam, Aaron Lazare, A. Keller, T. Klein, P.K. Williams, 2012-12-06 Primary care medicine is the new frontier in medicine. Every nation in the world has recognized the necessity to deliver personal and primary care to its people. This includes first-contact care, care based in a posi tive and caring personal relationship, care by a single healthcare pro vider for the majority of the patient's problems, coordination of all care by the patient's personal provider, advocacy for the patient by the pro vider, the provision of preventive care and psychosocial care, as well as care for episodes of acute and chronic illness. These facets of care work most effectively when they are embedded in a coherent integrated approach. The support for primary care derives from several significant trends. First, technologically based care costs have rocketed beyond reason or availability, occurring in the face of exploding populations and diminish ing real resources in many parts of the world, even in the wealthier nations. Simultaneously, the primary care disciplines-general internal medicine and pediatrics and family medicine-have matured significantly. |
| data science intern interview questions: Cracking the Finance Quant Interview Jean Peyre, 2020-07-18 Although quantitative interviews are technically challenging, the hardest part can be to guess what you will be expected to know on the interview day. The scope of the requirements can also differ a lot between these roles within the banking sector. Author Jean Peyre has built a strong experience of quant interviews, both as an interviewee and an interviewer. Designed to be exhaustive but concise, this book covers all the parts you need to know before attending an interview. Content The book compiles 51 real quant interview questions asked in the banking industry 1) Brainteasers 2) Stochastic Calculus - Brownian motion, Martingale, Stopping time 3) Finance - Option pricing - Exchange Option, Forward starting Option, Straddles, Compound Option, Barrier Option 4) Programming - Sorting algorithms, Python, C++ 5) Classic derivations - Ornstein Uhlenbeck - Local Volatility - Fokker Planck - Hybrid Vasicek Model 6) Math handbook - The definitions and theorems you need to know |
Top 33 Data Science Intern Interview Questions and Answers 2025
Knowing what questions to expect and how to answer them effectively can significantly improve your odds of landing that coveted position. To help you in this endeavor, we have compiled a …
20 Common Data Science Intern Interview Questions
Apr 30, 2025 · Common Data Science Intern interview questions, how to answer them, and sample answers from a certified career coach.
Top 30 Data Science Intern Interview Questions You Need to …
Oct 1, 2024 · In this blog, we’ll explore the top 30 interview questions commonly asked for data science internships. We’ll not only present these questions but also offer in-depth answers, …
Top 48 Data Science Internship Interview Questions and Answer
Oct 24, 2024 · In this section, we have covered the fundamental questions often asked in data science internship interviews. These questions focus on testing your foundational knowledge of …
Data Science Intern Interview Questions and Answers
Feb 4, 2023 · Do you have an interview coming up for a Data Science Intern position? Find out what commonly asked questions are and learn how to answer.
25 Data Science Intern Interview Questions and Answers - CLIMB
May 1, 2025 · Learn what skills and qualities interviewers are looking for from a data science intern, what questions you can expect, and how you should go about answering…
Top 15 Data Science Intern Job Interview Questions, Answers
Aug 14, 2023 · What interests you the most about data science, and what do you hope to gain from this internship? 15. Can you provide an example of a time when you faced a challenge or …
100+ Data Science Intern Interview Questions and Answers
5 days ago · Discover Data Science Intern Interview questions & answers reported by 200+ real candidates on AmbitionBox.
50 Data Science Intern Interview Questions With Answers
Sep 21, 2023 · Practice the 50 most important data science intern interview questions with sample answers. Give confident answers and impress the hiring manager.
Data Science Intern Interview Questions - CovrLtr
Here are 20 Interview questions for Data Science Intern interviews. Prepare an answer for these questions and you’ll showcase yourself as the ideal candidate to the hiring manager.
Top 33 Data Science Intern Interview Questions and Answers 2025
Knowing what questions to expect and how to answer them effectively can significantly improve your odds of landing that coveted position. To help you in this endeavor, we have compiled a …
20 Common Data Science Intern Interview Questions
Apr 30, 2025 · Common Data Science Intern interview questions, how to answer them, and sample answers from a certified career coach.
Top 30 Data Science Intern Interview Questions You Need to …
Oct 1, 2024 · In this blog, we’ll explore the top 30 interview questions commonly asked for data science internships. We’ll not only present these questions but also offer in-depth answers, …
Top 48 Data Science Internship Interview Questions and Answer
Oct 24, 2024 · In this section, we have covered the fundamental questions often asked in data science internship interviews. These questions focus on testing your foundational knowledge …
Data Science Intern Interview Questions and Answers
Feb 4, 2023 · Do you have an interview coming up for a Data Science Intern position? Find out what commonly asked questions are and learn how to answer.
25 Data Science Intern Interview Questions and Answers - CLIMB
May 1, 2025 · Learn what skills and qualities interviewers are looking for from a data science intern, what questions you can expect, and how you should go about answering…
Top 15 Data Science Intern Job Interview Questions, Answers
Aug 14, 2023 · What interests you the most about data science, and what do you hope to gain from this internship? 15. Can you provide an example of a time when you faced a challenge or …
100+ Data Science Intern Interview Questions and Answers
5 days ago · Discover Data Science Intern Interview questions & answers reported by 200+ real candidates on AmbitionBox.
50 Data Science Intern Interview Questions With Answers
Sep 21, 2023 · Practice the 50 most important data science intern interview questions with sample answers. Give confident answers and impress the hiring manager.
Data Science Intern Interview Questions - CovrLtr
Here are 20 Interview questions for Data Science Intern interviews. Prepare an answer for these questions and you’ll showcase yourself as the ideal candidate to the hiring manager.
