Author: Kal Mishra
Publisher: Createspace Independent Publishing Platform
ISBN: 9781727287325
Category :
Languages : en
Pages : 240
Book Description
A collection of over 650 actual Data Scientist/Machine Learning Engineer job interview questions along with their full answers, references, and useful tips
Build a Career in Data Science
Author: Emily Robinson
Publisher: Manning
ISBN: 1617296244
Category : Computers
Languages : en
Pages : 352
Book Description
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
Publisher: Manning
ISBN: 1617296244
Category : Computers
Languages : en
Pages : 352
Book Description
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
Cracking the Data Science Interview
Author: Maverick Lin
Publisher:
ISBN: 9781710680133
Category :
Languages : en
Pages : 120
Book Description
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.
Publisher:
ISBN: 9781710680133
Category :
Languages : en
Pages : 120
Book Description
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 For Dummies
Author: Lillian Pierson
Publisher: John Wiley & Sons
ISBN: 1119811619
Category : Computers
Languages : en
Pages : 436
Book Description
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.
Publisher: John Wiley & Sons
ISBN: 1119811619
Category : Computers
Languages : en
Pages : 436
Book Description
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.
Quant Job Interview Questions and Answers
Author: Mark Joshi
Publisher:
ISBN: 9780987122827
Category : Business & Economics
Languages : en
Pages : 0
Book Description
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."
Publisher:
ISBN: 9780987122827
Category : Business & Economics
Languages : en
Pages : 0
Book Description
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."
Machine Learning Bookcamp
Author: Alexey Grigorev
Publisher: Simon and Schuster
ISBN: 1617296813
Category : Computers
Languages : en
Pages : 470
Book Description
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.
Publisher: Simon and Schuster
ISBN: 1617296813
Category : Computers
Languages : en
Pages : 470
Book Description
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.
She's Too Pretty to Burn
Author: Wendy Heard
Publisher: Henry Holt and Company (BYR)
ISBN: 1250246768
Category : Young Adult Fiction
Languages : en
Pages : 256
Book Description
“An anxiety-ridden ride as two girls' lives crash together through secrets, love and danger. . . Captivating and stunningly visual.” —Aiden Thomas, New York Times-bestselling author of Cemetery Boys An electric romance set against a rebel art scene sparks lethal danger for two girls in She's Too Pretty to Burn, an expertly plotted YA thriller by Wendy Heard. The summer is winding down in San Diego. Veronica is bored, caustically charismatic, and uninspired in her photography. Nico is insatiable, subversive, and obsessed with chaotic performance art. They’re artists first, best friends second. But that was before Mick. Delicate, lonely, magnetic Mick: the perfect subject, and Veronica’s dream girl. The days are long and hot—full of adventure—and soon they are falling in love. Falling so hard, they never imagine what comes next. One fire. Two murders. Three drowning bodies. One suspect . . . one stalker. This is a summer they won’t survive. Inspired by The Picture of Dorian Gray, this sexy psychological thriller explores the intersections of love, art, danger, and power. Christy Ottaviano Books
Publisher: Henry Holt and Company (BYR)
ISBN: 1250246768
Category : Young Adult Fiction
Languages : en
Pages : 256
Book Description
“An anxiety-ridden ride as two girls' lives crash together through secrets, love and danger. . . Captivating and stunningly visual.” —Aiden Thomas, New York Times-bestselling author of Cemetery Boys An electric romance set against a rebel art scene sparks lethal danger for two girls in She's Too Pretty to Burn, an expertly plotted YA thriller by Wendy Heard. The summer is winding down in San Diego. Veronica is bored, caustically charismatic, and uninspired in her photography. Nico is insatiable, subversive, and obsessed with chaotic performance art. They’re artists first, best friends second. But that was before Mick. Delicate, lonely, magnetic Mick: the perfect subject, and Veronica’s dream girl. The days are long and hot—full of adventure—and soon they are falling in love. Falling so hard, they never imagine what comes next. One fire. Two murders. Three drowning bodies. One suspect . . . one stalker. This is a summer they won’t survive. Inspired by The Picture of Dorian Gray, this sexy psychological thriller explores the intersections of love, art, danger, and power. Christy Ottaviano Books
Interview Research in Political Science
Author: Maria Elayna Mosley
Publisher: Cornell University Press
ISBN: 0801467969
Category : Political Science
Languages : en
Pages : 313
Book Description
Interviews are a frequent and important part of empirical research in political science, but graduate programs rarely offer discipline-specific training in selecting interviewees, conducting interviews, and using the data thus collected. Interview Research in Political Science addresses this vital need, offering hard-won advice for both graduate students and faculty members. The contributors to this book have worked in a variety of field locations and settings and have interviewed a wide array of informants, from government officials to members of rebel movements and victims of wartime violence, from lobbyists and corporate executives to workers and trade unionists. The authors encourage scholars from all subfields of political science to use interviews in their research, and they provide a set of lessons and tools for doing so. The book addresses how to construct a sample of interviewees; how to collect and report interview data; and how to address ethical considerations and the Institutional Review Board process. Other chapters discuss how to link interview-based evidence with causal claims; how to use proxy interviews or an interpreter to improve access; and how to structure interview questions. A useful appendix contains examples of consent documents, semistructured interview prompts, and interview protocols.
Publisher: Cornell University Press
ISBN: 0801467969
Category : Political Science
Languages : en
Pages : 313
Book Description
Interviews are a frequent and important part of empirical research in political science, but graduate programs rarely offer discipline-specific training in selecting interviewees, conducting interviews, and using the data thus collected. Interview Research in Political Science addresses this vital need, offering hard-won advice for both graduate students and faculty members. The contributors to this book have worked in a variety of field locations and settings and have interviewed a wide array of informants, from government officials to members of rebel movements and victims of wartime violence, from lobbyists and corporate executives to workers and trade unionists. The authors encourage scholars from all subfields of political science to use interviews in their research, and they provide a set of lessons and tools for doing so. The book addresses how to construct a sample of interviewees; how to collect and report interview data; and how to address ethical considerations and the Institutional Review Board process. Other chapters discuss how to link interview-based evidence with causal claims; how to use proxy interviews or an interpreter to improve access; and how to structure interview questions. A useful appendix contains examples of consent documents, semistructured interview prompts, and interview protocols.
Elements of Programming Interviews
Author: Adnan Aziz
Publisher: EPI
ISBN: 1479274836
Category : Business & Economics
Languages : en
Pages : 530
Book Description
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.
Publisher: EPI
ISBN: 1479274836
Category : Business & Economics
Languages : en
Pages : 530
Book Description
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.