| data science business case interview: Ace the Data Science Interview Kevin Huo, Nick Singh, 2021 |
| data science business case interview: 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 business case interview: Cracking the Data Science Interview Maverick Lin, 2019-12-17 Cracking the Data Science Interview is the first book that attempts to capture the essence of data science in a concise, compact, and clean manner. In a Cracking the Coding Interview style, Cracking the Data Science Interview first introduces the relevant concepts, then presents a series of interview questions to help you solidify your understanding and prepare you for your next interview. Topics include: - Necessary Prerequisites (statistics, probability, linear algebra, and computer science) - 18 Big Ideas in Data Science (such as Occam's Razor, Overfitting, Bias/Variance Tradeoff, Cloud Computing, and Curse of Dimensionality) - Data Wrangling (exploratory data analysis, feature engineering, data cleaning and visualization) - Machine Learning Models (such as k-NN, random forests, boosting, neural networks, k-means clustering, PCA, and more) - Reinforcement Learning (Q-Learning and Deep Q-Learning) - Non-Machine Learning Tools (graph theory, ARIMA, linear programming) - Case Studies (a look at what data science means at companies like Amazon and Uber) Maverick holds a bachelor's degree from the College of Engineering at Cornell University in operations research and information engineering (ORIE) and a minor in computer science. He is the author of the popular Data Science Cheatsheet and Data Engineering Cheatsheet on GCP and has previous experience in data science consulting for a Fortune 500 company focusing on fraud analytics. |
| data science business case interview: Decode and Conquer Lewis C. Lin, 2013-11-28 Land that Dream Product Manager Job...TODAYSeeking a product management position?Get Decode and Conquer, the world's first book on preparing you for the product management (PM) interview. Author and professional interview coach, Lewis C. Lin provides you with an industry insider's perspective on how to conquer the most difficult PM interview questions. Decode and Conquer reveals: Frameworks for tackling product design and metrics questions, including the CIRCLES Method(tm), AARM Method(tm), and DIGS Method(tm) Biggest mistakes PM candidates make at the interview and how to avoid them Insider tips on just what interviewers are looking for and how to answer so they can't say NO to hiring you Sample answers for the most important PM interview questions Questions and answers covered in the book include: Design a new iPad app for Google Spreadsheet. Brainstorm as many algorithms as possible for recommending Twitter followers. You're the CEO of the Yellow Cab taxi service. How do you respond to Uber? You're part of the Google Search web spam team. How would you detect duplicate websites? The billboard industry is under monetized. How can Google create a new product or offering to address this? Get the Book that's Recommended by Executives from Google, Amazon, Microsoft, Oracle & VMWare...TODAY |
| data science business case interview: Product Sense Peter Knudson, Braxton Bragg, 2021-07-12 Attempting to land a new job in product management is daunting. For starters, there have been no comprehensive blueprints for success. The interview process is grueling. Few candidates receive offers. Product Sense is the only comprehensive, yet accessible, resource available to help navigate a complex process and succeed an a hyper-competitive market. What will you learn from this book? The required PM common traits - ones that all PMs need to embody to get a job (regardless of industry, company, or product). The single, most crucial PM problem -What it is, why it is key to the role, and how to tackle it in four steps. Master our brand new Compass Framework - We designed our own proprietary interview framework from the ground up, which you can use to navigate product sense, execution, and leadership PM interview questions. How to get a job - A step-by-step hand-holding on what to do to land the most desired roles. Including take-home assignments, recruiter & hiring manager screens, and crafting your unique narrative - your PM Superpower. What's also inside? A detailed breakdown of the hiring criteria for PMs at FAANG and other tech companies Super-detailed example answers to tough PM interview case questions. An inside look at PM. Dozens of first-hand stories, interviews, real life examples, and no-fluff advice A robust glossary of PM terms used throughout the industry for easy reference This book will benefit those who are considering becoming PMs, those who are attempting to switch into product management from another role, or folks who are already PMs but want to be most prepared when applying for a new job. Here's what readers say about Product Sense: Product Sense helped me understand if PM is the right career path for me. Easy to read, clear, concise, and jam-packed full of insight and examples that illustrate all the concepts, this is the perfect starting point for anyone new to the field, and goes well beyond that for those looking to advance their career. Peter is one of the best strategic and tactical product minds I've ever worked with. For that reason, I'm not at all surprised that what he and Braxton have written here is a definitive guide to Product Management in today's ultra-competitive market. After reading Cracking the PM Interview, I was still lost as to how to structure my answers to case questions. While I understand that there is no right way to answer these interview questions, I appreciated that Product Sense gave me firm and clear guidance, walking me through the basics of PM thinking and how to adopt it in my interview answers. It was reassuring to see that the best mock interviews have all of the elements of Product Sense's Compass Framework. If CTPMI is the first step to prepare for landing a PM Role, then Product Sense is definitely the second step. |
| data science business case interview: Heard in Data Science Interviews Kal Mishra, 2018-10-03 A collection of over 650 actual Data Scientist/Machine Learning Engineer job interview questions along with their full answers, references, and useful tips |
| data science business case interview: Case Interview Secrets Victor Cheng, 2012 Cheng, a former McKinsey management consultant, reveals his proven, insider'smethod for acing the case interview. |
| data science business case interview: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| data science business case interview: Cases in Online Interview Research Janet Salmons, 2011-11-08 In an era of constrained research budgets, online interviewing opens up immense possibilities: a researcher can literally conduct a global study without ever leaving home. But more than a decade after these technologies started to become available, there are still few studies on how to utilize online interviews in research. This book provides 10 cases of research conducted using online interviews, with data collected through text-based, videoconferencing, multichannel meetings, and immersive 3-D environments. Each case is followed by two commentaries: one from another expert contributor, the second from Janet Salmons, as editor. |
| data science business case interview: Case Interview Questions for Tech Companies Lewis Lin, 2016-10-04 Case Interview Questions for Tech Companies provides 155 practice questions and answers to conquer case interviews for the following tech roles: Marketing Operations Finance Strategy Analytics Business Development Supplier or Vendor Management ...and Product Management -- QUESTIONS COVERED IN THE BOOK Here are some of the questions covered in the book: Marketing Create a marketing campaign for Microsoft Office 365. Write a media statement to respond to Uber mischaracterizations voiced in a taxi leader's newspaper op-ed. Operations Describe how Apple's supply chain works. What challenges does Apple face on a day-to-day basis? What's the bottleneck for an Amazon Robot Picker? And what is the capacity of the assembly line, in units per hour? During the holiday season, Amazon customers shipped 200 orders per second. Amazon's data science team discovered that the average number of orders waiting to be shipped was 20,650. How long did the average Amazon order wait to be shipped? Finance What should Apple consider before implementing a shop-in-shop store inside Best Buy? If you projected a $500M expense and the variance came in at $1M, what are some of the explanations for why that is happening? Be prepared to give more than three scenarios. Business Development A car dealer partner wants to stop doing business with Uber. What should you do? How would you identify university faculty to source content for an online university? Strategy If you could open a Google store anywhere, where would it be and why? Give your analysis of several recent acquisitions that Google has made. Analytics What top metrics would you track for the Tinder online dating app? If 1,000 people opened the Uber app during one hour, how many cars do you need? Product Management Let's say we wanted to implement an Amazon Mayday-like feature in Gmail. How would that work? How would you any Microsoft product to a restaurant? AUTHOR BIO Lewis C. Lin, former Google and Microsoft executive, has trained thousands of candidates to get ready for tech interviews, using his proven interview techniques. Lewis' students have received offers from the most coveted firms including Google, Facebook, Uber, Amazon, Microsoft, IBM, Dell and HP. Lewis has a bachelor's in computer science from Stanford University and an MBA from Northwestern University's Kellogg School of Management. He's the author of several bestsellers including Interview Math, Rise Above the Noise as well as Decode and Conquer. HERE'S WHAT PEOPLE SAY ABOUT THE AUTHOR Got the Amazon offer, with an initial package that was $100K more than what I currently make at [a top 5 tech company]. It's a dream job for the role of Principal Product Manager for a [special project]. - Q.K. Just signed the offer for a Google product marketing manager role. Your tips helped me relax and concentrate, so the time went by quickly even though it was really a tough interview. - D.E. I had my in-person interviews down at Facebook last week and got my offer letter the next day! You were definitely a huge help in preparing for the interviews. - L.S. |
| data science business case interview: Fighting Churn with Data Carl S. Gold, 2020-12-22 The beating heart of any product or service business is returning clients. Don't let your hard-won customers vanish, taking their money with them. In Fighting Churn with Data you'll learn powerful data-driven techniques to maximize customer retention and minimize actions that cause them to stop engaging or unsubscribe altogether. Summary The beating heart of any product or service business is returning clients. Don't let your hard-won customers vanish, taking their money with them. In Fighting Churn with Data you'll learn powerful data-driven techniques to maximize customer retention and minimize actions that cause them to stop engaging or unsubscribe altogether. This hands-on guide is packed with techniques for converting raw data into measurable metrics, testing hypotheses, and presenting findings that are easily understandable to non-technical decision makers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Keeping customers active and engaged is essential for any business that relies on recurring revenue and repeat sales. Customer turnover—or “churn”—is costly, frustrating, and preventable. By applying the techniques in this book, you can identify the warning signs of churn and learn to catch customers before they leave. About the book Fighting Churn with Data teaches developers and data scientists proven techniques for stopping churn before it happens. Packed with real-world use cases and examples, this book teaches you to convert raw data into measurable behavior metrics, calculate customer lifetime value, and improve churn forecasting with demographic data. By following Zuora Chief Data Scientist Carl Gold’s methods, you’ll reap the benefits of high customer retention. What's inside Calculating churn metrics Identifying user behavior that predicts churn Using churn reduction tactics with customer segmentation Applying churn analysis techniques to other business areas Using AI for accurate churn forecasting About the reader For readers with basic data analysis skills, including Python and SQL. About the author Carl Gold (PhD) is the Chief Data Scientist at Zuora, Inc., the industry-leading subscription management platform. Table of Contents: PART 1 - BUILDING YOUR ARSENAL 1 The world of churn 2 Measuring churn 3 Measuring customers 4 Observing renewal and churn PART 2 - WAGING THE WAR 5 Understanding churn and behavior with metrics 6 Relationships between customer behaviors 7 Segmenting customers with advanced metrics PART 3 - SPECIAL WEAPONS AND TACTICS 8 Forecasting churn 9 Forecast accuracy and machine learning 10 Churn demographics and firmographics 11 Leading the fight against churn |
| data science business case interview: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| data science business case interview: Problem Solving 101 Ken Watanabe, 2009-03-05 The fun and simple problem-solving guide that took Japan by storm Ken Watanabe originally wrote Problem Solving 101 for Japanese schoolchildren. His goal was to help shift the focus in Japanese education from memorization to critical thinking, by adapting some of the techniques he had learned as an elite McKinsey consultant. He was amazed to discover that adults were hungry for his fun and easy guide to problem solving and decision making. The book became a surprise Japanese bestseller, with more than 370,000 in print after six months. Now American businesspeople can also use it to master some powerful skills. Watanabe uses sample scenarios to illustrate his techniques, which include logic trees and matrixes. A rock band figures out how to drive up concert attendance. An aspiring animator budgets for a new computer purchase. Students decide which high school they will attend. Illustrated with diagrams and quirky drawings, the book is simple enough for a middleschooler to understand but sophisticated enough for business leaders to apply to their most challenging problems. |
| data science business case interview: 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 business case interview: Thinking with Data Max Shron, 2014-01-20 Many analysts are too concerned with tools and techniques for cleansing, modeling, and visualizing datasets and not concerned enough with asking the right questions. In this practical guide, data strategy consultant Max Shron shows you how to put the why before the how, through an often-overlooked set of analytical skills. Thinking with Data helps you learn techniques for turning data into knowledge you can use. You’ll learn a framework for defining your project, including the data you want to collect, and how you intend to approach, organize, and analyze the results. You’ll also learn patterns of reasoning that will help you unveil the real problem that needs to be solved. Learn a framework for scoping data projects Understand how to pin down the details of an idea, receive feedback, and begin prototyping Use the tools of arguments to ask good questions, build projects in stages, and communicate results Explore data-specific patterns of reasoning and learn how to build more useful arguments Delve into causal reasoning and learn how it permeates data work Put everything together, using extended examples to see the method of full problem thinking in action |
| data science business case interview: Storytelling with Data Cole Nussbaumer Knaflic, 2015-10-09 Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it! |
| data science business case interview: Data Science and Machine Learning Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman, 2019-11-20 Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code |
| data science business case interview: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
| data science business case interview: 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 business case interview: Interviewing for Social Scientists Hilary Arksey, Peter T Knight, 1999-10-25 `This is an excellent book. It will be required reading on my methods courses' - Nigel Fielding, University of Surrey Students at postgraduate, and increasingly at undergraduate, level are required to undertake research projects and interviewing is the most frequently used research method. This book provides a comprehensive and authoritative introduction to interviewing. It covers all the issues that arise in interview work: theories of interviewing; design; application; and interpretation. Richly illustrated with relevant examples, each chapter includes handy statements of `advantages' and `disadvantages' of the approaches discussed. |
| data science business case interview: Conducting Research Interviews for Business and Management Students Catherine Cassell, 2015-02-12 In Conducting Research Interviews, Catherine Cassell guides you through conceptualizing the interview, preparing for the research interview, conducting the interview, examples, conclusions and next steps. Ideal for Business and Management students reading for a Master’s degree, each book in the series may also serve as reference books for doctoral students and faculty members interested in the method. Part of SAGE’s Mastering Business Research Methods Series, conceived and edited by Bill Lee, Mark N. K. Saunders and Vadake K. Narayanan and designed to support researchers by providing in-depth and practical guidance on using a chosen method of data collection or analysis. |
| data science business case interview: The Fourth Industrial Revolution Klaus Schwab, 2017-01-03 World-renowned economist Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, explains that we have an opportunity to shape the fourth industrial revolution, which will fundamentally alter how we live and work. Schwab argues that this revolution is different in scale, scope and complexity from any that have come before. Characterized by a range of new technologies that are fusing the physical, digital and biological worlds, the developments are affecting all disciplines, economies, industries and governments, and even challenging ideas about what it means to be human. Artificial intelligence is already all around us, from supercomputers, drones and virtual assistants to 3D printing, DNA sequencing, smart thermostats, wearable sensors and microchips smaller than a grain of sand. But this is just the beginning: nanomaterials 200 times stronger than steel and a million times thinner than a strand of hair and the first transplant of a 3D printed liver are already in development. Imagine “smart factories” in which global systems of manufacturing are coordinated virtually, or implantable mobile phones made of biosynthetic materials. The fourth industrial revolution, says Schwab, is more significant, and its ramifications more profound, than in any prior period of human history. He outlines the key technologies driving this revolution and discusses the major impacts expected on government, business, civil society and individuals. Schwab also offers bold ideas on how to harness these changes and shape a better future—one in which technology empowers people rather than replaces them; progress serves society rather than disrupts it; and in which innovators respect moral and ethical boundaries rather than cross them. We all have the opportunity to contribute to developing new frameworks that advance progress. |
| data science business case interview: 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 |
| data science business case interview: Case in Point Marc Cosentino, 2011 Marc Cosentino demystifies the consulting case interview. He takes you inside a typical interview by exploring the various types of case questions and he shares with you the acclaimed Ivy Case System which will give you the confidence to answer even the most sophisticated cases. The book includes over 40 strategy cases, a number of case starts exercises, several human capital cases, a section on marketing cases and 21 ways to cut costs. |
| data science business case interview: 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 |
| data science business case interview: 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 business case interview: Solving Data Science Case Studies with Python Aman Kharwal, 2021-06-28 This book is specially written for those who know the basics of the Python programming language as well as the necessary Python libraries you need for data science like NumPy, Pandas, Matplotlib, Seaborn, Plotly, and Scikit-learn. This book aims to teach you how to think while solving a business problem with your data science skills. To achieve the goal of this book, I started by giving you all the knowledge you need to have before you apply for your first data science job. The technical skills and soft skills you need to become a Data Scientist are also discussed in this book. Next, you'll find some of the best data science case studies that will help you understand what your approach should be while solving a business problem. Ultimately, you will also find some of the most important data science interview questions with their solutions at the end. I hope this book will add a lot of value to your data science skills and that you will feel confident in your entire journey to become Data Scientist. |
| data science business case interview: Conducting Case Study Research for Business and Management Students Bill Lee, Mark N. K. Saunders, 2017-10-23 In Case Study Research, Bill Lee and Mark Saunders describe the properties of case study designs in organizational research, exploring the uses, advantages and limitations of case research. They also demonstrate the flexibility that case designs offer, and challenges the myths surrounding this approach. Ideal for Business and Management students reading for a Master’s degree, each book in the series may also serve as reference books for doctoral students and faculty members interested in the method. Part of SAGE’s Mastering Business Research Methods Series, conceived and edited by Bill Lee, Mark N. K. Saunders and Vadake K. Narayanan and designed to support students by providing in-depth and practical guidance on using a chosen method of data collection or analysis. |
| data science business case interview: 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 business case interview: The Humachine Nada R. Sanders, John D. Wood, 2019-09-09 There is a lot of hype, hand-waving, and ink being spilled about artificial intelligence (AI) in business. The amount of coverage of this topic in the trade press and on shareholder calls is evidence of a large change currently underway. It is awesome and terrifying. You might think of AI as a major environmental factor that is creating an evolutionary pressure that will force enterprise to evolve or perish. For those companies that do survive the silicon wave sweeping through the global economy, the issue becomes how to keep their humanity amidst the tumult. What started as an inquiry into how executives can adopt AI to harness the best of human and machine capabilities turned into a much more profound rumination on the future of humanity and enterprise. This is a wake-up call for business leaders across all sectors of the economy. Not only should you implement AI regardless of your industry, but once you do, you should fight to stay true to your purpose, your ethical convictions, indeed your humanity, even as our organizations continue to evolve. While not holding any punches about the dangers posed by overpowered AI, this book uniquely surveys where technology is limited, and gives reason for cautious optimism about the true opportunities that lie amidst all the disruptive change currently underway. As such, it is distinctively more optimistic than many of the competing titles on Big Technology. This compelling book weaves together business strategy and philosophy of mind, behavioral psychology and the limits of technology, leadership and law. The authors set out to identify where humans and machines can best complement one another to create an enterprise greater than the sum total of its parts: the Humachine. Combining the global business and forecasting acumen of Professor Nada R. Sanders, PhD, with the legal and philosophical insight of John D. Wood, Esq., the authors combine their strengths to bring us this profound yet accessible book. This is a must read for anyone interested in AI and the future of human enterprise. |
| data science business case interview: The Case Interview: 20 Days to Ace the Case Destin Whitehurst, Erin Robinson, 2016-02-11 Game-changing tips and tricks to nail the case interview and launch your consulting career. Management consultants Destin Whitehurst and Erin Robinson give you need-to-know techniques for polishing your poise and tightening your case interview skills. 20 Days to Ace the Case Interview preps you with the nuts and bolts of the case interview process with daily exercises, mock interviews, and industry know-how designed to help you ace your interview. Think of this book as your twenty-day intensive, management consulting boot camp, the perfect supplement to your arsenal of case interview lessons and material. With this guidebook, you will: Gain day-by-day structure: Daily case interview exercises progressively prep you Ask the right questions: Fundamental frameworks teach you exactly what to ask under pressure Learn from the pros: Review real-life stories from consulting experts Uncover unique strategies: Discover custom-developed case interview tips straight from the authors Go off script: Adapt what you’ve learned with our bonus case interview guides |
| data science business case interview: 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 business case interview: Case in Point Marc Cosentino, Mukund Jain, 2016 The use of complex graphs in case interviews has exploded. Interviewees have a very short time to look at the graph, analyze it, extract what is important and apply it to their answer. This book was designed to help understand the role of graphs in consulting (both during an interview and on the job). The authors introduce the Ivy Graph Framework to assist the analysis of graphs during interviews. In addition the book provides ten sophisticated cases with numerous graphs per case. |
| data science business case interview: 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 business case interview: The Tao of Coaching Max Landsberg, 2015-03-05 The essence and success of The Tao of Coaching has always been its focus on the practical tips and techniques for making work more rewarding through the habit of coaching - and this philosophy continues to underpin this brand new reissue. The book's premise is simple: that to become an effective coach, managers and leaders need master only a few techniques, even though mastery obviously requires practice. Each chapter focuses on a specific technique - or Golden Rule - of coaching to help practice make perfect. Tried and tested by generations within and beyond the workplace, this succinct and engaging book gives readers the tools to: - create more time for themselves, by delegating well - build, and enjoy working with, effective teams - achieve better results - enhance their interpersonal skills. It demonstrates that coaching is not simply a matter of helping others and improving performance, but is also a powerful force for self-development and personal fulfilment. |
| data science business case interview: The Consulting Interview Bible Jenny Rae Le Roux, Kevin Gao, 2014 |
| data science business case interview: 500 Data Science Interview Questions and Answers Vamsee Puligadda, Get that job, you aspire for! Want to switch to that high paying job? Or are you already been preparing hard to give interview the next weekend? Do you know how many people get rejected in interviews by preparing only concepts but not focusing on actually which questions will be asked in the interview? Don't be that person this time. This is the most comprehensive Data Science interview questions book that you can ever find out. It contains: 500 most frequently asked and important Data Science interview questions and answers Wide range of questions which cover not only basics in Data Science but also most advanced and complex questions which will help freshers, experienced professionals, senior developers, testers to crack their interviews. |
| data science business case interview: 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 business case interview: How to Get Into the Top Consulting Firms Tim Darling, 2009 |
| data science business case interview: 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. |
Prep Kit Rd. 1 Interview Candidate - DoorDash
Case Focus: The interview will focus on DoorDash’s 3-sided Marketplace, and will require you to talk through a vague data science issue that would be relevant to DoorDash. Below is more …
Part II: Case Study | 30-minute using Zoom video conference
Research and understand the data & metrics a company like DoorDash looks into to preserve the health of our business & products. The Case Study will give us a sense of your overall data …
Case Interview Workbook - Accenture
clients. A case interview provides excellent insight into how well you would perform in a consulting situation. The purpose of this guide is to help familiarize you with the process of a case …
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What is a case interview? Definition Who uses case interviews Format of case interviews Scenario-based job interviews that test problem solving skills. In a case interview, candidates …
Ace the Data Science Interview - dispatch-cdn.its.uiowa.edu
Want to Ace your upcoming Data Science or Analytics job interview? Get tips on how to solve SQL, Statistics, and Data Case interview questions asked by FAANG + Wall Street. Random …
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120 Data Science Interview Questions 1. What is meant by selection bias? Answer: Selection bias is a type of error that arises when the researcher decides on whom he is going to conduct the …
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Data Science Business Case Interview (Download Only)
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consulting case interview He takes you inside a typical interview by exploring the various types of case questions and he shares with you the acclaimed Ivy Case System which will give you the …
Prep Kit Rd. 1 Interview Candidate - DoorDash
Case Focus: The interview will focus on DoorDash’s 3-sided Marketplace, and will require you to talk through a vague data science issue that would be …
Part II: Case Study | 30-minute using Zoom video c…
Research and understand the data & metrics a company like DoorDash looks into to preserve the health of our business & products. The Case Study …
Case Interview Workbook - Accenture
clients. A case interview provides excellent insight into how well you would perform in a consulting situation. The purpose of this guide …
Case Book 2013 - Case Interview
There are two major case interview styles: • Interviewer-led (McKinsey-type): Interviewer asks candidate questions in a logical sequence and …
Case Interview Preparation - Simon-Kucher
What is a case interview? Definition Who uses case interviews Format of case interviews Scenario-based job interviews that test problem solving …
