Advertisement
| data science case study 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 case study 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 case study interview: Ace the Data Science Interview Kevin Huo, Nick Singh, 2021 |
| data science case study 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 case study 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 case study 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 case study 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 case study 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 case study interview: Designing with Data Rochelle King, Elizabeth F Churchill, Caitlin Tan, 2017-03-29 On the surface, design practices and data science may not seem like obvious partners. But these disciplines actually work toward the same goal, helping designers and product managers understand users so they can craft elegant digital experiences. While data can enhance design, design can bring deeper meaning to data. This practical guide shows you how to conduct data-driven A/B testing for making design decisions on everything from small tweaks to large-scale UX concepts. Complete with real-world examples, this book shows you how to make data-driven design part of your product design workflow. Understand the relationship between data, business, and design Get a firm grounding in data, data types, and components of A/B testing Use an experimentation framework to define opportunities, formulate hypotheses, and test different options Create hypotheses that connect to key metrics and business goals Design proposed solutions for hypotheses that are most promising Interpret the results of an A/B test and determine your next move |
| data science case study interview: Data Science Projects with Python Stephen Klosterman, 2021-07-29 Gain hands-on experience of Python programming with industry-standard machine learning techniques using pandas, scikit-learn, and XGBoost Key FeaturesThink critically about data and use it to form and test a hypothesisChoose an appropriate machine learning model and train it on your dataCommunicate data-driven insights with confidence and clarityBook Description If data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable. In this book, you'll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you'll experience in real-world data science projects. You'll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest. Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world. By the end of this data science book, you'll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data. What you will learnLoad, explore, and process data using the pandas Python packageUse Matplotlib to create compelling data visualizationsImplement predictive machine learning models with scikit-learnUse lasso and ridge regression to reduce model overfittingEvaluate random forest and logistic regression model performanceDeliver business insights by presenting clear, convincing conclusionsWho this book is for Data Science Projects with Python – Second Edition is for anyone who wants to get started with data science and machine learning. If you're keen to advance your career by using data analysis and predictive modeling to generate business insights, then this book is the perfect place to begin. To quickly grasp the concepts covered, it is recommended that you have basic experience of programming with Python or another similar language, and a general interest in statistics. |
| data science case study 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 case study interview: Cracking The Machine Learning Interview Nitin Suri, 2018-12-18 A breakthrough in machine learning would be worth ten Microsofts. -Bill Gates Despite being one of the hottest disciplines in the Tech industry right now, Artificial Intelligence and Machine Learning remain a little elusive to most.The erratic availability of resources online makes it extremely challenging for us to delve deeper into these fields. Especially when gearing up for job interviews, most of us are at a loss due to the unavailability of a complete and uncondensed source of learning. Cracking the Machine Learning Interview Equips you with 225 of the best Machine Learning problems along with their solutions. Requires only a basic knowledge of fundamental mathematical and statistical concepts. Assists in learning the intricacies underlying Machine Learning concepts and algorithms suited to specific problems. Uniquely provides a manifold understanding of both statistical foundations and applied programming models for solving problems. Discusses key points and concrete tips for approaching real life system design problems and imparts the ability to apply them to your day to day work. This book covers all the major topics within Machine Learning which are frequently asked in the Interviews. These include: Supervised and Unsupervised Learning Classification and Regression Decision Trees Ensembles K-Nearest Neighbors Logistic Regression Support Vector Machines Neural Networks Regularization Clustering Dimensionality Reduction Feature Extraction Feature Engineering Model Evaluation Natural Language Processing Real life system design problems Mathematics and Statistics behind the Machine Learning Algorithms Various distributions and statistical tests This book can be used by students and professionals alike. It has been drafted in a way to benefit both, novices as well as individuals with substantial experience in Machine Learning. Following Cracking The Machine Learning Interview diligently would equip you to face any Machine Learning Interview. |
| data science case study interview: Be the Outlier Shrilata Murthy, 2020-07-27 According to LinkedIn's third annual U.S. Emerging Jobs Report, the data scientist role is ranked third among the top-15 emerging jobs in the U.S. Though the field of data science has been exploding, there didn't appear to be a comprehensive resource to help data scientists navigate the interview process... until now. In Be the Outlier: How to Ace Data Science Interviews, data scientist Shrilata Murthy covers all aspects of a data science interview in today's industry. Murthy combines her own experience in the job market with expert insight from data scientists with Google, Facebook, Amazon, NASA, Aetna, MBB & Big 4 consulting firms, and many more. In this book, you'll learn... the foundational knowledge that is key to any data science interview the 100-Word Story framework for writing a stellar resume what to expect from a variety of interview styles (take-home, presentation, case study, etc.), and actionable ways to differentiate yourself from your peers. By using real-world examples, practice questions, and sample interviews, Murthy has created an easy-to-follow guide that will help you crack any data science interview. After reading Be the Outlier, get ready to land your dream job in data science. |
| data science case study 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 case study 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 case study 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 case study 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 case study 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 case study 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 case study 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 case study 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 case study interview: Doing Interview Research Uwe Flick, 2021-10-13 If you want to use interview methods in your research project but are not sure where to start, this book will get you up and running. With hands-on advice for every stage of the social research process, it helps you succeed in every step, from understanding interview research through to designing and conducting your study and working with data. The book: Discusses eight methods of interviewing in-depth, including semi-structured interviews, narrative interviews, focus groups and online interviews. Features over 75 case studies of real interview research from across the globe, including Australia, Canada, Germany, Norway, the Philippines and South Africa. Spotlights strategies for conducting ethical, inclusive research, including indigenous research approaches. Packed not only with learning features - including learning objectives, checklists of questions to ask yourself at every stage of your project, practical exercises to help you put your learning into practice and further reading so you can broaden your knowledge - it is also supported by online resources such as annotated transcripts and videos of mock interviews to empower any social science student to use interview research methods with confidence. |
| data science case study interview: Data Science Projects with Python Stephen Klosterman, 2019-04-30 Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key FeaturesTackle data science problems by identifying the problem to be solvedIllustrate patterns in data using appropriate visualizationsImplement suitable machine learning algorithms to gain insights from dataBook Description Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You’ll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you’ll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions. By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data. What you will learnInstall the required packages to set up a data science coding environmentLoad data into a Jupyter notebook running PythonUse Matplotlib to create data visualizationsFit machine learning models using scikit-learnUse lasso and ridge regression to regularize your modelsCompare performance between models to find the best outcomesUse k-fold cross-validation to select model hyperparametersWho this book is for If you are a data analyst, data scientist, or business analyst who wants to get started using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of Python and data analytics will help you get the most from this book. Familiarity with mathematical concepts such as algebra and basic statistics will also be useful. |
| data science case study 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 case study 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 case study 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 case study interview: Fundamentals of Machine Learning for Predictive Data Analytics, second edition John D. Kelleher, Brian Mac Namee, Aoife D'Arcy, 2020-10-20 The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning. |
| data science case study 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 case study 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 case study 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 case study 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 case study interview: Algorithms Robert Sedgewick, Kevin Wayne, 2014-02-01 This book is Part I of the fourth edition of Robert Sedgewick and Kevin Wayne’s Algorithms, the leading textbook on algorithms today, widely used in colleges and universities worldwide. Part I contains Chapters 1 through 3 of the book. The fourth edition of Algorithms surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use. The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts. The companion web site, algs4.cs.princeton.edu contains An online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material The MOOC related to this book is accessible via the Online Course link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants. Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience. |
| data science case study interview: Reflective Interviewing Kathryn Roulston, 2010-01-21 Qualitative researchers have long made use of many different interview forms. Yet, for novice researchers, making the connections between theory and method is not always easy. This book provides a theoretically-informed guide for researchers learning how to interview in the social sciences. In order to undertake quality research using qualitative interviews, a researcher must be able to theorize the application of interviews to investigate research problems in social science research. As part of this process, researchers examine their subject positions in relation to participants, and examine their interview interactions systematically to inform research design. This book provides a practical approach to interviewing, helping researchers to learn about themselves as interviewers in ways that will inform the design, conduct, analysis and representation of interview data. The author takes the reader through the practicalities of designing and conducting an interview study, and relates various forms of interview to different underlying epistemological assumptions about how knowledge is produced. The book concludes with practical advice and perspectives from experienced researchers who use interviews as a method of data generation. This book is written for a multidisciplinary audience of students of qualitative research methods. |
| data science case study interview: 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 case study 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 case study 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 case study interview: Case Study Research in Software Engineering Per Runeson, Martin Host, Austen Rainer, Bjorn Regnell, 2012-03-07 Based on their own experiences of in-depth case studies of software projects in international corporations, in this book the authors present detailed practical guidelines on the preparation, conduct, design and reporting of case studies of software engineering. This is the first software engineering specific book on the case study research method. |
| data science case study 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 case study interview: The Patient History: Evidence-Based Approach Mark Henderson, Lawrence Tierney, Gerald Smetana, 2012-06-13 The definitive evidence-based introduction to patient history-taking NOW IN FULL COLOR For medical students and other health professions students, an accurate differential diagnosis starts with The Patient History. The ideal companion to major textbooks on the physical examination, this trusted guide is widely acclaimed for its skill-building, and evidence based approach to the medical history. Now in full color, The Patient History defines best practices for the patient interview, explaining how to effectively elicit information from the patient in order to generate an accurate differential diagnosis. The second edition features all-new chapters, case scenarios, and a wealth of diagnostic algorithms. Introductory chapters articulate the fundamental principles of medical interviewing. The book employs a rigorous evidenced-based approach, reviewing and highlighting relevant citations from the literature throughout each chapter. Features NEW! Case scenarios introduce each chapter and place history-taking principles in clinical context NEW! Self-assessment multiple choice Q&A conclude each chapter—an ideal review for students seeking to assess their retention of chapter material NEW! Full-color presentation Essential chapter on red eye, pruritus, and hair loss Symptom-based chapters covering 59 common symptoms and clinical presentations Diagnostic approach section after each chapter featuring color algorithms and several multiple-choice questions Hundreds of practical, high-yield questions to guide the history, ranging from basic queries to those appropriate for more experienced clinicians |
| data science case study 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. |
Prep Kit Rd. 1 Interview Candidate - DoorDash
The Round 1 Case Study pertains to areas of focus and assessment criteria. Case Focus: The interview will focus on DoorDash’s 3-sided Marketplace, and will require you to talk through a …
Data Science Interview Questions Statistics - Tanujit's Blog
Data Science: Q1. What is Data Science? List the differences between supervised and unsupervised learning. Data Science is a blend of various tools, algorithms, and machine …
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 …
Quant, FM, and Data Science Interview Compilation - LSU
Discuss algorithms for parallel matrix multiplication. Design a risk and asset pricing model for tech startup equity. Discuss ordinary least squares (OLS), maximum likelihood (MLE), and …
Ace The Data Science Interview - new.context.org
Ace The Data Science Interview Ace the Data Science Interview: A Data-Driven Guide to Success The data science field is booming, but competition is fierce. Landing a data science role ...
Demystifying Data Science Interviews - UC Berkeley School of …
What do the roles look like? Defines and monitors metrics. Provides narratives and trends. Builds ML models that power data products and features. Derives and uncovers relationship between …
DATA SCIENCE – ANALYTICS Onsite Interview Guide
Our Data Science team created an interview prep video (available here with or without captions) to help you understand what to expect during your onsite interview, and to provide you with …
Ace The Data Science Interview [PDF] - offsite.creighton.edu
You'll learn to write clean, efficient, and well-documented code, a critical aspect of a successful data science interview. The focus will be on demonstrating your ability to leverage these tools …
Data Analysis Case Studies - Data Action Lab
In Data Science and Data Analysis, as in most technical or quantitative fields of inquiry, there is an important distinction between understanding the theoretical underpinnings of the methods …
120 Data Science Interview Questions - epsiloneg.com
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 …
Ace The Data Science Interview - lms.sabt.edu.au
Acing a data science interview transcends memorizing algorithms; it's about demonstrating your ability to think critically, solve complex problems, and communicate your insights effectively.
Data Skills Training Program
The Data Science CoLab provides the opportunity for cohorts of 30-60 participants, including HHS employees as well as contractors, to learn basic and intermediate data skills. These data skills …
Interview Prep Guide
We are looking for data scientists who can tell a compelling story with data, make data-driven decisions, and impact change through product development and optimization. This guide will …
UNIT 5 OBSERVATION, INTERVIEW AND Qualitative Research …
As we can see in the above three examples, three different methods of data collection have been mentioned, namely, observation, interview and case study. All the three methods can be …
Ace The Data Science Interview Book - offsite.creighton.edu
Landing a data science job requires navigating a rigorous interview process that tests not only technical skills but also problem-solving abilities, communication, and cultural fit. This ebook, …
25 Important Data Science Interview Questions - AlgoTutor
Data science is the field that combines statistical analysis, machine learning, and programming to extract insights from data. 2. What are the key steps in the data science process? The key …
DATA SCIENCE INTERVIEW QUESTIONS AND - epsiloneg.com
data science interview questions and answers table of contents statistics • q1. what is the central limit theorem and why is it important? • q2. what is sampling? how many sampling methods do …
Case Study: Data Science Ethics Public Dialogue
framework is designed to enable data scientists and policymakers to maximise the potential for data science within government, whilst also managing potential legal and ethical questions of …
Interview Prep Guide
Whether you’re taking your initial screen or your full loop interview, our Data Science leaders and recruiters put together this guide so you know what to expect and how to prepare. We …
Qualitative Case Study Guidelines - DTIC
This report presents a set of guidelines for conducting qualitative case studies that are based on Yin’s case study process (comprising six interdependent stages) and additional principles from …
8 RESEARCH TOOLS-I1 INTERVIEW, OBSERVATION AND …
iii) An interview is an effective tool for a social scientist in the study of human Research Tools-11: behaviour. Through this technique., a researcher can secure very intimate and 1,te,~,,, …
The Bones of the System: A Case Study of Logging and …
on the emerging role of the data scientist, the study in this paper widens the lens to look at how data is a ecting all roles across the entire company, including the boundaries between roles. …
Sample Size for Interview in Qualitative Research in Social …
Dec 28, 2021 · shadowed data, the qualitative method and study design used. (Morse, 2000, p. 3) According to her study, there is also more issue to be considered upon which the decision can …
Goals, Process, and Challenges of Exploratory Data …
amining data values, derived statistics, and visualizations. Today, data exploration is widely adopted as a critical part of data science, both in industrial and scientific settings [36]. …
The Implications of Open Generative Models in Human …
The Implications of Open Generative Models in Human-Centered Data Science Work: A Case Study with Fact-Checking Organizations Robert Wolfe, Tanushree Mitra University of …
Data Analysis and Optimization for (Citi)Bike Sharing
Dec 28, 2020 · 3 Data Analytics for Bike Share Systems Our earliest goal for the collaboration with NYC Bike Share LLC was to make their planning and decision making data driven. …
Open Case Studies: Statistics and Data Science Education …
the case study 2. Main questions Scientific question(s) Introduction Describes context of scientific question and motivation 3. Learning objectives Both data science and statistics …
Case Studies in Clinical Psychological Science - Scott …
Library of Congress Cataloging-in-Publication Data Case studies in clinical psychological science : bridging the gap from science to practice / edited by William O’Donohue, Scott O. Lilienfeld. p. …
Process Tracing and Elite Interviewing: A Case for Non
(2005) Case Studies and Theory Devel opment in the Social Sciences. That book represents a robust defense of qual itative methodology, and case study re search in particular. Touching …
Data Engineering, Onsite Interview Guide
As a function, data engineering operates within the analytics organization alongside data science. This strong partnership enables us to build data assets which unlock the in-depth analysis we …
Cracking Data Science Interview - www2.internationalinsurance
Cracking the Data Science Interview: Your Ultimate Guide to Success Landing your dream data science role isn't just about possessing technical skills; it's about demonstrating them …
Case-Selection Techniques in Case Study Research: A Menu …
Department of Political Science 232 Bay State Road Boston, MA 02215 617-353-2756 jgerring@bu.edu ... summarized as case-study types -- “extreme,” “deviant,” “crucial,” “most …
Case Study Interview Protocol (CSIP) - InterPARES
documentation from or about the case study system or organization (i.e. website), or other identifiable sources. The Template for Analysis element data and data source information will …
Interview and Interview- Schedule in Social Research
Instead of an interview outline, which includes only a list of topics and subtopics, a typical interview schedule also contains major questions, as well as follow-up questions designed to …
McKinsey & Company - THE CONSULTING SOCIETY
problem (otherwise known as a “case”) with each interviewer, as this is a significant element of our recruitment process. Because many candidates are unfamiliar with this approach, this note …
What is a Case Study? - SAGE Publications Inc
sive design (e.g. a survey) or an intensive one (e.g. a case study). A definition of the case study is presented in section 1.5, and expanded upon in section 1.6. The popular point of view that a …
Doing Data Science: A Framework and Case Study
Feb 21, 2020 · Harvard Data Science Review • Issue 2.1, Winter 2020 Doing Data Science: A Framework and Case Study 5 2.1. Problem Identification Data science brings together …
Utilization of the United States Soft Power in Intimidating the …
with The Interview movie as case study of utilization of the. U.S. soft power to further its foreign policy against the North Korea by criticizing its autocracy through movie media. 2.
Data Collection methods (Questionnaire & Interview)
• We can introduce necessary changes in the interview schedule based on initial results (which is not possible in the case of a questionnaire study/survey). Disadvantages: • Data …
The Complete Collection of Data Science Cheat Sheets
learning and data science. Abid holds a master’s degree in Technology Management and a bachelor’s degree in Telecommunication Engineering. His vision is to build an AI product using …
Appendix 1: Semi-structured interview guide - BMJ Open
The interview will take place in person at a specific location or over the phone. Participants in the UK have the option of an in-person or teleconference interview, and all other participants will …
DATA SCIENTIST COURSE October 2020 - Crystal System
Data Science Case Study Project _____ 12 CERTIFICATES_____ 13 CRYSTAL TALENT SOLUTIONS EXECUTIVE TEAM_____ 14. 3 ABOUT THE COURSE Crystal System Data …
Redefining Case Study - SAGE Journals
data prescriptively using case study. Instead, researchers employ various research methods such ... intensive interview involves discussing how the social sciences generate new knowledge …
Analysing Interview Data (1) - The University of Warwick
Interview Data (1) Dr Maria de Hoyos & Dr Sally-Anne Barnes ... Building Theories from Case Study Research. Academy of Management Review, 14(4), 532-550. Glaser, B. G. (1993) The …
TORCH THE CASE - Case Interview
Case Book Design: Training Cases (Class of 2015 Case Authors): 2015 Case Competition New Case Authors: We would love to hear your suggestions, corrections and questions. Please …
Case Study: Data Science Ethics Public Dialogue
Case Study: Data Science Ethics Public Dialogue In December 2015 the Government Digital Service (GDS), supported by Sciencewise, ... 1 Interview with Senior Civil Servant in August …
Case study as a research method - CORE
By including both quantitative and qualitative data, case study helps explain both the process and outcome of a phenomenon through complete observation, reconstruction and analysis of the …
When interviewing: how many is enough? - ed
case study work where one would typically interview every member of a case, or in situations where it is both desirable and feasible to interview all target population members. However, in …
DATA PRIVACY AND CONFIDENTIALITY CASE STUDY …
and tribes can use to encourage data sharing while protecting data privacy and confidentiality. [Entity] has been selected as a case study site for this project. The case study will focus on the …
www.riebbs.ac.in
Research/Data Science/Decision Science/ Data Analysis/Mathematics Computing with minimum 55% marks for General and 50% in case of SC/ST/PH. Desirable Qualification: Basic …
Analyzing and Interpreting Qualitative Data: After the …
Interview is explicitly designed to help researchers choose appropriate methods, practices, and strategies for qualitative data analysis and interpretation. Each chapter will be based on an …
DATA SCIENCE INTERVIEW QUESTIONS AND - epsiloneg.com
data science interview questions and answers table of contents statistics • q1. what is the central limit theorem and why is it important? • q2. what is sampling?how many sampling methods do …
Four Steps to Analyse Data from a Case Study Method
only way case study data can be analysed (Barry, 1998) and it is recommended that they be used in conjunction with the overall case study design frameworks proposed by Yin (1994); and …
A Collection Of Advanced Data Science And Machine …
A Collection Of Advanced Data Science And Machine Learning Interview Questions Solved In Python And Spark Ii Hands On Big Data And Machine Programming ... ancient strategy game …
The Case Study Crisis: Some Answers - JSTOR
spectre of a case study crisis, and these are the questions that require some answers. The purpose of this reply is to reaffirm the role of the case study as a systematic research tool. …
What is a Case Study? - SAGE Publications Inc
sive design (e.g. a survey) or an intensive one (e.g. a case study). A definition of the case study is presented in section 1.5, and expanded upon in section 1.6. The popular point of view that a …
Enhancing students’ interest in science and …
This case study involved 16 students aged 13–15 years old, who participated in a classroom intervention consisting ... interview data were analysed through content analysis. The findings …
Case Study: Elaine - human-solutions.net
Data from a Career Style Interview also clearly manifest a client’s vocational personality type and career adaptability. In several publications I have written about how to use the Career Style …
Nurturing A Data-first Culture In A Multinational BFSI …
data science has in improving customer experience, optimizing risk management, bettering product development and positioning, competitive advantage and more, the company was …
Comparing the Five Approaches - SAGE Publications Inc
raphy, more interviews in grounded theory) and extent of data collection (e.g., only interviews in phenomenology, multiple forms in case study research to provide the in-depth case picture). …
DATA SCIENCE CASE STUDY
ABOUT MOSAIC DATA SCIENCE mosaicdatascience.com info@mosaicdatascience.com (866) 202-8600 We provide innovative machine learning, AI and analytics consulting across …
DSML Brochure 2024 - InterviewBit
Scaler Data Science and Machine Learning Freshers, tech and 7-18 months non-tech professionals The Learning Experience 1:1 Industry mentorship Practical application-based …
CASE STUDY RESEARCH IN SOFTWARE ENGINEERING
8.4 The Economics of Case Study Research 118 8.4.1 Costs and Benefits of Evaluation Techniques 119 8.4.2 Evaluation of the DESMET Methodology 119 8.4.3 Frameworks for …
Asking the Right Question: Qualitative Research Design …
To quantify data and generalize results from a sample to the population of interest Sometimes followed by qualitative research to explore findings further Sample Small, focused Larger …
INFLUENCER POLITICAL ENDORSEMENTS: GOOD BUSINESS?
A CASE STUDY OF THE 2024 PRESIDENTIAL ELECTION Yen-Hao Chen1, David ... Interview Guide Design: Developing an interview guide to direct participants in sharing their ... The …
Case in Point: Complete Case Interview Preparation
on the skills you'll need to walk into the case interview well prepared and confident, developed by Marc P. Cosentino, author of Case in Point: Complete Case Interview Preparation Intro to …
Data collection instruments (questionnaire and interview)
Data Collection Instruments • Accurate and systematic data collection is critical to conducting scientific research. • Data collection allows us to collect information that we want to collect …
