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| data science product manager: 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 product manager: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| data science product manager: Cracking the PM Career Jackie Bavaro, Gayle Laakmann McDowell, 2022-04 Product management is a big role, and this is a big book. This comprehensive guide teaches new PMs and experienced PMs the skills, frameworks, and practices to become great product managers. ?Product skills: Drive better product decisions by conducting user research, performing data analysis, prototyping, writing product docs, and understanding technology.?Execution skills: Run your team well and deliver your projects quickly, smoothly, and effectively with project management, incremental development, launch processes, and good time management.?Strategic skills: Set a better direction for your team and optimize for long-term impact with vision, strategy, roadmapping, and team goals. Learn what it means to be more strategic.?Leadership skills: Lead more effectively by developing your personal mindset, collaboration, communication, inspiration, and mentorship skills.?People management: Learn leadership skills for managers, including coaching, recruiting, interviewing, and creating organizational structures.?Careers: Navigate your career by understanding the career ladder, setting goals, and translating your accomplishments into advancement. |
| data science product manager: Product Analytics Joanne Rodrigues, 2020-08-27 Use Product Analytics to Understand Consumer Behavior and Change It at Scale Product Analytics is a complete, hands-on guide to generating actionable business insights from customer data. Experienced data scientist and enterprise manager Joanne Rodrigues introduces practical statistical techniques for determining why things happen and how to change what people do at scale. She complements these with powerful social science techniques for creating better theories, designing better metrics, and driving more rapid and sustained behavior change. Writing for entrepreneurs, product managers/marketers, and other business practitioners, Rodrigues teaches through intuitive examples from both web and offline environments. Avoiding math-heavy explanations, she guides you step by step through choosing the right techniques and algorithms for each application, running analyses in R, and getting answers you can trust. Develop core metrics and effective KPIs for user analytics in any web product Truly understand statistical inference, and the differences between correlation and causation Conduct more effective A/B tests Build intuitive predictive models to capture user behavior in products Use modern, quasi-experimental designs and statistical matching to tease out causal effects from observational data Improve response through uplift modeling and other sophisticated targeting methods Project business costs/subgroup population changes via advanced demographic projection Whatever your product or service, this guide can help you create precision-targeted marketing campaigns, improve consumer satisfaction and engagement, and grow revenue and profits. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| data science product manager: Building Products for the Enterprise Blair Reeves, Benjamin Gaines, 2018-03-09 If you’re new to software product management or just want to learn more about it, there’s plenty of advice available—but most of it is geared toward consumer products. Creating high-quality software for the enterprise involves a much different set of challenges. In this practical book, two expert product managers provide straightforward guidance for people looking to join the thriving enterprise market. Authors Blair Reeves and Benjamin Gaines explain critical differences between enterprise and consumer products, and deliver strategies for overcoming challenges when building for the enterprise. You’ll learn how to cultivate knowledge of your organization, the products you build, and the industry you serve. Explore why: Identifying customer vs user problems is an enterprise project manager’s main challenge Effective collaboration requires in-depth knowledge of the organization Analyzing data is key to understanding why users buy and retain your product Having experience in the industry you’re building products for is valuable Product longevity depends on knowing where the industry is headed |
| data science product manager: 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 product manager: Escaping the Build Trap Melissa Perri, 2018-11-01 To stay competitive in today’s market, organizations need to adopt a culture of customer-centric practices that focus on outcomes rather than outputs. Companies that live and die by outputs often fall into the build trap, cranking out features to meet their schedule rather than the customer’s needs. In this book, Melissa Perri explains how laying the foundation for great product management can help companies solve real customer problems while achieving business goals. By understanding how to communicate and collaborate within a company structure, you can create a product culture that benefits both the business and the customer. You’ll learn product management principles that can be applied to any organization, big or small. In five parts, this book explores: Why organizations ship features rather than cultivate the value those features represent How to set up a product organization that scales How product strategy connects a company’s vision and economic outcomes back to the product activities How to identify and pursue the right opportunities for producing value through an iterative product framework How to build a culture focused on successful outcomes over outputs |
| data science product manager: Managing Data Science Kirill Dubovikov, 2019-11-12 Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key FeaturesLearn the basics of data science and explore its possibilities and limitationsManage data science projects and assemble teams effectively even in the most challenging situationsUnderstand management principles and approaches for data science projects to streamline the innovation processBook Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learnUnderstand the underlying problems of building a strong data science pipelineExplore the different tools for building and deploying data science solutionsHire, grow, and sustain a data science teamManage data science projects through all stages, from prototype to productionLearn how to use ModelOps to improve your data science pipelinesGet up to speed with the model testing techniques used in both development and production stagesWho this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book. |
| data science product manager: 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 product manager: Data Smart John W. Foreman, 2013-10-31 Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the data scientist, toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know. |
| data science product manager: 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 product manager: Agile Data Science Russell Jurney, 2013-10-15 Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track |
| data science product manager: HBR Guide to Data Analytics Basics for Managers (HBR Guide Series) Harvard Business Review, 2018-03-13 Don't let a fear of numbers hold you back. Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. How do you get started? Whether you're working with data experts or running your own tests, you'll find answers in the HBR Guide to Data Analytics Basics for Managers. This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others. You'll learn how to: Identify the metrics you need to measure Run experiments and A/B tests Ask the right questions of your data experts Understand statistical terms and concepts Create effective charts and visualizations Avoid common mistakes |
| data science product manager: Building Data Science Teams DJ Patil, 2011-09-15 As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills, perspectives, tools and processes that position data science teams for success. Topics include: What it means to be data driven. The unique roles of data scientists. The four essential qualities of data scientists. Patil's first-hand experience building the LinkedIn data science team. |
| data science product manager: Product Leadership Richard Banfield, Martin Eriksson, Nate Walkingshaw, 2017-05-12 In today’s lightning-fast technology world, good product management is critical to maintaining a competitive advantage. Yet, managing human beings and navigating complex product roadmaps is no easy task, and it’s rare to find a product leader who can steward a digital product from concept to launch without a couple of major hiccups. Why do some product leaders succeed while others don’t? This insightful book presents interviews with nearly 100 leading product managers from all over the world. Authors Richard Banfield, Martin Eriksson, and Nate Walkingshaw draw on decades of experience in product design and development to capture the approaches, styles, insights, and techniques of successful product managers. If you want to understand what drives good product leaders, this book is an irreplaceable resource. In three parts, Product Leadership helps you explore: Themes and patterns of successful teams and their leaders, and ways to attain those characteristics Best approaches for guiding your product team through the startup, emerging, and enterprise stages of a company’s evolution Strategies and tactics for working with customers, agencies, partners, and external stakeholders |
| data science product manager: High Growth Handbook Elad Gil, 2018-07-17 High Growth Handbook is the playbook for growing your startup into a global brand. Global technology executive, serial entrepreneur, and angel investor Elad Gil has worked with high-growth tech companies including Airbnb, Twitter, Google, Stripe, and Square as they’ve grown from small companies into global enterprises. Across all of these breakout companies, Gil has identified a set of common patterns and created an accessible playbook for scaling high-growth startups, which he has now codified in High Growth Handbook. In this definitive guide, Gil covers key topics, including: · The role of the CEO · Managing a board · Recruiting and overseeing an executive team · Mergers and acquisitions · Initial public offerings · Late-stage funding. Informed by interviews with some of the biggest names in Silicon Valley, including Reid Hoffman (LinkedIn), Marc Andreessen (Andreessen Horowitz), and Aaron Levie (Box), High Growth Handbook presents crystal-clear guidance for navigating the most complex challenges that confront leaders and operators in high-growth startups. |
| data science product manager: 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 product manager: Agile Data Science 2.0 Russell Jurney, 2017-06-07 Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track |
| data science product manager: Data Science at the Command Line Jeroen Janssens, 2021-08-17 This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens provides a Docker image packed with over 100 Unix power tools--useful whether you work with Windows, macOS, or Linux. You'll quickly discover why the command line is an agile, scalable, and extensible technology. Even if you're comfortable processing data with Python or R, you'll learn how to greatly improve your data science workflow by leveraging the command line's power. This book is ideal for data scientists, analysts, engineers, system administrators, and researchers. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on text, CSV, HTML, XML, and JSON files Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow Create your own tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines Model data with dimensionality reduction, regression, and classification algorithms Leverage the command line from Python, Jupyter, R, RStudio, and Apache Spark |
| data science product manager: The Product Book: How to Become a Great Product Manager Product School, Josh Anon, 2017-05 Nobody asked you to show up. Every experienced product manager has heard some version of those words at some point in their career. Think about a company. Engineers build the product. Designers make sure it has a great user experience and looks good. Marketing makes sure customers know about the product. Sales get potential customers to open their wallets to buy the product. What more does a company need? What does a product manager do? Based upon Product School's curriculum, which has helped thousands of students become great product managers, The Product Book answers that question. Filled with practical advice, best practices, and expert tips, this book is here to help you succeed! |
| data science product manager: Designing Great Data Products Jeremy Howard, Margit Zwemer, Mike Loukides, 2012-03-23 In the past few years, we’ve seen many data products based on predictive modeling. These products range from weather forecasting to recommendation engines like Amazon's. Prediction technology can be interesting and mathematically elegant, but we need to take the next step: going from recommendations to products that can produce optimal strategies for meeting concrete business objectives. We already know how to build these products: they've been in use for the past decade or so, but they're not as common as they should be. This report shows how to take the next step: to go from simple predictions and recommendations to a new generation of data products with the potential to revolutionize entire industries. |
| data science product manager: 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 product manager: Seeing What Others Don't Gary Klein, 2013-06-25 Insights -- like Darwin's understanding of the way evolution actually works, and Watson and Crick's breakthrough discoveries about the structure of DNA -- can change the world. We also need insights into the everyday things that frustrate and confuse us so that we can more effectively solve problems and get things done. Yet we know very little about when, why, or how insights are formed -- or what blocks them. In Seeing What Others Don't, renowned cognitive psychologist Gary Klein unravels the mystery. Klein is a keen observer of people in their natural settings -- scientists, businesspeople, firefighters, police officers, soldiers, family members, friends, himself -- and uses a marvelous variety of stories to illuminate his research into what insights are and how they happen. What, for example, enabled Harry Markopolos to put the finger on Bernie Madoff? How did Dr. Michael Gottlieb make the connections between different patients that allowed him to publish the first announcement of the AIDS epidemic? What did Admiral Yamamoto see (and what did the Americans miss) in a 1940 British attack on the Italian fleet that enabled him to develop the strategy of attack at Pearl Harbor? How did a smokejumper see that setting another fire would save his life, while those who ignored his insight perished? How did Martin Chalfie come up with a million-dollar idea (and a Nobel Prize) for a natural flashlight that enabled researchers to look inside living organisms to watch biological processes in action? Klein also dissects impediments to insight, such as when organizations claim to value employee creativity and to encourage breakthroughs but in reality block disruptive ideas and prioritize avoidance of mistakes. Or when information technology systems are dumb by design and block potential discoveries. Both scientifically sophisticated and fun to read, Seeing What Others Don't shows that insight is not just a eureka! moment but a whole new way of understanding. |
| data science product manager: Lean Analytics Alistair Croll, Benjamin Yoskovitz, 2024-02-23 Whether you're a startup founder trying to disrupt an industry or an entrepreneur trying to provoke change from within, your biggest challenge is creating a product people actually want. Lean Analytics steers you in the right direction. This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and how to spread the word. Packed with more than thirty case studies and insights from over a hundred business experts, Lean Analytics provides you with hard-won, real-world information no entrepreneur can afford to go without. Understand Lean Startup, analytics fundamentals, and the data-driven mindset Look at six sample business models and how they map to new ventures of all sizes Find the One Metric That Matters to you Learn how to draw a line in the sand, so you'll know it's time to move forward Apply Lean Analytics principles to large enterprises and established products |
| data science product manager: The Manager's Path Camille Fournier, 2017-03-13 Managing people is difficult wherever you work. But in the tech industry, where management is also a technical discipline, the learning curve can be brutal—especially when there are few tools, texts, and frameworks to help you. In this practical guide, author Camille Fournier (tech lead turned CTO) takes you through each stage in the journey from engineer to technical manager. From mentoring interns to working with senior staff, you’ll get actionable advice for approaching various obstacles in your path. This book is ideal whether you’re a new manager, a mentor, or a more experienced leader looking for fresh advice. Pick up this book and learn how to become a better manager and leader in your organization. Begin by exploring what you expect from a manager Understand what it takes to be a good mentor, and a good tech lead Learn how to manage individual members while remaining focused on the entire team Understand how to manage yourself and avoid common pitfalls that challenge many leaders Manage multiple teams and learn how to manage managers Learn how to build and bootstrap a unifying culture in teams |
| data science product manager: Continuous Discovery Habits Teresa Torres, 2021-05-19 If you haven't had the good fortune to be coached by a strong leader or product coach, this book can help fill that gap and set you on the path to success. - Marty Cagan How do you know that you are making a product or service that your customers want? How do you ensure that you are improving it over time? How do you guarantee that your team is creating value for your customers in a way that creates value for your business? In this book, you'll learn a structured and sustainable approach to continuous discovery that will help you answer each of these questions, giving you the confidence to act while also preparing you to be wrong. You'll learn to balance action with doubt so that you can get started without being blindsided by what you don't get right. If you want to discover products that customers love-that also deliver business results-this book is for you. |
| data science product manager: 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 product manager: The Influential Product Manager Ken Sandy, 2020-01-14 This book is a comprehensive and practical guide to the core skills, activities, and behaviors that are required of product managers in modern technology companies. Product management is one of the fastest growing and most sought-after roles by job seekers and companies alike. The availability of trained and experienced talent can barely keep up with the accelerating demand for new and improved technology products. People from nontechnical and technical backgrounds alike are eager to master this exciting new role. The Influential Product Manager teaches product managers how to behave at each stage of the product life cycle to achieve the best outcome for the customer. Product managers are under pressure to drive spectacular results, often without wielding much direct power or authority. If you don't know how to influence people at all levels of the organization, how will you create the best possible product? This comprehensive entry-level textbook distills over twenty years of hard-won field experience and industry knowledge into lessons that will empower new product managers to act like pros right out of the gate. With teaching experience both from UC Berkeley and Lynda.com, the author boils down the most complex topics into principles that are easy to memorize and apply. This book methodically documents the tools product managers everywhere use to align their teams with market needs and organizational goals. From setting priorities to capturing requirements to navigating trade-offs, this book makes it easy. Not only will your product succeed, you'll succeed, too, when you read the final chapter on advancing your career. Let your product's success become your success! |
| data science product manager: User Story Mapping Jeff Patton, Peter Economy, 2014-09-05 User story mapping is a valuable tool for software development, once you understand why and how to use it. This insightful book examines how this often misunderstood technique can help your team stay focused on users and their needs without getting lost in the enthusiasm for individual product features. Author Jeff Patton shows you how changeable story maps enable your team to hold better conversations about the project throughout the development process. Your team will learn to come away with a shared understanding of what you’re attempting to build and why. Get a high-level view of story mapping, with an exercise to learn key concepts quickly Understand how stories really work, and how they come to life in Agile and Lean projects Dive into a story’s lifecycle, starting with opportunities and moving deeper into discovery Prepare your stories, pay attention while they’re built, and learn from those you convert to working software |
| data science product manager: Driving Digital Isaac Sacolick, 2017-08-24 Every organization makes plans for updating products, technologies, and business processes. But that’s not enough anymore for the twenty-first-century company. The race is now on for everyone to become a digital enterprise. For those individuals who have been charged with leading their company’s technology-driven change, the pressure is intense while the correct path forward unclear. Help has arrived! In Driving Digital, author Isaac Sacolick shares the lessons he’s learned over the years as he has successfully spearheaded multiple transformations and helped shape digital-business best practices. Readers no longer have to blindly trek through the mine field of their company’s digital transformation. In this thoroughly researched one-stop manual, learn how to: • Formulate a digital strategy • Transform business and IT practices • Align development and operations • Drive culture change • Bolster digital talent • Capture and track ROI • Develop innovative digital practices • Pilot emerging technologies • And more! Your company cannot avoid the digital disruption heading its way. The choice is yours: Will this mean the beginning of the end for your business, or will your digital practices be what catapults you into next-level success? |
| data science product manager: Engineering MLOps Emmanuel Raj, 2021-04-19 Get up and running with machine learning life cycle management and implement MLOps in your organization Key FeaturesBecome well-versed with MLOps techniques to monitor the quality of machine learning models in productionExplore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed modelsPerform CI/CD to automate new implementations in ML pipelinesBook Description Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production. The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you'll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You'll learn how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitor pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. Finally, you'll apply the knowledge you've gained to build real-world projects. By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization. What you will learnFormulate data governance strategies and pipelines for ML training and deploymentGet to grips with implementing ML pipelines, CI/CD pipelines, and ML monitoring pipelinesDesign a robust and scalable microservice and API for test and production environmentsCurate your custom CD processes for related use cases and organizationsMonitor ML models, including monitoring data drift, model drift, and application performanceBuild and maintain automated ML systemsWho this book is for This MLOps book is for data scientists, software engineers, DevOps engineers, machine learning engineers, and business and technology leaders who want to build, deploy, and maintain ML systems in production using MLOps principles and techniques. Basic knowledge of machine learning is necessary to get started with this book. |
| data science product manager: Swipe to Unlock Neel Mehta, Parth Detroja, Aditya Agashe, 2017 WANT A NON-CODING JOB AT A TECH COMPANY? Interested in product management, marketing, strategy, or business development? The tech industry is the place to be: nontechnical employees at tech companies outnumber their engineering counterparts almost 3 to 1 (Forbes, 2017). You might be worried that your lack of coding skills or tech industry knowledge will hold you back. But here's the secret: you don't need to learn how to code to break into the tech industry. Written by three former Microsoft PMs, Swipe to Unlock gives you a breakdown of the concepts you need to know to crush your interviews, like software development, big data, and internet security. We'll explain how Google's ad targeting algorithm works, but Google probably won't ask you how to explain it in a non-technical interview. But they might ask you how you could increase ad revenue from a particular market segment. And if you know how Google's ad platform works, you'll be in a far stronger position to come up with good growth strategies. We'll show you how Robinhood, an app that lets you trade stocks without commission, makes money by earning interest on the unspent money that users keep in their accounts. No one will ask you to explain this. But if someone asks you to come up with a new monetization strategy for Venmo (which lets you send and receive money without fees), you could pull out the Robinhood anecdote to propose that Venmo earn interest off the money sitting in users' accounts. We'll talk about some business cases like why Microsoft acquired LinkedIn. Microsoft interviewers probably won't ask you about the motive of the purchase, but they might ask you for ideas to improve Microsoft Outlook. From our case study, you'll learn how the Microsoft and LinkedIn ecosystems could work together, which can help you craft creative, impactful answers. You could propose that Outlook use LinkedIn's social graph to give salespeople insights about clients before meeting them. Or you could suggest linking Outlook's organizational tree to LinkedIn to let HR managers analyze their company's hierarchy and figure out what kind of talent they need to add. (We'll further explore both ideas in the book.) Either way, you're sure to impress. Learn the must know concepts of tech from authors who have received job offers for Facebook's Rotational Product Manager, Google's Associate Product Marketing Manager, and Microsoft's Program Manager to get a competitive edge at your interviews! |
| data science product manager: The Signal and the Noise Nate Silver, 2015-02-03 One of the more momentous books of the decade. —The New York Times Book Review Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of the website FiveThirtyEight. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future. In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball to global pandemics, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good—or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science. Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise. With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read. |
| data science product manager: 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 product manager: EMPOWERED Marty Cagan, 2020-12-03 Great teams are comprised of ordinary people that are empowered and inspired. They are empowered to solve hard problems in ways their customers love yet work for their business. They are inspired with ideas and techniques for quickly evaluating those ideas to discover solutions that work: they are valuable, usable, feasible and viable. This book is about the idea and reality of achieving extraordinary results from ordinary people. Empowered is the companion to Inspired. It addresses the other half of the problem of building tech products?how to get the absolute best work from your product teams. However, the book's message applies much more broadly than just to product teams. Inspired was aimed at product managers. Empowered is aimed at all levels of technology-powered organizations: founders and CEO's, leaders of product, technology and design, and the countless product managers, product designers and engineers that comprise the teams. This book will not just inspire companies to empower their employees but will teach them how. This book will help readers achieve the benefits of truly empowered teams-- |
| data science product manager: Sales Engagement Manny Medina, Max Altschuler, Mark Kosoglow, 2019-03-12 Engage in sales—the modern way Sales Engagement is how you engage and interact with your potential buyer to create connection, grab attention, and generate enough interest to create a buying opportunity. Sales Engagement details the modern way to build the top of the funnel and generate qualified leads for B2B companies. This book explores why a Sales Engagement strategy is so important, and walks you through the modern sales process to ensure you’re effectively connecting with customers every step of the way. • Find common factors holding your sales back—and reverse them through channel optimization • Humanize sales with personas and relevant information at every turn • Understand why A/B testing is so incredibly critical to success, and how to do it right • Take your sales process to the next level with a rock solid, modern Sales Engagement strategy This book is essential reading for anyone interested in up-leveling their game and doing more than they ever thought possible. |
| data science product manager: Impact Mapping Gojko Adzic, 2012-10 A practical guide to impact mapping, a simple yet incredibly effective method for collaborative strategic planning that helps organizations make an impact with software. |
| data science product manager: Winning with Data Tomasz Tunguz, Frank Bien, 2016-06-20 Crest the data wave with a deep cultural shift Winning with Data explores the cultural changes big data brings to business, and shows you how to adapt your organization to leverage data to maximum effect. Authors Tomasz Tunguz and Frank Bien draw on extensive background in big data, business intelligence, and business strategy to provide a blueprint for companies looking to move head-on into the data wave. Instrumentation is discussed in detail, but the core of the change is in the culture—this book provides sound guidance on building the type of organizational culture that creates and leverages data daily, in every aspect of the business. Real-world examples illustrate these important concepts at work: you'll learn how data helped Warby-Parker disrupt a $13 billion monopolized market, how ThredUp uses data to process more than 20 thousand items of clothing every day, how Venmo leverages data to build better products, how HubSpot empowers their salespeople to be more productive, and more. From decision making and strategy to shipping and sales, this book shows you how data makes better business. Big data has taken on buzzword status, but there is little real guidance for companies seeking everyday business data solutions. This book takes a deeper look at big data in business, and shows you how to shift internal culture ahead of the curve. Understand the changes a data culture brings to companies Instrument your company for maximum benefit Utilize data to optimize every aspect of your business Improve decision making and transform business strategy Big data is becoming the number-one topic in business, yet no one is asking the right questions. Leveraging the full power of data requires more than good IT—organization-wide buy-in is essential for long-term success. Winning with Data is the expert guide to making data work for your business, and your needs. |
| data science product manager: Numsense! Data Science for the Layman Annalyn Ng, 2017-03-24 Used in Stanford's CS102 Big Data (Spring 2017) course. Want to get started on data science? Our promise: no math added. This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations, as well as lots of visuals, all of which are colorblind-friendly. Popular concepts covered include: A/B Testing Anomaly Detection Association Rules Clustering Decision Trees and Random Forests Regression Analysis Social Network Analysis Neural Networks Features: Intuitive explanations and visuals Real-world applications to illustrate each algorithm Point summaries at the end of each chapter Reference sheets comparing the pros and cons of algorithms Glossary list of commonly-used terms With this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions. |
| data science product manager: Ace the Data Science Interview Kevin Huo, Nick Singh, 2021 |
Data and Digital Outputs Management Plan (DDOMP)
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