Data Science For Product Managers Course

  data science for product managers course: 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 for product managers course: 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 for product managers course: 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 for product managers course: 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 for product managers course: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.
  data science for product managers course: 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 for product managers course: 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 for product managers course: 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 for product managers course: 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 for product managers course: Ethics and Data Science Mike Loukides, Hilary Mason, DJ Patil, 2018-07-25 As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C’s) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today.
  data science for product managers course: 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 for product managers course: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
  data science for product managers course: INSPIRED Marty Cagan, 2017-11-17 How do today’s most successful tech companies—Amazon, Google, Facebook, Netflix, Tesla—design, develop, and deploy the products that have earned the love of literally billions of people around the world? Perhaps surprisingly, they do it very differently than the vast majority of tech companies. In INSPIRED, technology product management thought leader Marty Cagan provides readers with a master class in how to structure and staff a vibrant and successful product organization, and how to discover and deliver technology products that your customers will love—and that will work for your business. With sections on assembling the right people and skillsets, discovering the right product, embracing an effective yet lightweight process, and creating a strong product culture, readers can take the information they learn and immediately leverage it within their own organizations—dramatically improving their own product efforts. Whether you’re an early stage startup working to get to product/market fit, or a growth-stage company working to scale your product organization, or a large, long-established company trying to regain your ability to consistently deliver new value for your customers, INSPIRED will take you and your product organization to a new level of customer engagement, consistent innovation, and business success. Filled with the author’s own personal stories—and profiles of some of today’s most-successful product managers and technology-powered product companies, including Adobe, Apple, BBC, Google, Microsoft, and Netflix—INSPIRED will show you how to turn up the dial of your own product efforts, creating technology products your customers love. The first edition of INSPIRED, published ten years ago, established itself as the primary reference for technology product managers, and can be found on the shelves of nearly every successful technology product company worldwide. This thoroughly updated second edition shares the same objective of being the most valuable resource for technology product managers, yet it is completely new—sharing the latest practices and techniques of today’s most-successful tech product companies, and the men and women behind every great product.
  data science for product managers course: 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 for product managers course: 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 for product managers course: Hooked Nir Eyal, 2014-11-04 Revised and Updated, Featuring a New Case Study How do successful companies create products people can’t put down? Why do some products capture widespread attention while others flop? What makes us engage with certain products out of sheer habit? Is there a pattern underlying how technologies hook us? Nir Eyal answers these questions (and many more) by explaining the Hook Model—a four-step process embedded into the products of many successful companies to subtly encourage customer behavior. Through consecutive “hook cycles,” these products reach their ultimate goal of bringing users back again and again without depending on costly advertising or aggressive messaging. Hooked is based on Eyal’s years of research, consulting, and practical experience. He wrote the book he wished had been available to him as a start-up founder—not abstract theory, but a how-to guide for building better products. Hooked is written for product managers, designers, marketers, start-up founders, and anyone who seeks to understand how products influence our behavior. Eyal provides readers with: • Practical insights to create user habits that stick. • Actionable steps for building products people love. • Fascinating examples from the iPhone to Twitter, Pinterest to the Bible App, and many other habit-forming products.
  data science for product managers course: Foundations of Machine Learning, second edition Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, 2018-12-25 A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
  data science for product managers course: 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 for product managers course: 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 for product managers course: 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 for product managers course: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students.
  data science for product managers course: 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 for product managers course: Product Management in Practice Matt LeMay, 2017-11-08 Product management has become a critical connective role for modern organizations, from small technology startups to global corporate enterprises. And yet the day-to-day work of product management remains largely misunderstood. In theory, product management is about building products that people love. The real-world practice of product management is often about difficult conversations, practical compromises, and hard-won incremental gains. In this book, author Matt LeMay focuses on the CORE connective skills— communication, organization, research, execution—that can build a successful product management practice across industries, organizations, teams, andtoolsets. For current and aspiring product managers, this book explores:? On-the-ground tactics for facilitating collaboration and communication? How to talk to users and work with executives? The importance of setting clear and actionable goals? Using roadmaps to connect and align your team? A values-first approach to implementing Agile practices? Common behavioral traps that turn good product managers bad
  data science for product managers course: Law and Policy for the Quantum Age Chris Jay Hoofnagle, Simson L. Garfinkel, 2022-01-06 The Quantum Age cuts through the hype to demystify quantum technologies, their development paths, and the policy issues they raise.
  data science for product managers course: Learning How to Learn Barbara Oakley, PhD, Terrence Sejnowski, PhD, Alistair McConville, 2018-08-07 A surprisingly simple way for students to master any subject--based on one of the world's most popular online courses and the bestselling book A Mind for Numbers A Mind for Numbers and its wildly popular online companion course Learning How to Learn have empowered more than two million learners of all ages from around the world to master subjects that they once struggled with. Fans often wish they'd discovered these learning strategies earlier and ask how they can help their kids master these skills as well. Now in this new book for kids and teens, the authors reveal how to make the most of time spent studying. We all have the tools to learn what might not seem to come naturally to us at first--the secret is to understand how the brain works so we can unlock its power. This book explains: Why sometimes letting your mind wander is an important part of the learning process How to avoid rut think in order to think outside the box Why having a poor memory can be a good thing The value of metaphors in developing understanding A simple, yet powerful, way to stop procrastinating Filled with illustrations, application questions, and exercises, this book makes learning easy and fun.
  data science for product managers course: Financial Risk Management Allan M. Malz, 2011-09-13 Financial risk has become a focus of financial and nonfinancial firms, individuals, and policy makers. But the study of risk remains a relatively new discipline in finance and continues to be refined. The financial market crisis that began in 2007 has highlighted the challenges of managing financial risk. Now, in Financial Risk Management, author Allan Malz addresses the essential issues surrounding this discipline, sharing his extensive career experiences as a risk researcher, risk manager, and central banker. The book includes standard risk measurement models as well as alternative models that address options, structured credit risks, and the real-world complexities or risk modeling, and provides the institutional and historical background on financial innovation, liquidity, leverage, and financial crises that is crucial to practitioners and students of finance for understanding the world today. Financial Risk Management is equally suitable for firm risk managers, economists, and policy makers seeking grounding in the subject. This timely guide skillfully surveys the landscape of financial risk and the financial developments of recent decades that culminated in the crisis. The book provides a comprehensive overview of the different types of financial risk we face, as well as the techniques used to measure and manage them. Topics covered include: Market risk, from Value-at-Risk (VaR) to risk models for options Credit risk, from portfolio credit risk to structured credit products Model risk and validation Risk capital and stress testing Liquidity risk, leverage, systemic risk, and the forms they take Financial crises, historical and current, their causes and characteristics Financial regulation and its evolution in the wake of the global crisis And much more Combining the more model-oriented approach of risk management-as it has evolved over the past two decades-with an economist's approach to the same issues, Financial Risk Management is the essential guide to the subject for today's complex world.
  data science for product managers course: Digital Product Management Sascha Hoffmann,
  data science for product managers course: 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 for product managers course: Introducing MLOps Mark Treveil, Nicolas Omont, Clément Stenac, Kenji Lefevre, Du Phan, Joachim Zentici, Adrien Lavoillotte, Makoto Miyazaki, Lynn Heidmann, 2020-11-30 More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized
  data science for product managers course: 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 for product managers course: Information Dashboard Design Stephen Few, 2006 Dashboards have become popular in recent years as uniquely powerful tools for communicating important information at a glance. Although dashboards are potentially powerful, this potential is rarely realized. The greatest display technology in the world won't solve this if you fail to use effective visual design. And if a dashboard fails to tell you precisely what you need to know in an instant, you'll never use it, even if it's filled with cute gauges, meters, and traffic lights. Don't let your investment in dashboard technology go to waste. This book will teach you the visual design skills you need to create dashboards that communicate clearly, rapidly, and compellingly. Information Dashboard Design will explain how to: Avoid the thirteen mistakes common to dashboard design Provide viewers with the information they need quickly and clearly Apply what we now know about visual perception to the visual presentation of information Minimize distractions, cliches, and unnecessary embellishments that create confusion Organize business information to support meaning and usability Create an aesthetically pleasing viewing experience Maintain consistency of design to provide accurate interpretation Optimize the power of dashboard technology by pairing it with visual effectiveness Stephen Few has over 20 years of experience as an IT innovator, consultant, and educator. As Principal of the consultancy Perceptual Edge, Stephen focuses on data visualization for analyzing and communicating quantitative business information. He provides consulting and training services, speaks frequently at conferences, and teaches in the MBA program at the University ofCalifornia in Berkeley. He is also the author of Show Me the Numbers: Designing Tables and Graphs to Enlighten. Visit his website at www.perceptualedge.com.
  data science for product managers course: Data Science on AWS Chris Fregly, Antje Barth, 2021-04-07 With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
  data science for product managers course: Data Science for Business and Decision Making Luiz Paulo Favero, Patricia Belfiore, 2019-04-11 Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®. - Combines statistics and operations research modeling to teach the principles of business analytics - Written for students who want to apply statistics, optimization and multivariate modeling to gain competitive advantages in business - Shows how powerful software packages, such as SPSS and Stata, can create graphical and numerical outputs
  data science for product managers course: Data Science Bookcamp Leonard Apeltsin, 2021-12-07 Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science. In Data Science Bookcamp you will learn: - Techniques for computing and plotting probabilities - Statistical analysis using Scipy - How to organize datasets with clustering algorithms - How to visualize complex multi-variable datasets - How to train a decision tree machine learning algorithm In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data. About the book Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results. What's inside - Web scraping - Organize datasets with clustering algorithms - Visualize complex multi-variable datasets - Train a decision tree machine learning algorithm About the reader For readers who know the basics of Python. No prior data science or machine learning skills required. About the author Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. Table of Contents CASE STUDY 1 FINDING THE WINNING STRATEGY IN A CARD GAME 1 Computing probabilities using Python 2 Plotting probabilities using Matplotlib 3 Running random simulations in NumPy 4 Case study 1 solution CASE STUDY 2 ASSESSING ONLINE AD CLICKS FOR SIGNIFICANCE 5 Basic probability and statistical analysis using SciPy 6 Making predictions using the central limit theorem and SciPy 7 Statistical hypothesis testing 8 Analyzing tables using Pandas 9 Case study 2 solution CASE STUDY 3 TRACKING DISEASE OUTBREAKS USING NEWS HEADLINES 10 Clustering data into groups 11 Geographic location visualization and analysis 12 Case study 3 solution CASE STUDY 4 USING ONLINE JOB POSTINGS TO IMPROVE YOUR DATA SCIENCE RESUME 13 Measuring text similarities 14 Dimension reduction of matrix data 15 NLP analysis of large text datasets 16 Extracting text from web pages 17 Case study 4 solution CASE STUDY 5 PREDICTING FUTURE FRIENDSHIPS FROM SOCIAL NETWORK DATA 18 An introduction to graph theory and network analysis 19 Dynamic graph theory techniques for node ranking and social network analysis 20 Network-driven supervised machine learning 21 Training linear classifiers with logistic regression 22 Training nonlinear classifiers with decision tree techniques 23 Case study 5 solution
  data science for product managers course: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.
  data science for product managers course: Product-Led Onboarding Ramli John, 2021-06-04 When you borrow a plate from grandma, does she ask you to pay a deposit? Of course not. Likewise, blocking your non-paying (freemium) customers from the core experience of your product, is like chopping your own leg off while running a marathon. Yet, this is just one of the crucial mistakes that most SaaS companies make right off the bat. Think about it. Do YOU have... Stalled accounts taking up valuable space? Sub-par clients who only expect freebies and don't ever use the full features of your product? Low conversion from free accounts to paid? Then, you might have a shot-yourself-in-the-foot problem. In this book, you'll find the easy, 6-step formula you can apply to your operations today that can change absolutely everything. You'll be able to count your company among giants like Mixpanel, Ubisoft, and Outsystems when you: Captivate clients' attention from the get-go. Make it easier for clients to get good at using your software so they are more likely to use it. Create a fool-proof checklist to make your product go viral. Match services with behaviors, and get users addicted to your product. Win rave reviews by making clients feel like VIPs. Use this strategy at each level in your team to supercharge its effect. Rinse and repeat, and watch your business grow while you sleep. In short, you'll discover why putting your customer first is the ultimate secret to growing your company. And how you can achieve astronomical conversions and customer loyalty without even trying. Check out what others are saying:
  data science for product managers course: 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 for product managers course: 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 for product managers course: The Secret Product Manager Handbook Nils Davis, 2018-03-05 Product management isn't about you and it isn't about your product. It's about solving problems for your customers, creating a solution, and taking it to market. When I started in product management, I had a lot of questions, like What is product management? It's a common question still, but most people don't have a good answer. After all these years, the same questions keep coming up. I see them on forums, I hear them when I talk to new and experienced product managers, and I still do not see them being answered well or usefully. So I wrote this book, with the answers to the questions I always had. You'll learn: The real reason people choose to buy a product - it's not about how good the product is! How to get the very best from your developers. The 5-word phrase that can accelerate sales and marketing. The best ways to talk to executives and customers about what you're building. Among other critical information, you'll find a powerful framework for thinking about product management - and even for talking to your Mom about what you do. The framework provides an infrastructure for most of The Secret Product Manager Handbook. I provide a concrete and explicit explanation of why product management is so important for businesses, including a calculation of the true business value of product management. And the book is full of specific techniques and practices for transforming your product management career. What People Are Saying Nuggets of product management wisdom and ideas you'll want to hang on your monitor. The book is like having a conversation with a mentor. (Ken Hanson, Growth Product Manager) The summary of product management - identify market problems, guide the creation of solutions, and take the solutions to market - is powerful. As a former engineer, it's especially important to be reminded of the third point (Frank Licea, Product Manager) The intro is one of the clearest and smartest explanations of the value a product manager should bring to the table I've ever read. (Luca Candela, VP of Product Management)
  data science for product managers course: Pragmatic AI Noah Gift, 2018-07-12 Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Data Product Manager
Leverage market data to amplify product development. Learn how to apply data science techniques, data engineering processes, and market experimentation tests to deliver …

for Managers Data Science and Artificial Intelligence
to equip professionals and aspiring data scientists with the skills and knowledge required to excel in the rapidly evolving fields of data science and artificial intelligence (AI). The programme is …

Data Science for Managers
This one day course is aimed at providing an overview of Data Science to managers and executives. Data Science projects are di erent from typical software engineering projects, and …

CERTIFICATE PROGRAMME IN DATA SCIENCE & MACHINE …
Become industry-ready with an in-depth understanding of in-demand data science and machine learning tools and techniques with Python. WHO IS THIS PROGRAMME FOR? The …

PRODUCT MANAGEMENT PRODUCT MARKETING PRODUCT …
Founded in 1993, Pragmatic Institute is the world’s leading authority on product management, product marketing and data science. The company’s courses—taught by accomplished …

Executive Program in Data Driven Product Management - IIT …
Unlock potential from Data with the Executive Program in Data Driven Product Management Program. Ideal for emerging leaders and ambitious professionals. Gain a profound …

Data Product Manager
roducts but create completely new ones. Understand the role of data product managers within organizations and how they utilize data science, machine learning, and ar. ificial intelligence to …

Introduction to AI and ML - Walmart
This program for Product managers, Program managers and tech consultants teaches the business aspects that are important for solving and implementing data science solutions.

Data Science For Business Syllabus - Harvard Online
Module 1 The Data Science Shift Carvana: Good Data and Bad Buys • Apply the steps of the Data Driven Decision Framework •Identify the benefits that data science brings to business …

CERTIFICATE PROGRAMME IN DATA SCIENCE - Indian …
To help you learn the skills required to be successful in a data-driven world, IIM Kozhikode has launched the Certificate Programme in Data Science. This programme is designed for …

Practical Machine Learning and Data Science or Managers
Learning and Data Science for Managers provides hands-on experience build-ing and implementing data science projects. Upon course completion, you’ll earn a Digital Badge that …

Advance Certification In Product Management with - IIT …
Sessions 25-28 Basics of Data Science; Importance for PMs. Sessions 29-32 Python for Data Analysis; Introduction and Practical Exercises. Sessions 33-36 Utilizing Pandas and Numpy …

Executive Program in Product Management - IIT Guwahati
Make Data-Driven Decisions Utilize data analysis and metrics to inform product decisions, track product performance, and iterate on product features Build User-Centric Mindset Learn how to …

for Managers Data Science and Artificial Intelligence
Data Science and AI in today's World. Data Cleaning and Preparation. Module 3: Data Modelling. Module 4: Introduction to Data Analytics. Module 5: Data Visualization. Module 6: Data …

DATA SCIENCE AND ARTIFICIAL INTELLIGENCE - LEADERSHIP …
programme on Data Science and Artificial Intelligence - Leadership Essentials. This is a 2 month course that aims to give a high level overview of machine learning/deep learning along with …

Advanced Data Analytics for Managers Professional Certificate …
The Advanced Data Analytics for Managers programme at the IIMK is a thorough and specialised training programme created to give managers and professionals the knowledge and abilities …

Certificate in Data Analytics - IIT Guwahati
Module 1 - Data Science Funda-mentals • Thought Experiment: Data Science from a layman’s perspective • Brief intro to Data Science • How companies use Data Science • Overview of …

DATA SCIENCE, MACHINE LEARNING AND ARTIFICIAL …
IIM Kozhikode’s Data Science, Machine Learning and Artificial Intelligence programme responds to the growing need for skilled data science professionals who can leverage insights from data …

ADVANCED DATA ANALYTICS FOR MANAGERS - Indian …
IIM Kozhikode’s Professional Certificate Programme in Advanced Data Analytics for Managers imparts in-depth skills to master analytics, data science and machine learning. Its high-impact …

ADVANCED DATA SCIENCE FOR MANAGERS - Indian Institute …
IIM Kozhikode's Advanced Data Science for Managers programme imparts in-depth skills to master data science techniques and machine learning algorithms. Its high-impact learning …

Data Product Manager
Leverage market data to amplify product development. Learn how to apply data science techniques, data engineering processes, and market experimentation tests to deliver …

for Managers Data Science and Artificial Intelligence
to equip professionals and aspiring data scientists with the skills and knowledge required to excel in the rapidly evolving fields of data science and artificial intelligence (AI). The programme is …

Data Science for Managers
This one day course is aimed at providing an overview of Data Science to managers and executives. Data Science projects are di erent from typical software engineering projects, and …

CERTIFICATE PROGRAMME IN DATA SCIENCE
Become industry-ready with an in-depth understanding of in-demand data science and machine learning tools and techniques with Python. WHO IS THIS PROGRAMME FOR? The …

PRODUCT MANAGEMENT PRODUCT MARKETING …
Founded in 1993, Pragmatic Institute is the world’s leading authority on product management, product marketing and data science. The company’s courses—taught by accomplished …

Executive Program in Data Driven Product Management - IIT …
Unlock potential from Data with the Executive Program in Data Driven Product Management Program. Ideal for emerging leaders and ambitious professionals. Gain a profound …

Data Product Manager
roducts but create completely new ones. Understand the role of data product managers within organizations and how they utilize data science, machine learning, and ar. ificial intelligence to …

Introduction to AI and ML - Walmart
This program for Product managers, Program managers and tech consultants teaches the business aspects that are important for solving and implementing data science solutions.

Data Science For Business Syllabus - Harvard Online
Module 1 The Data Science Shift Carvana: Good Data and Bad Buys • Apply the steps of the Data Driven Decision Framework •Identify the benefits that data science brings to business …

CERTIFICATE PROGRAMME IN DATA SCIENCE - Indian …
To help you learn the skills required to be successful in a data-driven world, IIM Kozhikode has launched the Certificate Programme in Data Science. This programme is designed for …

Practical Machine Learning and Data Science or Managers
Learning and Data Science for Managers provides hands-on experience build-ing and implementing data science projects. Upon course completion, you’ll earn a Digital Badge that …

Advance Certification In Product Management with - IIT …
Sessions 25-28 Basics of Data Science; Importance for PMs. Sessions 29-32 Python for Data Analysis; Introduction and Practical Exercises. Sessions 33-36 Utilizing Pandas and Numpy …

Executive Program in Product Management - IIT Guwahati
Make Data-Driven Decisions Utilize data analysis and metrics to inform product decisions, track product performance, and iterate on product features Build User-Centric Mindset Learn how to …

for Managers Data Science and Artificial Intelligence
Data Science and AI in today's World. Data Cleaning and Preparation. Module 3: Data Modelling. Module 4: Introduction to Data Analytics. Module 5: Data Visualization. Module 6: Data …

DATA SCIENCE AND ARTIFICIAL INTELLIGENCE
programme on Data Science and Artificial Intelligence - Leadership Essentials. This is a 2 month course that aims to give a high level overview of machine learning/deep learning along with …

Advanced Data Analytics for Managers Professional …
The Advanced Data Analytics for Managers programme at the IIMK is a thorough and specialised training programme created to give managers and professionals the knowledge and abilities …

Certificate in Data Analytics - IIT Guwahati
Module 1 - Data Science Funda-mentals • Thought Experiment: Data Science from a layman’s perspective • Brief intro to Data Science • How companies use Data Science • Overview of …

DATA SCIENCE, MACHINE LEARNING AND ARTIFICIAL …
IIM Kozhikode’s Data Science, Machine Learning and Artificial Intelligence programme responds to the growing need for skilled data science professionals who can leverage insights from data …

ADVANCED DATA ANALYTICS FOR MANAGERS - Indian …
IIM Kozhikode’s Professional Certificate Programme in Advanced Data Analytics for Managers imparts in-depth skills to master analytics, data science and machine learning. Its high-impact …

ADVANCED DATA SCIENCE FOR MANAGERS - Indian …
IIM Kozhikode's Advanced Data Science for Managers programme imparts in-depth skills to master data science techniques and machine learning algorithms. Its high-impact learning …