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Your learning outcomes are my #1 priority. That means answering your questions and providing additional context to help you succeed. You get access to the office hours for the duration of the course.
Meet Your Instructor:
A Globally Recognized Expert In Data Science & AI Product Strategy
Vin Vashishta has a 3-layered professional background: Technology, Leadership, and Strategy.
25 years in technology, starting in software engineering and moving into data science in 2012.
Strategist, first published in 2013, Vin has combined his technical and strategic expertise to build Data and AI Strategies for 10 of the largest companies in the world, 21 SMEs, and 3 startups.
2X Founder, leader, and organization builder. Clients bring Vin in as an interim CDO to build and lead their data organizations during crucial growth stages. He founded V-Squared in 2012 and built it into a successful AI Strategy consulting practice. In 2019, he expanded into education and launched High ROI Data Science.
Since 2014, Vin has been called a Data Science and Strategy expert by IBM, Intel, SAP, LinkedIn, and NVIDIA. He is a globally recognized thought leader, followed by Gartner, Walmart, Uber, Microsoft, Salesforce, MongoDB, Deloitte, and many others.
Vin has been teaching technical and non-technical audiences since 2007.
Course Content
- Course Support (3:26)
- Course Topics & A Lesson On Pricing Strategy (7:52)
- Content Overview (12:41)
- How You'll Learn (5:20)
- My Background (4:48)
- Breaking Bad Habits (3:47)
- Why Is Now The Right Time For AI Strategy & Products? (7:10)
- What Is An AI Product Manager? (4:39)
- Why Businesses Need AI Product Managers And New Approaches? (17:51)
- Slides
- Introduction To The Business, Operating, & Technology Models (6:23)
- Exploring The Technology Model (10:00)
- Continuous Transformation & Its Impact On Products (8:03)
- Steps That Launch & Derail AI Strategy (7:22)
- The Capabilities Maturity Framework & Progression (11:46)
- The Transition From Opacity To Transparency (10:09)
- The Product Maturity Model & Strategic Decisions (11:26)
- The Data Maturity Model (10:47)
- Opportunity Discovery Without The Frameworks (10:42)
- Slides
- The Big McDonalds Reveal (10:34)
- Ford & Being Distracted By Screens (5:58)
- Why Change & Adopt New Frameworks? (11:41)
- Why Did I Choose McDonalds & Ford? (7:02)
- Becoming Strategic Partners With CxOs (8:31)
- Top Down Opportunity Discovery Part 1 (15:10)
- Top Down Opportunity Discovery Part 2 (6:54)
- Bottom Up Opportunity Discovery Part 1 (11:15)
- Bottom Up Opportunity Discovery Part 2 (7:08)
- Breaking Initiatives Down (21:57)
- McDonalds Top Down Opportunity Discovery Exercise (7:41)
- McDonalds Top Down Opportunity Discovery Exercise Answer (14:41)
- Ford Top Down Opportunity Discovery Exercise Answer (10:55)
- Slides
- Introduction To The Maturity Models & Why We Need Them (8:07)
- The First 5 Phases & A New Approach To Product Design (13:12)
- Advanced Maturity Phases And New Business Types (18:05)
- The Data & AI Product Maturity Model (11:27)
- The Product Lifecycle Maturity Model (10:41)
- Breaking Down Initiatives & Delivering Value Incrementally (14:03)
- AI Product Design & The Human Machine Maturity Model (13:09)
- The Data Generation Maturity Model (13:34)
- Opportunity Estimation (10:18)
- Opportunity Estimation Exercise Copilot (9:32)
- Slides
- Introduction To The Product Platform Exercise Estimating ROI For The iPhone Camera (5:45)
- iPhone Exercise Answer Walk Through (9:32)
- Introduction To The Product Platform Mentorship Marketplace Exercise (3:37)
- The Mentorship Marketplace Exercise Answer (4:14)
- AI Product Examples TikTok (5:21)
- AI Product Examples Recruiter ATS (6:03)
- AI Product Examples Netflix Recommender (5:27)
- AI Product Examples Amazon Prime Pricing (5:27)
- AI Product Examples Citi Credit Card Fraud Detection (4:59)
- Product Platform Monetization Review (3:30)
- Slides
- Introduction To Pricing Strategy Basics (13:49)
- Introduction To AI Pricing Strategy (11:38)
- Do Subscriptions Still Make Sense? (10:45)
- GPT-4 vs GPT-3.5 Pricing Exercise (2:16)
- GPT-4 vs GPT-3.5 Pricing Answer And Copilot Pricing Exercise (4:59)
- Copilot Pricing Exercise Answer (5:41)
- Data Product Pricing Strategy (12:25)
- Amazon Textract Pricing Exercise (5:27)
- Amazon Textract Pricing Exercise Answer (7:15)
- The 3 Body Problem (9:56)
- The Dangers Of Discounting (8:12)
- Slides
- Use Case Assessment Points (9:13)
- AI Product Platform HRTech Exercise Learn From My Mistakes (10:54)
- Problem, Data, & Solution Space Definition (11:42)
- How Platforms Were Built (12:23)
- How Platforms Were Built Exercise Answer (7:09)
- Blurring The Lines Between Products And Operations (6:15)
- Breaking Initiatives Down And Meeting The Business Where It Is (10:39)
- Introduction To T Shaped Platforms (5:59)
- From Opportunities To Roadmaps (9:53)
- Slides
- Introduction (5:55)
- How Are Operating Platforms Monetized? (8:40)
- Operating Platform As A Product (8:14)
- What Happens When Workflows Are Disconnected? (7:11)
- Creating Alignment With Customers (7:46)
- Introduction To The Decision Platform (7:23)
- Understanding Platform Layers (8:48)
- Why Data Gathering Is Harder For Decision Platform Features (7:15)
- Decision Chains And ROI (9:45)
- Slides
- Introduction To The Fourth Platform (10:45)
- Why Generative Interfaces And Products Have Rapid Adoption Cycles (7:30)
- Disrupting Education Workflows And Adaptations (10:26)
- Disrupting Streaming Exercise (9:47)
- Disrupting Streaming Exercise Answer (13:37)
- SAP Joule Generative Interface Case Study (9:56)
- Saving ChatGPT Exercise (6:54)
- Saving ChatGPT Exercise Answer (8:13)
- Slides
- Introduction And Understanding Tractable (9:07)
- Saving Tractable Exercise (7:22)
- Saving Tractable Answer (10:43)
- Introduction To Competitive Analysis (26:10)
- Who Are Your Stakeholders (2:08)
- High Level Pushback Strategies (10:46)
- Business Culture Causes Resistance (12:49)
- The Business Culture Must Adapt For Us To Get Buy In (10:08)
- A Game Of Alignment (7:50)
- Managing Stakeholder Expectations (7:58)
- The Board Would Like A Word (10:18)
- Slides
- Introduction And Do You Need A Technical Background (4:32)
- Revisiting The Problem, Data, And Solution Space Breakdown (9:21)
- Nike SNKRS App Introduction (11:12)
- Causal KPIs And Setting Up For The Experiment (5:55)
- Setting The Conditions For An Experiment And Research Phase (5:52)
- Nike SNKRS App Experiment 1 (10:37)
- Managing The Research Lifecycle (6:44)
- Nike SNKRS App Experiment 2 (10:52)
- Slides
Why Certify?
Individual Benefits:
- Get Certified As A Data & AI Product Manager
- Prepare For A High-Value Role
- Future Proof Your Career
- Increase Your Earnings Potential
- Accelerate Your Career Path
- Become More Strategic While Staying Close To Product Development
Problems Solved:
- The Data Team Cannot Demonstrate ROI Or Is Seen As A Cost Center
- Low Adoption Rates For Data & AI Products
- Business Leaders Are Afraid Of Change And See AI As A Threat
- ‘Proof Of Concept Purgatory’ With Very Little Making It Beyond Evaluation
- A Lack Of Prioritization, Constant Interruptions From Fire Drills, & Every Request Is Priority 1
- No One Listens: The Best Ideas Are Ignored Or Overlooked
- C-level Leaders & The Business Aren’t Interested Or Engaged
What You'll Do:
- Discover & Justify High-Value Use Cases
- Monetize Data & AI
- Work Effectively With Stakeholders & C-level Leaders
- Price AI Products & Features
- Develop Initiatives That Deliver Value Incrementally
- Define the AI Product Vision & Roadmap
- Manage Research Lifecycles & Advanced Machine Learning Initiatives
- Develop AI Go To Market Strategies
- Estimate AI Opportunity Size & Initiative Costs
- Lead & Persuade Without Authority
Why HROI Certifications?
Well Recognized and Respected: Taught by an award-winning industry expert on AI and strategy. HROI certifications prove capabilities.
Customized For Data Professionals: Lessons are connected to the field and relevant to data professionals' unique needs.
Curriculum Built From A Rich Set Of Sources: Use cases, research, industry surveys, and dozens of authors.
Expert Insights: 27 years in technology, 17 in leadership, 10 in data science, and 7 in technical strategy. The instructor brings real-world insights into each lesson.
Course Design Best Practices: Courses are distraction-free and built on adult education principles. A combination of engaging methods delivers key learning objects for multiple learning styles.