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Product Management for AI & Data Science
Intro to Product Management for AI & Data Science
Introduction (4:03)
Course overview (3:19)
Growing importance of a PM in AI & Data Management (3:04)
The role of a Product Manager (4:48)
Difference of a PM in AI & Data Science (3:25)
Product Management vs. Project Management (4:03)
Key Technological Concepts for AI & Data Science
A product manager as an Analytics Translator (3:49)
Data Analysis vs. Data Science (2:59)
An Algorithm vs. AI (5:47)
Explaining Machine Learning (6:02)
Explaining Deep Learning (5:15)
When to use Machine Learning vs. Deep Learning (6:03)
Quiz 1: Machine Learning or Deep Learning
Supervised, Unsupervised, & Reinforcement Learning (4:53)
Quiz 2: Supervised Learning, Unsupervised Learning or Reinforcement Learning
Business Strategy for AI & Data Science
AI Business Model Innovations (4:54)
When to Use AI (3:59)
SWOT Analysis (3:31)
Building a Hypothesis (4:11)
Testing a Hypothesis (3:46)
AI Business Canvas (4:01)
Assignment 1: Dr.DermaApp Case Study
User Experience for AI & Data Science
User Experience for Data & AI Science (3:58)
Getting to the Core Problem (4:19)
User Research Methods (4:27)
Developing User Personas (4:20)
Prototyping with AI (4:25)
Data Management for AI & Data Science
Data Growth Strategy (5:37)
Open Data (2:58)
Company Data (3:08)
Crowdsourcing Labeled Data (6:44)
New Feature Data (4:16)
Acquisition/Purchase Data Collection (3:29)
Quiz 3: Data Collection Needs Matching
Databases, Data Warehouses, & Data Lakes (3:50)
Product Development for AI & Data Science
AI Flywheel Effect (3:22)
Top & Bottom Problem Solving (3:15)
Product Ideation Techniques (4:27)
Complexity vs. Benefit Prioritization (5:47)
MVPs & MVDs (Minimum Viable Data) (5:43)
Agile & Data Kanban (4:54)
Building The Model
Who Should Buid Your Model (5:07)
Enterpise AI (4:30)
Machine Learning as a Service (MLaaS) (4:32)
In-House AI & The Machine Learning Lifecycle (3:26)
Timelines & Diminishing Returns (4:42)
Setting a Model Performance Metric (4:39)
Evaluating Performance
Dividing Test Data (4:21)
The Confusion Matrix (3:15)
Precision, Recall & F1 Score (3:48)
Optimizing for Experience (6:23)
Error Recovery (3:46)
Assignment 2: AutoBikerz Case Study
Deployment & Continuous Improvement
Model Deployment Methods (5:34)
Monitoring Models (4:20)
Selecting a Feedback Metric (3:49)
User Feedback Loops (3:46)
Shadow Deployments (3:16)
Managing Data Science & AI Teams
AI Hierarchy of Needs (4:55)
AI Within an Organization (4:20)
Roles in AI & Data Teams (4:53)
Managing Team Workflow (3:18)
Dual & Triple-Track Agile (4:06)
Communication
Internal Stakeholder Management (5:04)
Setting Data Expectations (4:50)
Active Listening & Communication (4:19)
Compelling Presentations with Storytelling (4:18)
Running Effective Meetings (4:57)
Ethics, Privacy, & Bias
AI User Concerns (3:38)
Bad Actors & Security (5:10)
AI Amplifying Human Bias (5:47)
Data Laws & Regulations (4:00)
In-House AI & The Machine Learning Lifecycle
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