IIRF Online > Development > Data Science > Artificial Intelligence > The Product Management for AI & Data Science Course
The Product Management for AI & Data Science Course by Udemy
The Complete Course for Becoming a Successful Product Manager in the Field of AI & Data Science
Course Highlights
- This course provides a complete overview for a product manager in the field of data science and AI
- Learn how to be the bridge between business needs and technically oriented data science and AI personnel
- Learn what is the role of a product manager and what is the difference between a product and a project manager
- Distinguish between data analysis and data science
- Be able to tell the difference between an algorithm and an AI
- Distinguish different types of machine learning
- Execute business strategy for AI and Data
- Perform SWOT analysis
- Learn how to build and test a hypothesis
- Acquire user experience for AI and data science skills
- Source data for your projects and understand how this data needs to be managed
- Examine the full lifecycle of an AI or data science project in a company
- Learn how to manage data science and AI teams
- Improve communication between team members
- Address ethics, privacy, and bias
Skills you will learn!
Curriculum
6 Topics
Introduction
Course Overview
Growing Importance of an AI & Data PM
The Role of a Product Manager
Differentiation of a PM in AI & Data
Product Management vs. Project Management
10 Topics
A Product Manager as an Analytics Translator
Data Analysis vs. Data Science
A Traditional Algorithm vs. AI
AI vs Traditional Algorithm
Explaining Machine Learning
Explaining Deep Learning
When to use Machine Learning vs. Deep Learning
Machine Learning or Deep Learning
Supervised Unsupervised & Reinforcement Learning
Supervised Learning Unsupervised Learning or Reinforcement Learning
7 Topics
AI Business Model Innovations
When to Use AI
SWOT Analysis
Building a Hypothesis
Testing a Hypothesis
AI Business Canvas
Dr.DermaApp Case Study
5 Topics
User Experience for Data & AI
Getting to the Core Problem
User Research Methods
Developing User Personas
Prototyping with AI
8 Topics
Data Growth Strategy
Open Data
Company Data
Crowdsourcing Labeled Data
New Feature Data
Acquisition/Purchase Data Collection
Data Collection Needs Matching
Databases Data Warehouses & Data Lakes
6 Topics
AI Flywheel Effect
Top & Bottom Problem Solving
Product Ideation Techniques
Complexity vs. Benefit Prioritization
MVPs & MVDs (Minimum Viable Data)
Agile & Data Kanban
6 Topics
Who Should Buid Your Model
Enterpise AI
Machine Learning as a Service (MLaaS)
In-House AI & The Machine Learning Lifecycle
Timelines & Diminishing Returns
Setting a Model Performance Metric
6 Topics
Dividing Test Data
The Confusion Matrix
Precision Recall & F1 Score
Optimizing for Experience
Error Recovery
AutoBikerz Case Study
5 Topics
Model Deployment Methods
Monitoring Models
Selecting a Feedback Metric
User Feedback Loops
Shadow Deployments
5 Topics
AI Hierarchy of Needs
AI Within an Organization
Roles in AI & Data Teams
Managing Team Workflow
Dual & Triple-Track Agile
5 Topics
Internal Stakeholder Management
Setting Data Expectations
Active Listening & Communication
Compelling Presentations with Storytelling
Running Effective Meetings
5 Topics
AI User Concerns
Bad Actors & Security
AI Amplifying Human Bias
Data Laws & Regulations
Bonus Lecture
The Product Management for AI & Data Science Course
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