Michael H.
Before 365:
PhD Researcher
After 365:
Lecturer | James Cook Univ. & Digital Transformation Consultant
See all reviews
Online Course
top-rated
free
Product Management for AI & Data Science
with
Danielle Thé
Grasp the full lifecycle of AI and data science projects: learn effective product management techniques
11,876 students already enrolled
Skill level:
Basic
Duration:
6 hours
-
Lessons (5 hours)
-
Practice exams (42 minutes)
- Lessons (5 hours)
- Practice exams (42 minutes)
CPE credits:
7.5
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited
certificate
What you learn
- Understand product manager responsibilities in data science and AI.
- Improve communication to enhance collaboration with teams and stakeholders.
- Decide strategically when to use AI to optimize resources and efficiency.
- Gain a big-picture view of how data science and AI create business value.
- Master key technological concepts essential for data science and AI.
Topics & tools
Product ManagementMachine LearningCareer DevelopmentUser ExperienceEthicsAITheoryData LiteracyBusiness Skills
Your instructor
Lead Product Manager at Meta | AI & Data Products
Track record
Bringing real-world expertise from leading global companies
Academic background
Master's degree, Management, International Management
Media and recognition
- Global Impact: Ivey Emerging Leaders Award (Nov 2016)
- Advertising Excellence Award, Pollution Engineering (Feb 2014)
- Creator of tech and AI product management content on YouTube
Course OVERVIEW
Description
CPE Credits: 7.5
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
This course is designed to teach you the specialized skills needed to manage the development of successful AI products. You'll learn how to identify opportunities for a business to leverage AI and prepare the data needed. In addition, you will grasp the organizational structure of AI and data science teams, how to communicate effectively with stakeholders, and the best methods for managing a team's workflow. After the course, you'll be able to take an idea, evaluate it with potential customers, prototype, strategize, build tests, and iterate on AI and data driven products.
Prerequisites
- No prior experience or knowledge is required. We will start from the basics and gradually build your understanding. Everything you need is included in the course
Advanced preparation
Curriculum
69 lessons
13 exercises
4 exams
- 2. Key Technological Concepts for AI & Data Science35 minThis section explains some key technological concepts for AI and data science. You will learn how to distinguish between data analysis and data science, how an algorithm differs from AI, and when to choose machine learning vs deep learning methods. You will also decipher the types of machine learning (supervised, unsupervised, and reinforcement learning).35 minThis section explains some key technological concepts for AI and data science. You will learn how to distinguish between data analysis and data science, how an algorithm differs from AI, and when to choose machine learning vs deep learning methods. You will also decipher the types of machine learning (supervised, unsupervised, and reinforcement learning).A Product Manager as an Analytics Translator FreeData Analysis vs. Data Science FreeAn Algorithm vs. AI FreeExplaining Machine Learning FreeExplaining Deep Learning FreeWhen to use Machine Learning vs. Deep Learning FreeExercise FreeSupervised, Unsupervised, & Reinforcement Learning FreeExercise Free
- 3. Business Strategy for AI & Data Science26 minThis section focuses on business strategy for AI and data science. You will discover when a company needs to use AI, as well as how to perform a SWOT analysis, and how to build and test a hypothesis. Here, you will receive your first assignment – to create a business proposal.26 minThis section focuses on business strategy for AI and data science. You will discover when a company needs to use AI, as well as how to perform a SWOT analysis, and how to build and test a hypothesis. Here, you will receive your first assignment – to create a business proposal.AI Business Model Innovations FreeWhen to Use AI FreeSWOT Analysis FreeBuilding a Hypothesis FreeTesting a Hypothesis FreeCreate a Business Proposal FreeAI Business Canvas Free
- 4. User Experience for AI & Data Science20 minThis part of the course revolves around user experience for AI & data science. We will talk about getting to the core problem, user research methods, how to develop user personas, and how to approach AI prototyping.20 minThis part of the course revolves around user experience for AI & data science. We will talk about getting to the core problem, user research methods, how to develop user personas, and how to approach AI prototyping.User Experience for Data & AI FreeGetting to the Core Problem FreeUser Research Methods FreeDeveloping User Personas FreePrototyping with AI FreePractice exam Free
- 5. Data Management for AI & Data Science30 minHere, we draw attention to data management. You will learn how to source data for your projects and how this data needs to be managed. You will also gain insight into the type of data that you need when working with different types of machine learning.30 minHere, we draw attention to data management. You will learn how to source data for your projects and how this data needs to be managed. You will also gain insight into the type of data that you need when working with different types of machine learning.Data Growth Strategy FreeOpen Data FreeCompany Data FreeCrowdsourcing Labeled Data FreeNew Feature Data FreeAcquisition/Purchase Data Collection FreeExercise FreeDatabases, Data Warehouses, & Data Lakes Free
- 6. Product Development for AI & Data Science27 minIn this section, we begin examining the full lifecycle of an AI or data science project in a company. We cover the AI Flywheel Effect, top & bottom problem solving, and how to apply various product ideation techniques. You will learn how to prioritize what products should be developed and when they should be developed. Finally, you will get familiar with MVPs & MVDs, the Agile framework, and Kanban software methodology.27 minIn this section, we begin examining the full lifecycle of an AI or data science project in a company. We cover the AI Flywheel Effect, top & bottom problem solving, and how to apply various product ideation techniques. You will learn how to prioritize what products should be developed and when they should be developed. Finally, you will get familiar with MVPs & MVDs, the Agile framework, and Kanban software methodology.AI Flywheel Effect FreeTop & Bottom Problem Solving FreeProduct Ideation Techniques FreeComplexity vs. Benefit Prioritization FreeMVPs & MVDs (Minimum Viable Data) FreeAgile & Data Kanban Free
- 7. Building The Model28 minThis section explores the different options for developing solutions in your organization. Building custom in-house A.I., leveraging M.O.s, also known as machine learning as a service, or hiring out enterprise A.I. Solutions.28 minThis section explores the different options for developing solutions in your organization. Building custom in-house A.I., leveraging M.O.s, also known as machine learning as a service, or hiring out enterprise A.I. Solutions.Who Should Buid Your Model FreeEnterpise AI FreeMachine Learning as a Service (MLaaS) FreeIn-House AI & The Machine Learning Lifecycle FreeTimelines & Diminishing Returns FreeSetting a Model Performance Metric Free
- 8. Evaluating Performance22 minThis part of the course is dedicated to everything you need to know about performance evaluation: dividing test data, visualizing the performance of a ML model via confusion matrix, and calculating precision, recall and F1 scores. You will also learn how to use these to determine what model is producing the optimal performance, and how to minimize the negative impact of an error.22 minThis part of the course is dedicated to everything you need to know about performance evaluation: dividing test data, visualizing the performance of a ML model via confusion matrix, and calculating precision, recall and F1 scores. You will also learn how to use these to determine what model is producing the optimal performance, and how to minimize the negative impact of an error.Dividing Test Data FreeThe Confusion Matrix FreePrecision, Recall & F1 Score FreeOptimizing for Experience FreeError Recovery FreeCompare & Select the Best Model FreePractice exam Free
- 9. Deployment & Continuous Improvement21 minHere, we examine the three kinds of model deployment methods. The section covers how to use both proactive and reactive monitoring efforts to ensure that your model stays relevant. Further on, you will learn how to select feedback metric, build user feedback loops, and when to use shadow deployment method of testing.21 minHere, we examine the three kinds of model deployment methods. The section covers how to use both proactive and reactive monitoring efforts to ensure that your model stays relevant. Further on, you will learn how to select feedback metric, build user feedback loops, and when to use shadow deployment method of testing.Model Deployment Methods FreeMonitoring Models FreeSelecting a Feedback Metric FreeUser Feedback Loops FreeShadow Deployments Free
- 10. Managing Data Science & AI Teams21 minWhere your A.I. and data team is placed within the organization impacts how you work with your team and across the company. This section describes the AI hierarchy of needs, along with the various roles in AI and data science teams. It also illustrated a simplified workflow of an AI data product and how to minimize asynchrony by adopting a modified dual-track agile methodology.21 minWhere your A.I. and data team is placed within the organization impacts how you work with your team and across the company. This section describes the AI hierarchy of needs, along with the various roles in AI and data science teams. It also illustrated a simplified workflow of an AI data product and how to minimize asynchrony by adopting a modified dual-track agile methodology.AI Hierarchy of Needs FreeAI Within an Organization FreeRoles in AI & Data Teams FreeManaging Team Workflow FreeDual & Triple-Track Agile Free
- 11. Communication23 minThis section sheds light on how to improve communication between team members and stakeholders, set expectations as a product manager, and develop strong people skills by active listening. You will also learn what is necessary to make memorable presentations, and how to manage meetings more efficiently.23 minThis section sheds light on how to improve communication between team members and stakeholders, set expectations as a product manager, and develop strong people skills by active listening. You will also learn what is necessary to make memorable presentations, and how to manage meetings more efficiently.Internal Stakeholder Management FreeSetting Data Expectations FreeActive Listening & Communication FreeCompelling Presentations with Storytelling FreeRunning Effective Meetings Free
- 12. Ethics, Privacy, & Bias19 minAs an AI and data product manager, you should consider ways in which your product can build user trust. In this final section, you will learn about the most common AI user concerns, and how to put security measures in place to prevent manipulation of your model by bad actors once it’s deployed. You will also understand how to vet your models for biases, and get familiar with data privacy laws and regulations.19 minAs an AI and data product manager, you should consider ways in which your product can build user trust. In this final section, you will learn about the most common AI user concerns, and how to put security measures in place to prevent manipulation of your model by bad actors once it’s deployed. You will also understand how to vet your models for biases, and get familiar with data privacy laws and regulations.AI User Concerns FreeBad Actors & Security FreeAI Amplifying Human Bias FreeData Laws & Regulations FreePractice exam Free
- 13. Course exam40 min40 minCourse exam Free
Free lessons
1.1 Introduction
4 min
1.2 Course Overview
3 min
1.3 Growing Importance of an AI & Data PM
3 min
1.4 The Role of a Product Manager
5 min
1.5 Differentiation of a PM in AI & Data
3 min
1.6 Product Management vs. Project Management
4 min
9 in 10
of our graduates landed a new AI & data job
after enrollment
$29,000
average salary increase
after moving to an AI and data science career
94%
of AI and data science graduates
successfully change
or advance their careers.
ACCREDITED certificates
Craft a resume and LinkedIn profile you’re proud of—featuring certificates recognized by leading global
institutions.
Earn CPE-accredited credentials that showcase your dedication, growth, and essential skills—the qualities
employers value most.
How it WORKS
Student REVIEWS
