Online Course top-rated
Product Management for AI & Data Science

Name: Product Management for AI and Data Science Course
Price: 36 USD

with Danielle Thé

Grasp the full lifecycle of AI and data science projects: learn effective product management techniques

4.8

863 reviews on

Start for free

11,790 students already enrolled

Skill level:

Basic

Duration:

6 hours

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

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

Danielle Thé

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

Ask Danielle a question

Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 7.5 Field of Study: Information Technology
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

Intro to AI

Curriculum

69 lessons 13 exercises 4 exams

1. Intro to Product Management for AI & Data Science

22 min

An introduction to product management for AI and data science. You will learn about the important role of a product manager and understand the difference between product management and project management.

22 min

An introduction to product management for AI and data science. You will learn about the important role of a product manager and understand the difference between product management and project management.

Introduction Free

Course Overview Free

Growing Importance of an AI & Data PM Free

The Role of a Product Manager Free

Differentiation of a PM in AI & Data Free

Product Management vs. Project Management Free
2. Key Technological Concepts for AI & Data Science

35 min

This 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 min

This 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 Free

Data Analysis vs. Data Science Free

An Algorithm vs. AI Free

Explaining Machine Learning Free

Explaining Deep Learning Free

When to use Machine Learning vs. Deep Learning Free

Exercise Free

Supervised, Unsupervised, & Reinforcement Learning Free

Exercise Free
3. Business Strategy for AI & Data Science

26 min

This 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 min

This 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

When to Use AI

SWOT Analysis

Building a Hypothesis

Testing a Hypothesis

Create a Business Proposal

AI Business Canvas
4. User Experience for AI & Data Science

20 min

This 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 min

This 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

Getting to the Core Problem

User Research Methods

Developing User Personas

Prototyping with AI

Practice exam
5. Data Management for AI & Data Science

30 min

Here, 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 min

Here, 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

Open Data

Company Data

Crowdsourcing Labeled Data

New Feature Data

Acquisition/Purchase Data Collection

Exercise

Databases, Data Warehouses, & Data Lakes
6. Product Development for AI & Data Science

27 min

In 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 min

In 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

Top & Bottom Problem Solving

Product Ideation Techniques

Complexity vs. Benefit Prioritization

MVPs & MVDs (Minimum Viable Data)

Agile & Data Kanban
7. Building The Model

28 min

This 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 min

This 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

Enterpise AI

Machine Learning as a Service (MLaaS)

In-House AI & The Machine Learning Lifecycle

Timelines & Diminishing Returns

Setting a Model Performance Metric
8. Evaluating Performance

22 min

This 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 min

This 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

The Confusion Matrix

Precision, Recall & F1 Score

Optimizing for Experience

Error Recovery

Compare & Select the Best Model

Practice exam
9. Deployment & Continuous Improvement

21 min

Here, 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 min

Here, 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

Monitoring Models

Selecting a Feedback Metric

User Feedback Loops

Shadow Deployments
10. Managing Data Science & AI Teams

21 min

Where 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 min

Where 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

AI Within an Organization

Roles in AI & Data Teams

Managing Team Workflow

Dual & Triple-Track Agile
11. Communication

23 min

This 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 min

This 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

Setting Data Expectations

Active Listening & Communication

Compelling Presentations with Storytelling

Running Effective Meetings
12. Ethics, Privacy, & Bias

19 min

As 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 min

As 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

Bad Actors & Security

AI Amplifying Human Bias

Data Laws & Regulations

Practice exam
13. Course exam

40 min

40 min

Course exam

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

Start for free

As a PhD candidate and consultant, I needed both analytical rigor and creative problem-solving. 365 Data Science gave me that. Their structured lessons, hands-on exercises, and clear explanations strengthened my research and consulting work. The platform doesn’t just teach tools—it builds a mindset. I’ve recommended it to both students and professionals because it provides accessible, practical training that delivers long-term value. In today’s data-driven world, 365 is where real understanding begins.

Michael H.

Before 365: PhD Researcher

After 365: Lecturer | James Cook Univ. & Digital Transformation Consultant

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9 in 10

of our graduates landed a new AI & data job

after enrollment

When I began my master’s program, I felt lost—statistical jargon was overwhelming, and Python and R seemed impossible. 365 Data Science changed that. The clear explanations, structured courses, and practical projects helped me build a solid foundation in statistics, programming, and SQL. I not only completed my degree but also advanced to a better role. 365 made complex topics approachable and gave me skills that boosted both my academic and professional growth.

Enamul K. B.

After 365: Accounting Manager | JFE Engineering Corporation

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$29,000

average salary increase

after moving to an AI and data science career

Coming from a biology background, I had no prior experience in coding or data analysis when I began my master’s in Bioinformatics and Neuroinformatics. The learning curve was steep, but discovering 365 Data Science changed everything. Their comprehensive courses—complete with theory, notes, assessments, and real projects—helped me build skills and confidence. I fell in love with data analysis and chose it as my career path. Even after landing a job, I’ll continue learning with 365!