Product Management for AI & Data Science

This course is designed to teach you the specialized skills needed to manage the development of successful A.I. products. You'll learn how to identify opportunities for a business to leverage AI and how to prepare the data needed. You will grasp the organizational structure of A.I. 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.

Sign up to
preview the program
for FREE!

Create a free account and start learning data science today.

create free account
Our graduates work at exciting places:

Section 1

Intro to Product Management for AI & Data Science

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.

Premium course icon Introduction
Premium course icon Course Overview
Premium course icon Growing Importance of an AI & Data PM
Premium course icon The Role of a Product Manager
Premium course icon Differentiation of a PM in AI & Data
Premium course icon Product Management vs. Project Management
Premium course icon Quiz

Section 2

Key Technological Concepts for AI & Data Science

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).

Premium course icon A Product Manager as an Analytics Translator
Premium course icon Data Analysis vs. Data Science
Premium course icon An Algorithm vs. AI
Premium course icon Explaining Machine Learning
Premium course icon Explaining Deep Learning
Premium course icon When to use Machine Learning vs. Deep Learning
Premium course icon Supervised, Unsupervised, & Reinforcement Learning
Premium course icon Quiz

Section 3

Business Strategy for AI & Data Science

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.

Premium course icon AI Business Model Innovations
Premium course icon When to Use AI
Premium course icon SWOT Analysis
Premium course icon Building a Hypothesis
Premium course icon Testing a Hypothesis
Premium course icon AI Business Canvas
Premium course icon Assignment / Create a Business Proposal

Section 4

User Experience for AI & Data Science

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.

Premium course icon User Experience for Data & AI
Premium course icon Getting to the Core Problem
Premium course icon User Research Methods
Premium course icon Developing User Personas
Premium course icon Prototyping with AI
Premium course icon Assignment / Create an Empathy Map

Section 5

Data Management for AI & Data Science

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.

Premium course icon Data Growth Strategy
Premium course icon Open Data
Premium course icon Company Data
Premium course icon Crowdsourcing Labeled Data
Premium course icon New Feature Data
Premium course icon Acquisition/Purchase Data Collection
Premium course icon Databases, Data Warehouses, & Data Lakes
Premium course icon Project / Create a Dataset using Appen

Section 6

Product Development for AI & Data Science

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.

Premium course icon AI Flywheel Effect
Premium course icon Top & Bottom Problem Solving
Premium course icon Product Ideation Techniques
Premium course icon Complexity vs. Benefit Prioritization
Premium course icon MVPs & MVDs (Minimum Viable Data)
Premium course icon Agile & Data Kanban
Premium course icon Quiz

Section 7

Building The Model

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.

Premium course icon Who Should Buid Your Model
Premium course icon Enterpise AI
Premium course icon Machine Learning as a Service (MLaaS)
Premium course icon In-House AI & The Machine Learning Lifecycle
Premium course icon Timelines & Diminishing Returns
Premium course icon Setting a Model Performance Metric

Section 8

Evaluating Performance

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.

Premium course icon Dividing Test Data
Premium course icon The Confusion Matrix
Premium course icon Precision, Recall & F1 Score
Premium course icon Optomizing for Experience
Premium course icon Error Recovery
Premium course icon Assignment / Compare & Select the Best Model

Section 9

Deployment & Continuous Improvement

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.

Premium course icon Model Deployment Methods
Premium course icon Monitoring Models
Premium course icon Selecting a Feedback Metric
Premium course icon User Feedback Loops
Premium course icon Shadow Deployments
Premium course icon Assignment / Find Possible Data Errors

Section 10

Managing Data Science & AI Teams

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.

Premium course icon AI Hierarchy of Needs
Premium course icon AI Within an Organization
Premium course icon Roles in AI & Data Teams
Premium course icon Managing Team Workflow
Premium course icon Dual & Triple-Track Agile
Premium course icon Quiz

Section 11


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.

Premium course icon Internal Stakeholder Management
Premium course icon Setting Data Expectations
Premium course icon Active Listening & Communication
Premium course icon Compelling Presentations with Storytelling
Premium course icon Running Effective Meetings
Premium course icon Assignment

Section 12

Ethics, Privacy, & Bias

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.

Premium course icon AI User Concerns
Premium course icon Bad Actors & Security
Premium course icon AI Amplifying Human Bias
Premium course icon Data Laws & Regulations
Premium course icon Quiz

Advanced Specialization

This course is part of Module 4 of the 365 Data Science Program. The complete training consists of four modules, each building upon your knowledge from the previous one. Module 4 is focused on developing a specialized, industry-relevant skill set, and students are encouraged to complete Modules 1, 2, and 3 before they start this part of the training. Here, you will learn how to perform Credit Risk Modeling for banks, Customer Analytics for retail or other commercial companies, and Time Series Analysis for finance and stock data.

See All Modules

Trust the other 500,000 students

Ready to start?
Sign up today for FREE!

Whether you want to scale your career or transition into a new field, data science is the number one skillset employers look for. Grow your analytics expertise and get hired as a data scientist!
Complete Data Science Education
Get 50% OFF