Product Management for AI & Data Science
top-rated

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

with Danielle Thé

4.9/5

(742)

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

6 hours of content 10935 students

Start for Free

What you get:

6 hours of content
13 Interactive exercises
26 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Product Management for AI & Data Science
top-rated

A course by Danielle Thé

Start for Free

What you get:

6 hours of content
13 Interactive exercises
26 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Start for Free

What you get:

6 hours of content
13 Interactive exercises
26 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

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.

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

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.

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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

Curriculum

1. Intro to Product Management for AI & Data Science

6 Lessons 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
4 min
Course Overview
3 min
Growing Importance of an AI & Data PM
3 min
The Role of a Product Manager
5 min
Differentiation of a PM in AI & Data
3 min
Product Management vs. Project Management
4 min
2. Key Technological Concepts for AI & Data Science

7 Lessons 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
4 min
Data Analysis vs. Data Science
3 min
An Algorithm vs. AI
6 min
Explaining Machine Learning
6 min
Explaining Deep Learning
5 min
When to use Machine Learning vs. Deep Learning
6 min
Supervised, Unsupervised, & Reinforcement Learning
5 min
3. Business Strategy for AI & Data Science

7 Lessons 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
5 min
When to Use AI
4 min
SWOT Analysis
4 min
Building a Hypothesis
4 min
Testing a Hypothesis
4 min
AI Business Canvas
4 min
Create a Business Proposal Read now
1 min
4. User Experience for AI & Data Science

5 Lessons 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
4 min
Getting to the Core Problem
4 min
User Research Methods
4 min
Developing User Personas
4 min
Prototyping with AI
4 min
5. Data Management for AI & Data Science

7 Lessons 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
6 min
Open Data
3 min
Company Data
3 min
Crowdsourcing Labeled Data
7 min
New Feature Data
4 min
Acquisition/Purchase Data Collection
3 min
Databases, Data Warehouses, & Data Lakes
4 min
6. Product Development for AI & Data Science

6 Lessons 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
3 min
Top & Bottom Problem Solving
3 min
Product Ideation Techniques
4 min
Complexity vs. Benefit Prioritization
6 min
MVPs & MVDs (Minimum Viable Data)
6 min
Agile & Data Kanban
5 min
7. Building The Model

6 Lessons 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
5 min
Enterpise AI
5 min
Machine Learning as a Service (MLaaS)
5 min
In-House AI & The Machine Learning Lifecycle
3 min
Timelines & Diminishing Returns
5 min
Setting a Model Performance Metric
5 min
8. Evaluating Performance

6 Lessons 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
4 min
The Confusion Matrix
3 min
Precision, Recall & F1 Score
4 min
Optimizing for Experience
6 min
Error Recovery
4 min
Compare & Select the Best Model Read now
1 min
9. Deployment & Continuous Improvement

5 Lessons 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
6 min
Monitoring Models
4 min
Selecting a Feedback Metric
4 min
User Feedback Loops
4 min
Shadow Deployments
3 min
10. Managing Data Science & AI Teams

5 Lessons 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
5 min
AI Within an Organization
4 min
Roles in AI & Data Teams
5 min
Managing Team Workflow
3 min
Dual & Triple-Track Agile
4 min
11. Communication

5 Lessons 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
5 min
Setting Data Expectations
5 min
Active Listening & Communication
4 min
Compelling Presentations with Storytelling
4 min
Running Effective Meetings
5 min
12. Ethics, Privacy, & Bias

4 Lessons 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
4 min
Bad Actors & Security
5 min
AI Amplifying Human Bias
6 min
Data Laws & Regulations
4 min

Topics

Product ManagementMachine LearningCareer DevelopmentUser ExperienceEthicsAITheoryData LiteracyBusiness Skills

Tools & Technologies

Course Requirements

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

Who Should Take This Course?

Level of difficulty: Beginner

Aspiring business analysts, data analysts, and data scientists
Current business analysts, data analysts and data scientists who want to boost their business acumen and product management skills
Managers in small, mid-sized, and large organizations

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Meet Your Instructor

Danielle Thé

Product Manager at

1 Courses

742 Reviews

10935 Students

Danielle’s expertise in product marketing, product management, and project management is beyond impressive. She has worked with two of the FAANG companies. At Google, Danielle was involved in various initiatives for the YouTube brand, where she continued to develop her professional skills. Her extensive experience includes working with Data Scientists to leverage Natural Language Processing in storytelling for Wattpad, as well as founding Developers Without Borders - an online platform that connects software developers worldwide with development projects. Danielle describes herself as “a tech enthusiast that makes the digital world easy to understand”. Now and then, Danielle posts interesting videos on her YouTube channel. Her content is extremely engaging and fun to watch, so we definitely recommend checking it out.