Product Management for AI & Data Science trending topic

with Danielle Thé
4.9/5
(683)

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

6 hours of content 10179 students

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

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 trending topic

A course by Danielle Thé

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

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

$99.00

Lifetime access

Buy now

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

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 the responsibilities of a product manager in the context of data science and AI
  • Improve communication between team members and stakeholders to foster effective collaboration
  • Learn to strategically determine when to employ AI solutions in business operations to optimize resource allocation and avoid potential inefficiencies
  • Gain a big-picture understanding of how data science and AI create business value
  • Master essential technological concepts for data science and AI
  • Differentiate your data scientist or AI Engineer profile by adopting a product manager’s perspective, enhancing your appeal to hiring managers

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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Introduction

1.1 Introduction

4 min

Course Overview

1.2 Course Overview

3 min

Growing Importance of an AI & Data PM

1.3 Growing Importance of an AI & Data PM

3 min

The Role of a Product Manager

1.4 The Role of a Product Manager

5 min

Differentiation of a PM in AI & Data

1.5 Differentiation of a PM in AI & Data

3 min

Product Management vs. Project Management

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
    Compare & Select the Best Model Read now
    1 min
    Error Recovery
    4 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 ExperienceEthicsAITheory

Tools & Technologies

theory

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.

Exams and certification

Meet Your Instructor

Danielle Thé

Danielle Thé

Product Manager at

1 Courses

683 Reviews

10179 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.

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