Product Management for AI & Data Science

Curriculum

• Intro to Product Management for AI & Data Science Free

  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 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
• Key Technological Concepts for AI & Data Science Free

  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 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

  Supervised, Unsupervised, & Reinforcement Learning Free
• Business Strategy for AI & Data Science

  6 Lessons 25 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

  AI Business Canvas
• 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

  Getting to the Core Problem

  User Research Methods

  Developing User Personas

  Prototyping with AI
• 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

  Open Data

  Company Data

  Crowdsourcing Labeled Data

  New Feature Data

  Acquisition/Purchase Data Collection

  Databases, Data Warehouses, & Data Lakes
• 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

  Top & Bottom Problem Solving

  Product Ideation Techniques

  Complexity vs. Benefit Prioritization

  MVPs & MVDs (Minimum Viable Data)

  Agile & Data Kanban
• 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

  Enterpise AI

  Machine Learning as a Service (MLaaS)

  In-House AI & The Machine Learning Lifecycle

  Timelines & Diminishing Returns

  Setting a Model Performance Metric
• Evaluating Performance

  5 Lessons 21 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
• 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

  Monitoring Models

  Selecting a Feedback Metric

  User Feedback Loops

  Shadow Deployments
• 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

  AI Within an Organization

  Roles in AI & Data Teams

  Managing Team Workflow

  Dual & Triple-Track Agile
• 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

  Setting Data Expectations

  Active Listening & Communication

  Compelling Presentations with Storytelling

  Running Effective Meetings
• 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

  Bad Actors & Security

  AI Amplifying Human Bias

  Data Laws & Regulations