In the rapidly expanding world of today, technology is dominating more than ever before. As more and more products become digital, the amount of data generated and collected is increasing, hand-in-hand with the job opportunities peripheral to data.

One role that is on the rise in today’s market around the world — Data (and possibly AI but not always) Product Managers i.e. a professional with Data Science & Analytics and Product Management Experience.

With the increasing amount of data access, Product Managers now have the opportunity to utilize data to advantage by not only enhancing existing products but creating completely new products.

Objectives

Get a broad overview of most concepts and methodologies associated with data & AI product management

Walk away with the ability to

This course provides a complete overview for a product manager in the field of data analysis, data science and AI.

The course is structured in a beginner-friendly way. Even if you are new to data science and AI or if you don’t have prior product management experience, we will bring you up to speed in the first few chapters. We’ll start off with an introduction to product management for AI and data. You will learn what is the role of a product manager and what is the difference between a product and a project manager.

We will continue by introducing some key technological concepts for AI and data. You will learn how to distinguish between data analysis and data science, what is the difference between an algorithm and an AI, what counts as machine learning, and what counts as deep learning, and which are the different types of machine learning (supervised, unsupervised, and reinforcement learning). These first two modules of the course will provide you with the fundamentals of the field in no time and you will have a great overview of AI and data science today.

Then, in module 3, we’ll start talking about Business strategy for AI and Data. We will discuss when a company needs to use AI, as well as how to perform a SWOT analysis, and how to build and test a hypothesis. In this part of the course, you’ll receive your first assignment – to create a business proposal.

Module 4 focuses on User experience for AI & Data. We will talk about getting the core problem, user research methods, how to develop user personas, and how to approach AI prototyping. In module 5, we will talk about data management. You will learn how to source data for your projects and how this data needs to be managed. You will also acquire an idea about the type of data that you need when working with different types of machine learning.

In modules 6, 7, 8, and 9 we will examine the full lifecycle of an AI or data science project in a company. From product development to model construction, evaluating its performance, and deploying it, you will be able to acquire a holistic idea of the way this process works in practice.

Modules 10, 11, and 12 are very important ones too. You will learn how to manage data science and AI teams, and how to improve communication between team members. Finally we will make some necessary remarks regarding ethics, privacy, and bias.

Training materials

  • Training support documents,
  • Business proposal methodology

Assessment

The assessment of prior learning is done throughout the session through workshops and practice. An on-the-spot assessment of trainee satisfaction is systematically carried out at the end of the session and a training certificate is issued to participants mentioning the training objectives, the nature, program and duration of the training action as well as the formalization. achievements

Prerequisites

No particular skills required, ideally previous experience in project management

Public

  • Product owners, managers who wish to hone their skills
  • Anyone who wishes to get started with data product management

Material required

Recent laptop (<5 years) with administration rights

Book this course

contact us



Lead Instructor

Maxime Le Gouvello

Maxime Le Gouvello

Course agenda

Product Management for AI & data

5 hours

Introduction

  • Growing importance of an AI & data PM
  • The Role of a Product Manager
  • What’s so different in data & AI?
  • Product management vs. Project management

Key technological concepts

  • A Product Manager as an Analytics Translator
  • Data Analysis vs. Data Science
  • Traditional algorithms vs. AI
  • Explaining machine learning and deep learning
  • When to use Machine Learning vs. Deep Learning
  • Supervised, Unsupervised, & Reinforcement Learning
  • Which algorithms to use for which use case

Business strategy for AI and data

4 hours

  • AI Business Model Innovations
  • When to Use AI
  • SWOT Analysis
  • Building and testing a hypothesis
  • AI business canvas
  • Case study

User experience for AI & data

4 hours

  • User Experience for Data & AI
  • Getting to the Core Problem
  • User Research Methods
  • Developing User Personas
  • Prototyping with AI
  • Practical exercises : fast mockup, building a UR questionnaire, building an empathy map

Data management for AI and data

4 hours

  • Data Growth Strategy
  • The different data classifications (structured/ unstructured, 1P/2P/3P, Open / company data, etc.)
  • Crowdsourcing Labeled Data
  • New Feature Data
  • Acquisition/Purchase Data Collection
  • Databases, Data Warehouses, & Data Lakes
  • Building a data model
  • What is an APIs
  • Writing an interface contract

Product development for AI and data

4 hours

  • AI Flywheel Effect
  • The product development lifecycle (Discovery, Prototype, MVP, Delivery, Run)
  • Top & Bottom Problem Solving
  • Product Ideation Techniques
  • Complexity vs. Benefit Prioritization
  • Agile methodologies (scrum, kanban)

Building the model

2 hours

  • Who Should Build Your Model
  • Machine Learning as a Service (MLaaS)
  • In-House AI & The Machine Learning Lifecycle
  • Timelines & Diminishing Returns
  • Setting a Model Performance Metric

Evaluating performance

4 hours

  • Dividing Test Data
  • The Confusion Matrix
  • Precision, Recall & F1 Score
  • Optimizing for Experience
  • Error Recovery

Deployment and continuous improvement

2 hours

  • Model Deployment Methods
  • Monitoring Models
  • Selecting a Feedback Metric
  • User Feedback Loops
  • Shadow Deployments

Managing data science & AI teams

2 hours

  • AI Hierarchy of Needs
  • AI Within an Organization
  • Roles in AI & Data Teams
  • Managing Team Workflow
  • Dual-Track Agile

Communication & Ethics

2 hours

Communication

  • Internal Stakeholder Management
  • Setting Data Expectations
  • Active Listening & Communication
  • Compelling Presentations with Storytelling
  • Running Effective Meetings

Ethics, Privacy, Bias

  • AI User Concerns
  • Bad Actors & Security
  • AI Amplifying Human Bias
  • Data Laws & Regulations