The Product Management For Ai & Data Science Course 2023

The Complete Course for Becoming a Successful Product Manager in the Field of AI & Data Science

What you'll learn

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

Learn how to be the bridge between business needs and technically oriented data science and AI personnel

Learn what is the role of a product manager and what is the difference between a product and a project manager

Distinguish between data analysis and data science

Be able to tell the difference between an algorithm and an AI

Distinguish different types of machine learning

Execute business strategy for AI and Data

Perform SWOT analysis

Learn how to build and test a hypothesis

Acquire user experience for AI and data science skills

Source data for your projects and understand how this data needs to be managed

Examine the full lifecycle of an AI or data science project in a company

Learn how to manage data science and AI teams

Improve communication between team members

Address ethics, privacy, and bias

Requirements

No prior experience is required. We will start from the very basics

Description

Do you want to learn how to become a product manager?Are you interested in product management for AI & Data Science?If the answer is ‘yes’, then you have come to the right place!This course gives you a fairly unique opportunity. You will have the chance to learn from somebody who has been in the industry and who has actually seen AI & data science implemented at the highest level.Your instructor, Danielle Thé, is a Senior Product Manager for Machine Learning with a Master’s in Science of Management, and years of experience as a Product Manager, and Product Marketing Manager in the tech industry for companies like Google and Deloitte Digital.From security applications to recommendations engines, companies are increasingly turning to big data and artificial intelligence to improve their operations and product offerings. In the past 4 years alone, organizational adoption of AI has grown 270%. And companies are scrambling to find the talent that can manage the product implementation of big data and AI systems. In this context, a product manager serves as the bridge between business needs and technically oriented data science and AI personnel.Organizations are looking for people like you to rise to the challenge of leading their business into this new and exciting change.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 sections 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 section 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.Section 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 section 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 sections 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.Sections 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.This course is an amazing journey and it aims to prepare you for a very interesting career path!Why should you consider a career as a Product Manager?Salary. A Product Manager job usually leads to a very well-paid career (average salary reported on Glassdoor: $108,992)Promotions. Product Managers work closely with division heads and high - level executives, which makes them the leading candidates for senior roles within a corporationSecure Future. There is a high demand for Product Managers on the job marketGrowth. This isn’t a boring job. Every day, you will face different challenges that will test your existing skillsJust go ahead and subscribe to this course! If you don't acquire these skills now, you will miss an opportunity to distinguish yourself from the others. Don't risk your future success! Let's start learning together now!

Overview

Section 1: Intro to Product Management for AI & Data

Lecture 1 Introduction

Lecture 2 Course Overview

Lecture 3 Growing Importance of an AI & Data PM

Lecture 4 The Role of a Product Manager

Lecture 5 Differentiation of a PM in AI & Data

Lecture 6 Product Management vs. Project Management

Section 2: Key Technological Concepts for AI & Data

Lecture 7 A Product Manager as an Analytics Translator

Lecture 8 Data Analysis vs. Data Science

Lecture 9 A Traditional Algorithm vs. AI

Lecture 10 Explaining Machine Learning

Lecture 11 Explaining Deep Learning

Lecture 12 When to use Machine Learning vs. Deep Learning

Lecture 13 Supervised, Unsupervised, & Reinforcement Learning

Section 3: Business Strategy for AI & Data

Lecture 14 AI Business Model Innovations

Lecture 15 When to Use AI

Lecture 16 SWOT Analysis

Lecture 17 Building a Hypothesis

Lecture 18 Testing a Hypothesis

Lecture 19 AI Business Canvas

Section 4: User Experience for AI & Data

Lecture 20 User Experience for Data & AI

Lecture 21 Getting to the Core Problem

Lecture 22 User Research Methods

Lecture 23 Developing User Personas

Lecture 24 Prototyping with AI

Section 5: Data Management for AI & Data

Lecture 25 Data Growth Strategy

Lecture 26 Open Data

Lecture 27 Company Data

Lecture 28 Crowdsourcing Labeled Data

Lecture 29 New Feature Data

Lecture 30 Acquisition/Purchase Data Collection

Lecture 31 Databases, Data Warehouses, & Data Lakes

Section 6: Product Development for AI & Data

Lecture 32 AI Flywheel Effect

Lecture 33 Top & Bottom Problem Solving

Lecture 34 Product Ideation Techniques

Lecture 35 Complexity vs. Benefit Prioritization

Lecture 36 MVPs & MVDs (Minimum Viable Data)

Lecture 37 Agile & Data Kanban

Section 7: Building The Model

Lecture 38 Who Should Buid Your Model

Lecture 39 Enterpise AI

Lecture 40 Machine Learning as a Service (MLaaS)

Lecture 41 In-House AI & The Machine Learning Lifecycle

Lecture 42 Timelines & Diminishing Returns

Lecture 43 Setting a Model Performance Metric

Section 8: Evaluating Performance

Lecture 44 Dividing Test Data

Lecture 45 The Confusion Matrix

Lecture 46 Precision, Recall & F1 Score

Lecture 47 Optimizing for Experience

Lecture 48 Error Recovery

Section 9: Deployment & Continuous Improvement

Lecture 49 Model Deployment Methods

Lecture 50 Monitoring Models

Lecture 51 Selecting a Feedback Metric

Lecture 52 User Feedback Loops

Lecture 53 Shadow Deployments

Section 10: Managing Data Science & AI Teams

Lecture 54 AI Hierarchy of Needs

Lecture 55 AI Within an Organization

Lecture 56 Roles in AI & Data Teams

Lecture 57 Managing Team Workflow

Lecture 58 Dual & Triple-Track Agile

Section 11: Communication

Lecture 59 Internal Stakeholder Management

Lecture 60 Setting Data Expectations

Lecture 61 Active Listening & Communication

Lecture 62 Compelling Presentations with Storytelling

Lecture 63 Running Effective Meetings

Section 12: Ethics, Privacy, & Bias

Lecture 64 AI User Concerns

Lecture 65 Bad Actors & Security

Lecture 66 AI Amplifying Human Bias

Lecture 67 Data Laws & Regulations

You should take this course if you want to become a Product Manager or if you want to learn about the field of AI and Data Science,This course is for you if you want a great career,The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills