The Product Management For Data Science Ai

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

Last updated 2022-01-10 | 4.5

- 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

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

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

Course content

12 sections • 73 lectures

Intro to Product Management for AI & Data 6 lectures

Introduction Preview 04:03

Course Overview Preview 02:52

Growing Importance of an AI & Data PM Preview 03:04

The Role of a Product Manager Preview 04:48

Differentiation of a PM in AI & Data Preview 03:25

Product Management vs. Project Management Preview 04:03

Key Technological Concepts for AI & Data 10 lectures

A Product Manager as an Analytics Translator Preview 03:49

Data Analysis vs. Data Science Preview 02:59

A Traditional Algorithm vs. AI Preview 05:48

AI vs Traditional Algorithm

Select which of the following leverage AI

Explaining Machine Learning Preview 06:02

Explaining Deep Learning Preview 05:15

When to use Machine Learning vs. Deep Learning Preview 06:03

Machine Learning or Deep Learning

Identify if a problem statement is best suited for Machine Learning or Deep Learning.

Supervised, Unsupervised, & Reinforcement Learning Preview 04:53

Supervised Learning, Unsupervised Learning or Reinforcement Learning

Identify if a problem statement is best suited for Supervised Learning, Unsupervised Learning or Reinforcement Learning.

Business Strategy for AI & Data 7 lectures

AI Business Model Innovations Preview 04:54

When to Use AI Preview 03:59

SWOT Analysis Preview 03:31

Building a Hypothesis Preview 04:11

Testing a Hypothesis Preview 03:46

AI Business Canvas Preview 04:01

Dr.DermaApp Case Study

Filling out an AI Business Canvas for a hypothetical business.

User Experience for AI & Data 5 lectures

User Experience for Data & AI Preview 03:58

Getting to the Core Problem Preview 04:19

User Research Methods Preview 04:27

Developing User Personas Preview 04:20

Prototyping with AI Preview 04:25

Data Management for AI & Data 8 lectures

Data Growth Strategy Preview 05:37

Open Data Preview 02:58

Company Data Preview 03:08

Crowdsourcing Labeled Data Preview 06:44

New Feature Data Preview 04:16

Acquisition/Purchase Data Collection Preview 03:29

Data Collection Needs Matching

Identify which data collection option is most suitable for each problem statement.

Databases, Data Warehouses, & Data Lakes Preview 03:50

Product Development for AI & Data 6 lectures

AI Flywheel Effect Preview 03:22

Top & Bottom Problem Solving Preview 03:15

Product Ideation Techniques Preview 04:27

Complexity vs. Benefit Prioritization Preview 05:47

MVPs & MVDs (Minimum Viable Data) Preview 05:43

Agile & Data Kanban Preview 04:54

Building The Model 6 lectures

Who Should Buid Your Model Preview 05:07

Enterpise AI Preview 04:30

Machine Learning as a Service (MLaaS) Preview 04:32

In-House AI & The Machine Learning Lifecycle Preview 03:26

Timelines & Diminishing Returns Preview 04:42

Setting a Model Performance Metric Preview 04:39

Evaluating Performance 6 lectures

Dividing Test Data Preview 04:21

The Confusion Matrix Preview 03:15

Precision, Recall & F1 Score Preview 03:48

Optimizing for Experience Preview 06:23

Error Recovery Preview 03:46

AutoBikerz Case Study

Calculating the Precision, Recall and F1 Score of two models that are built to help the AutoBikerz autonomous driving motorcycle determine when to stop. Make a recommendation on which model AutoBikerz should use.