image
The Ultimate Drawing Course Beginner to Advanced...
$179
$79
image
User Experience Design Essentials - Adobe XD UI UX...
$179
$79
Total:
$659

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 recommendation engines, companies are increasingly leveraging big data and artificial intelligence, including cutting-edge tools like ChatGPT and other large language models (LLMs), to enhance operations and product offerings. In just the past few years, organizational adoption of AI has surged by 270%, driven by breakthroughs in natural language processing and machine learning. As businesses race to implement these technologies, there is a growing demand for skilled professionals who can manage AI and big data projects. In this context, a product manager plays a crucial role, bridging the gap between business goals and the technical expertise of data scientists and AI specialists.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: $128,992)
Promotions. Product Managers work closely with division heads and high - level executives, which makes them the leading candidates for senior roles within a corporation
Secure Future. There is a high demand for Product Managers on the job market
Growth. This isn’t a boring job. Every day, you will face different challenges that will test your existing skills
Just 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!
Who this course is for:
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

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

  • You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
  • No previous design experience is needed.
  • No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures
Expand All Sections
1-Intro to Product Management for AI & Data
6
1.1-Introduction
1.2-Course Overview
1.3-Growing Importance of an AI & Data PM
1.4-The Role of a Product Manager
1.5-Differentiation of a PM in AI & Data
1.6-Product Management vs. Project Management
2-Key Technological Concepts for AI & Data
10
2.1-A Product Manager as an Analytics Translator
2.2-Data Analysis vs. Data Science
2.3-A Traditional Algorithm vs. AI
2.4-AI vs Traditional Algorithm
2.5-Explaining Machine Learning
2.6-Explaining Deep Learning
2.7-When to use Machine Learning vs. Deep Learning
2.8-Machine Learning or Deep Learning
2.9-Supervised, Unsupervised, & Reinforcement Learning
2.10-Supervised Learning, Unsupervised Learning or Reinforcement Learning
3-Business Strategy for AI & Data
7
3.1-AI Business Model Innovations
3.2-When to Use AI
3.3-SWOT Analysis
3.4-Building a Hypothesis
3.5-Testing a Hypothesis
3.6-AI Business Canvas
3.7-Dr.DermaApp Case Study
4-User Experience for AI & Data
5
4.1-User Experience for Data & AI
4.2-Getting to the Core Problem
4.3-User Research Methods
4.4-Developing User Personas
4.5-Prototyping with AI
5-Data Management for AI & Data
8
5.1-Data Growth Strategy
5.2-Open Data
5.3-Company Data
5.4-Crowdsourcing Labeled Data
5.5-New Feature Data
5.6-Acquisition/Purchase Data Collection
5.7-Data Collection Needs Matching
5.8-Databases, Data Warehouses, & Data Lakes
6-Product Development for AI & Data
6
6.1-AI Flywheel Effect
6.2-Top & Bottom Problem Solving
6.3-Product Ideation Techniques
6.4-Complexity vs. Benefit Prioritization
6.5-MVPs & MVDs (Minimum Viable Data)
6.6-Agile & Data Kanban
7-Building The Model
6
7.1-Who Should Buid Your Model
7.2-Enterpise AI
7.3-Machine Learning as a Service (MLaaS)
7.4-In-House AI & The Machine Learning Lifecycle
7.5-Timelines & Diminishing Returns
7.6-Setting a Model Performance Metric
8-Evaluating Performance
6
8.1-Dividing Test Data
8.2-The Confusion Matrix
8.3-Precision, Recall & F1 Score
8.4-Optimizing for Experience
8.5-Error Recovery
8.6-AutoBikerz Case Study
9-Deployment & Continuous Improvement
5
9.1-Model Deployment Methods
9.2-Monitoring Models
9.3-Selecting a Feedback Metric
9.4-User Feedback Loops
9.5-Shadow Deployments
10-Managing Data Science & AI Teams
5
10.1-AI Hierarchy of Needs
10.2-AI Within an Organization
10.3-Roles in AI & Data Teams
10.4-Managing Team Workflow
10.5-Dual & Triple-Track Agile
11-Communication
5
11.1-Internal Stakeholder Management
11.2-Setting Data Expectations
11.3-Active Listening & Communication
11.4-Compelling Presentations with Storytelling
11.5-Running Effective Meetings
12-Ethics, Privacy, & Bias
5
12.1-AI User Concerns
12.2-Bad Actors & Security
12.3-AI Amplifying Human Bias
12.4-Data Laws & Regulations
12.5-Bonus Lecture