The Product Management for AI & Data Science Course
The Product Management for AI & Data Science Course, available at $94.99, has an average rating of 4.52, with 68 lectures, 4 quizzes, based on 6285 reviews, and has 35592 subscribers.
You will learn about 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 This course is ideal for individuals who are 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 or This course is for you if you want a great career or The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills It is particularly useful 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 or This course is for you if you want a great career or The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills.
Enroll now: The Product Management for AI & Data Science Course
Summary
Title: The Product Management for AI & Data Science Course
Price: $94.99
Average Rating: 4.52
Number of Lectures: 68
Number of Quizzes: 4
Number of Published Lectures: 68
Number of Published Quizzes: 4
Number of Curriculum Items: 74
Number of Published Curriculum Objects: 74
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- 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
Target Audiences
- 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
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?
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Salary. A Product Manager job usually leads to a very well-paid career (average salary reported on Glassdoor: $108,992)
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Promotions. Product Managers work closely with division heads and high – level executives, which makes them the leading candidates for senior roles within a corporation
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Secure Future. There is a high demand for Product Managers on the job market
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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!
Course Curriculum
Chapter 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
Chapter 2: Key Technological Concepts for AI & Data
Lecture 1: A Product Manager as an Analytics Translator
Lecture 2: Data Analysis vs. Data Science
Lecture 3: A Traditional Algorithm vs. AI
Lecture 4: Explaining Machine Learning
Lecture 5: Explaining Deep Learning
Lecture 6: When to use Machine Learning vs. Deep Learning
Lecture 7: Supervised, Unsupervised, & Reinforcement Learning
Chapter 3: Business Strategy for AI & Data
Lecture 1: AI Business Model Innovations
Lecture 2: When to Use AI
Lecture 3: SWOT Analysis
Lecture 4: Building a Hypothesis
Lecture 5: Testing a Hypothesis
Lecture 6: AI Business Canvas
Chapter 4: User Experience for AI & Data
Lecture 1: User Experience for Data & AI
Lecture 2: Getting to the Core Problem
Lecture 3: User Research Methods
Lecture 4: Developing User Personas
Lecture 5: Prototyping with AI
Chapter 5: Data Management for AI & Data
Lecture 1: Data Growth Strategy
Lecture 2: Open Data
Lecture 3: Company Data
Lecture 4: Crowdsourcing Labeled Data
Lecture 5: New Feature Data
Lecture 6: Acquisition/Purchase Data Collection
Lecture 7: Databases, Data Warehouses, & Data Lakes
Chapter 6: Product Development for AI & Data
Lecture 1: AI Flywheel Effect
Lecture 2: Top & Bottom Problem Solving
Lecture 3: Product Ideation Techniques
Lecture 4: Complexity vs. Benefit Prioritization
Lecture 5: MVPs & MVDs (Minimum Viable Data)
Lecture 6: Agile & Data Kanban
Chapter 7: Building The Model
Lecture 1: Who Should Buid Your Model
Lecture 2: Enterpise AI
Lecture 3: Machine Learning as a Service (MLaaS)
Lecture 4: In-House AI & The Machine Learning Lifecycle
Lecture 5: Timelines & Diminishing Returns
Lecture 6: Setting a Model Performance Metric
Chapter 8: Evaluating Performance
Lecture 1: Dividing Test Data
Lecture 2: The Confusion Matrix
Lecture 3: Precision, Recall & F1 Score
Lecture 4: Optimizing for Experience
Lecture 5: Error Recovery
Chapter 9: Deployment & Continuous Improvement
Lecture 1: Model Deployment Methods
Lecture 2: Monitoring Models
Lecture 3: Selecting a Feedback Metric
Lecture 4: User Feedback Loops
Lecture 5: Shadow Deployments
Chapter 10: Managing Data Science & AI Teams
Lecture 1: AI Hierarchy of Needs
Lecture 2: AI Within an Organization
Lecture 3: Roles in AI & Data Teams
Lecture 4: Managing Team Workflow
Lecture 5: Dual & Triple-Track Agile
Chapter 11: Communication
Lecture 1: Internal Stakeholder Management
Lecture 2: Setting Data Expectations
Lecture 3: Active Listening & Communication
Lecture 4: Compelling Presentations with Storytelling
Lecture 5: Running Effective Meetings
Chapter 12: Ethics, Privacy, & Bias
Lecture 1: AI User Concerns
Lecture 2: Bad Actors & Security
Lecture 3: AI Amplifying Human Bias
Lecture 4: Data Laws & Regulations
Lecture 5: Bonus Lecture
Instructors
-
365 Careers
Creating opportunities for Data Science and Finance students -
Danielle Thé
Senior Product Manager for Applied Machine Learning
Rating Distribution
- 1 stars: 94 votes
- 2 stars: 118 votes
- 3 stars: 614 votes
- 4 stars: 2208 votes
- 5 stars: 3251 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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