| File Name: | The Product Management for AI & Data Science Course |
| Content Source: | https://www.udemy.com/course/the-product-management-for-data-science-ai-course/ |
| Genre / Category: | Other Tutorials |
| File Size : | 1.8 GB |
| Publisher: | 365 Careers |
| Updated and Published: | February 4, 2026 |
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.
DOWNLOAD LINK: The Product Management for AI & Data Science Course
The_Product_Management_for_AI_Data_Science_Course.part1.rar – 1000.0 MB
The_Product_Management_for_AI_Data_Science_Course.part2.rar – 883.4 MB
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