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
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 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!
