Product Management for AI & Data Science
Course descriptionThis course is designed to teach you the specialized skills needed to manage the development of successful A.I. products. You'll learn how to identify opportunities for a business to leverage AI and how to prepare the data needed. You will grasp the organizational structure of A.I. and data science teams, how to communicate effectively with stakeholders, and the best methods for managing a team's workflow. After the course, you'll be able to take an idea, evaluate it with potential customers, prototype, strategize, build tests and iterate on AI and data driven products.
Key Technological Concepts for AI & Data Science
This section explains some key technological concepts for AI and data science. You will learn how to distinguish between data analysis and data science, how an algorithm differs from AI, and when to choose machine learning vs deep learning methods. You will also decipher the types of machine learning (supervised, unsupervised, and reinforcement learning).
Business Strategy for AI & Data Science
This section focuses on business strategy for AI and data science. You will discover when a company needs to use AI, as well as how to perform a SWOT analysis, and how to build and test a hypothesis. Here, you will receive your first assignment – to create a business proposal.
Data Management for AI & Data Science
Here, we draw attention to data management. You will learn how to source data for your projects and how this data needs to be managed. You will also gain insight into the type of data that you need when working with different types of machine learning.
Product Development for AI & Data Science
In this section, we begin examining the full lifecycle of an AI or data science project in a company. We cover the AI Flywheel Effect, top & bottom problem solving, and how to apply various product ideation techniques. You will learn how to prioritize what products should be developed and when they should be developed. Finally, you will get familiar with MVPs & MVDs, the Agile framework, and Kanban software methodology.
This part of the course is dedicated to everything you need to know about performance evaluation: dividing test data, visualizing the performance of a ML model via confusion matrix, and calculating precision, recall and F1 scores. You will also learn how to use these to determine what model is producing the optimal performance, and how to minimize the negative impact of an error.
Deployment & Continuous Improvement
Here, we examine the three kinds of model deployment methods. The section covers how to use both proactive and reactive monitoring efforts to ensure that your model stays relevant. Further on, you will learn how to select feedback metric, build user feedback loops, and when to use shadow deployment method of testing.
Managing Data Science & AI Teams
Where your A.I. and data team is placed within the organization impacts how you work with your team and across the company. This section describes the AI hierarchy of needs, along with the various roles in AI and data science teams. It also illustrated a simplified workflow of an AI data product and how to minimize asynchrony by adopting a modified dual-track agile methodology.
This section sheds light on how to improve communication between team members and stakeholders, set expectations as a product manager, and develop strong people skills by active listening. You will also learn what is necessary to make memorable presentations, and how to manage meetings more efficiently.
Ethics, Privacy, & Bias
As an AI and data product manager, you should consider ways in which your product can build user trust. In this final section, you will learn about the most common AI user concerns, and how to put security measures in place to prevent manipulation of your model by bad actors once it’s deployed. You will also understand how to vet your models for biases, and get familiar with data privacy laws and regulations.