AI Strategy and Governance from Coursera

What's inside

Syllabus

Module 1 – Economics of AI

In this module, you will begin by examining the key inputs to AI and what tools are currently used to lower the barriers of entry for AI use. Next, you will learn the economics of AI and the competition that has emerged as AI becomes more crucial to support industry needs and we see more cloud adoption. You will learn about the value of data as it is tied to Deep Learning, and how AutoML is changing the landscape of Machine Learning, and the growing competition and implications of data harvesting. By the end of this module, you will have gained knowledge about the economic implications of AI and Machine Learning and how they impact our lives in unseen ways. You will also understand the complex nature of computational hardware and how that affects consumer demand, but also the demand for privacy.

Module 2 – AI Innovation

In this module, you will examine AI and data analytics to show the economical use-cases of Big Data. You will also learn about the methods and tools that are being used to lower the barriers of entry for AI use. You will review current examples of Big Data and how those firms are using their analytical tools to enhance productivity and transformation. Lastly, you will get an in-depth look at how AI can be used in BioPharma and how the payoff of their AI investment is revitalizing their industry. By the end of this module, you will have a firm grasp on the practical deployment of AI across different industries, their use-cases, and how you can best implement them to drive innovation and transformation within business.

Module 3 – Algorithmic Bias and Fairness

In this module, you will examine the inherent bias that can exist within data based on human behaviors. Building on these foundations, you will explore different responses within algorithmic bias and how organizations should respond and overcome these challenges. You will then review the manipulation of data, the different kinds of manipulation, and ways to ethically approach these issues. Lastly, you will examine data protection and the legal frameworks that exist to protect the consumer and individual data, and the stages of the privacy lifecycle. By the end of this module, you will have a thorough understanding of data biases, manipulation, and ethical questions of how data is handled and stored. You will be able to implement fairer algorithms and understand the legal ramifications of improperly managing data you collect.

Module 4 – AI Governance and Explainable AI

In this module, you will learn about explainable AI and its relationship to Deep Learning. You will also review why it is important to have explainable AI and the different approaches to creating fair algorithms and AI policies. You will also examine Explainable AI and review the necessity of equitable algorithms. You will also learn why we do not always use Explainable AI for every model, and the impacts that it can have on performance. By the end of this module, you will have gained insight into decision-making with AI and the importance of fairness and transparency in creating explainable AI systems, as well as the ethical principles and governance policies that build trust in using AI and Machine Learning.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches AI and data analytics, which is used by firms for productivity and transformation

Develops knowledge about computational hardware and demand for privacy

Examines bias in data based on human behaviors

Taught by Kevin Werbach, Kartik Hosanagar, Lynn Wu, Prasanna Tambe, who have experience with AI and Machine Learning

Develops skills with Explainable AI and Deep Learning

Taught by faculty with industry experience

Examines manipulation of data and ethical approaches

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AI Strategy and Governance with these activities:

Review your notes from previous AI courses

Show steps

This activity will help you refresh your knowledge of AI concepts before starting this course.

Show steps

Gather your notes from previous AI courses
Review your notes

Complete the exercises in the course textbook

Show steps

These exercises will help you reinforce your understanding of the concepts covered in the course.

Show steps

Open your textbook
Complete the exercises at the end of each chapter
Check your answers against the answer key

Follow a tutorial on building an AI-powered chatbot

Show steps

This activity will help you develop your practical AI skills and gain hands-on experience in building AI-powered applications.

Show steps

Choose a tutorial on building an AI-powered chatbot
Follow the tutorial step-by-step
Test your chatbot and make improvements

Six other activities

Expand to see all activities and additional details

Show all nine activities

Solve data modeling and visualization problems on LeetCode

Show steps

These problems will help you reinforce your understanding of data modeling and visualization techniques.

Browse courses on Data Modeling

Show steps

Register for a LeetCode account
Complete the data modeling and visualization problems
Review your solutions and identify areas for improvement

Create an AI governance policy for your organization

Show steps

This activity will help you develop your AI governance skills and ensure that your organization uses AI responsibly.

Browse courses on AI Governance

Show steps

Research AI governance best practices
Develop an AI governance policy that is tailored to your organization's needs
Implement your AI governance policy
Monitor and evaluate your AI governance policy

Write a blog post or article on a topic related to AI ethics

Show steps

This activity will help you develop your critical thinking and writing skills, as well as your understanding of the ethical implications of AI.

Browse courses on AI Ethics

Show steps

Choose a topic related to AI ethics
Research the topic thoroughly
Write an outline for your blog post or article
Draft your blog post or article
Revise and edit your blog post or article
Publish your blog post or article

Contribute to an open-source AI project

Show steps

This activity will help you gain real-world AI development experience and contribute to the AI community.

Browse courses on AI Development

Show steps

Find an open-source AI project on GitHub or another platform
Identify an issue or feature that you can contribute to
Fork the project and make your changes
Submit a pull request with your changes

Read A Human's Guide to Machine Intelligence

Show steps

This book provides extensive additional information on topics covered in this course.

View A Human's Guide to Machine Intelligence: How... on Amazon

Show steps

Purchase the book from the publisher or a retailer
Begin reading the book
Take notes as you read
Complete the book

Develop an AI solution to a real-world problem

Show steps

This activity will allow you to apply your AI knowledge and skills to solve a real-world problem.

Browse courses on AI Applications

Show steps

Identify a real-world problem that you can solve with AI
Gather data and explore different AI techniques
Develop and train your AI model
Test and evaluate your AI model
Deploy your AI solution

Career center

Learners who complete AI Strategy and Governance will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist is someone who would be highly involved in the day-to-day processes an operations team follows to implement and maintain AI. This course teaches about the different uses for AI that are available to an enterprise. It also emphasizes the tools that are required to lower the barriers to the implementation of AI. You can also learn about explainable AI and responsible AI governance algorithms. By using this course, a Data Scientist could grow their understanding of the field and be a more valuable member of their team.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

As Machine Learning Engineers work on the design, implementation, and maintenance of machine learning algorithms, this course would be a very useful one. It teaches the attendee about the inner workings of AI, machine learning, and the importance of using Explainable AI. By taking this course, a Machine Learning Engineer has the opportunity to learn more about responsible AI governance as well as the different tools and resources that are used for AI and Machine Learning.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

This course focuses on the tools and strategies businesses can use to get a competitive advantage through the implementation of AI. For this reason, it may be very helpful to a Data Analyst with an interest in moving into a management position. The course provides knowledge about the economics, innovation, and governance of AI and machine learning.

See salaries and explore the career path for Data Analyst

Business Analyst

A Business Analyst who has an interest in helping their organization with moving to new technologies may be interested in this course. This course teaches about AI and the strategies that are used in transforming business in order to gain a competitive advantage. The attendee will learn about the multitude of uses for AI in an enterprise setting and the tools that are available to lower the barriers to AI use. By taking this course, a Business Analyst may be able to successfully present AI to an organization's leadership and help their organization.

See salaries and explore the career path for Business Analyst

Product Manager

This course may be helpful to a Product Manager that is working to develop an AI-based product. The program will provide knowledge of AI, innovation, and governance. By learning about the economics of AI, the Product Manager will be able to make better decisions about the pricing model for their future product.

See salaries and explore the career path for Product Manager

Software Engineer

A Software Engineer that wants to work on products that utilize AI will find this course helpful. Among other things, the course will teach the attendee about the different uses for AI in an enterprise setting and the tools that are available to lower the barriers to AI use.

See salaries and explore the career path for Software Engineer

Quantitative Analyst

For a Quantitative Analyst that wants to learn more about data science and AI, this course could be a good choice. The course will teach the attendee about the purpose, function, and use-cases for explainable AI. They will also learn how to build responsible AI governance algorithms.

See salaries and explore the career path for Quantitative Analyst

Data Engineer

This course could help build a foundation for someone that wants to become a Data Engineer. It will teach the attendee about the large datasets that can be expected in an enterprise setting and how those datasets affect the business. They will also get a closer look at the purpose and function of explainable AI.

See salaries and explore the career path for Data Engineer

Statistician

This course will be of particular interest to a Statistician who is working with a business on AI implementation. This is because the course teaches about the tools and strategies businesses can use to get a competitive advantage through the implementation of AI.

See salaries and explore the career path for Statistician

Financial Analyst

A Financial Analyst who wants to learn about the economics of AI may want to consider taking this course. The course will provide the attendee with a knowledge of the economics of AI and machine learning. They will also learn how AI is playing a crucial role in change management.

See salaries and explore the career path for Financial Analyst

Actuary

Although an Actuary may not be directly involved with AI implementation, this course may still be helpful. The attendee will learn about the economics of AI and machine learning, which may be an important consideration when calculating and pricing products.

See salaries and explore the career path for Actuary

Operations Research Analyst

This course teaches about AI governance and explainable AI. This could be useful to an Operations Research Analyst that is working on the implementation of AI within their organization.

See salaries and explore the career path for Operations Research Analyst

Risk Analyst

This course may be of interest to a Risk Analyst who is interested in learning more about AI and its governance. It teaches about AI governance and explainable AI. The attendee will also learn about the risks with AI processes.

See salaries and explore the career path for Risk Analyst

Management Consultant

A management consultant may work with a wide range of clients and business. Some of those may be exploring or implementing AI technologies. By understanding the concepts of AI, this course helps these professionals make recommendations about how AI can be used to benefit their clients.

See salaries and explore the career path for Management Consultant

Business Intelligence Analyst

This course teaches the attendee about the practical deployment of AI across different industries, their use-cases, and how you can best implement them to drive innovation and transformation within business. The course also goes over explainable AI and its relationship to Deep Learning. This makes the course a good choice for a Business Intelligence Analyst.

See salaries and explore the career path for Business Intelligence Analyst