❗The content presented here is sourced directly from Edx platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [March 06th, 2023]

This course, Applied Data Science Ethics, provides guidance and practical tools to build better models, do better data analysis and avoid potential ethical issues. Participants will learn about tools for model interpretability, global versus local model interpretability methods, metrics for model fairness, auditing models for bias and fairness, and remedies for biased models. Real world problems and datasets, a framework data scientists can use to develop their projects, and an audit process to follow in reviewing them will be provided. Case studies with ethical considerations, along with Python code, will also be included.

[Applications]
Upon completion of this course, participants are encouraged to apply the knowledge and skills acquired to their own data science projects. They should use the tools and techniques learned to ensure that their models are interpretable, fair, and free from bias. Additionally, they should use the audit process to review their models for any potential ethical issues. Finally, they should be aware of the remedies available to address any issues that may arise.

[Career Paths]
The career paths recommended to learners of this course are:

1. Data Scientist: Data Scientists are responsible for analyzing large datasets to uncover trends and insights, and developing models to predict outcomes. They must be able to interpret and communicate their findings to stakeholders, and be knowledgeable about ethical considerations when working with data. The demand for Data Scientists is growing rapidly, and the field is expected to continue to expand in the coming years.

2. Data Ethics Consultant: Data Ethics Consultants are responsible for advising organizations on ethical considerations when working with data. They must be knowledgeable about data science principles and be able to identify potential ethical issues in data-driven projects. The demand for Data Ethics Consultants is increasing as organizations become more aware of the potential risks associated with data-driven projects.

3. Data Governance Analyst: Data Governance Analysts are responsible for ensuring that data is collected, stored, and used in a secure and ethical manner. They must be knowledgeable about data privacy laws and regulations, and be able to identify potential risks associated with data-driven projects. The demand for Data Governance Analysts is increasing as organizations become more aware of the potential risks associated with data-driven projects.

4. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They must be knowledgeable about data science principles and be able to identify potential ethical issues in data-driven projects. The demand for Machine Learning Engineers is increasing as organizations become more aware of the potential benefits of machine learning.

[Education Paths]
Recommended degree paths:
1. Bachelor of Science in Data Science: This degree program provides students with a comprehensive understanding of data science principles and techniques, including data analysis, machine learning, and data visualization. Students will learn how to use data to solve real-world problems and develop the skills to become a successful data scientist. The degree also covers topics such as ethics, privacy, and security.

2. Master of Science in Artificial Intelligence: This degree program provides students with a comprehensive understanding of artificial intelligence principles and techniques, including machine learning, natural language processing, and robotics. Students will learn how to use AI to solve real-world problems and develop the skills to become a successful AI engineer. The degree also covers topics such as ethics, privacy, and security.

3. Doctor of Philosophy in Data Science: This degree program provides students with a comprehensive understanding of data science principles and techniques, including data analysis, machine learning, and data visualization. Students will learn how to use data to solve real-world problems and develop the skills to become a successful data scientist. The degree also covers topics such as ethics, privacy, and security.

4. Master of Science in Data Ethics: This degree program provides students with a comprehensive understanding of data ethics principles and techniques, including ethical decision-making, data privacy, and data security. Students will learn how to use data to make ethical decisions and develop the skills to become a successful data ethicist. The degree also covers topics such as machine learning, artificial intelligence, and data visualization.

The development trends for these degree paths are focused on the ethical implications of data science and artificial intelligence. As the use of data and AI increases, so does the need for data scientists and AI engineers to understand the ethical implications of their work. As a result, universities are offering more courses and degree programs focused on data ethics, as well as incorporating ethical considerations into existing data science and AI courses.