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We're here to help you find itEthics in AI and Data Science (LFS112) Course Overview
Explore the Ethics in AI and Data Science (LFS112) course to master the ethical principles crucial for your AI and Data Science projects. In just 4 hours, this course equips business, government, and tech leaders, as well as data scientists, with the skills needed to implement transparency, build trust, and lead responsibly.
Delve into three core chapters:
1. The State of Ethics, Trust & Responsibility with AI and Data Science
2. Understanding AI and Data Science and Their Importance
3. Strategies and Challenges of Ethical Practice
Learn to apply these concepts practically to drive adoption and credibility in your initiatives.
Purchase This Course
USD
View Fees Breakdown
Flexi Video | 16,449 |
Official E-coursebook | |
Exam Voucher (optional) | |
Hands-On-Labs2 | 4,159 |
+ GST 18% | 4,259 |
Total Fees (without exam & Labs) |
22,359 (INR) |
Total Fees (with exam & Labs) |
28,359 (INR) |
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
The "Ethics in AI and Data Science (LFS112)" course is designed to be accessible to individuals from various professional backgrounds. However, to ensure a productive learning experience, it is recommended that participants meet the following minimum prerequisites:
These prerequisites are designed to ensure that you can effectively engage with the course material and apply the ethical principles and frameworks discussed to your specific context.
Introduction:
The Ethics in AI and Data Science (LFS112) course equips leaders and data scientists with the knowledge to embed ethical principles in AI and data science for transparency and trust.
Target Audience:
The Ethics in AI and Data Science (LFS112) course aims to equip leaders and data scientists with the knowledge and strategies needed to incorporate ethical principles and frameworks into AI and data science initiatives, focusing on transparency, trust, and responsibility.