Generative AI for Data Science with Copilot

Microsoft via Coursera

Go to Course: https://www.coursera.org/learn/generative-ai-for-data-science-with-copilot

Introduction

Define and differentiate types of generative AI models

Use Microsoft Copilot to generate code, analyze data, and build generative models

Identify practical use cases for generative AI in data science, such as data augmentation and anomaly detection

Assess the strengths and weaknesses of different generative models and understand their ethical implications

Syllabus

Foundations of generative AI and Microsoft Copilot

This module provides a comprehensive introduction to generative AI, exploring its definition, key concepts like GANs, VAEs, and Transformers, and highlighting the role of Microsoft Copilot in enhancing data science workflows through code generation, data analysis, and bias mitigation. It also addresses the ethical implications of generative AI and provides practical guidance on integrating Copilot into existing data science practices.

Generative AI use cases in data science with Copilot

This module dives into practical applications of generative AI in data science, demonstrating how tools like Microsoft Copilot can be used to augment data, uncover hidden patterns, detect anomalies, and simulate scenarios for enhanced decision-making and risk management.

Data security and privacy in generative AI

This module dives into the data security and privacy challenges of generative AI, focusing on Microsoft Copilot. You'll learn about potential risks like data breaches and the creation of misleading information, while also exploring strategies and techniques to safeguard data and ensure responsible AI use.