Generative AI for Data Science with Copilot

Instructor: Microsoft

Beginner Level • 6 hours to complete Recommended experience • Flexible Schedule

What You'll Learn

  • ✓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

Skills You'll Gain

Microsoft Copilot
OpenAI
Data Security
Large Language Modeling
Data Synthesis
Information Privacy
Anomaly Detection
Artificial Intelligence and Machine Learning (AI/ML)
Data analysis
Data Ethics
Generative AI
Prompt Engineering

Shareable Certificate

Earn a shareable certificate to add to your LinkedIn profile

Outcomes

  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 3 modules in this course

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.

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.

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.