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Microsoft

Generative AI for Data Science with Copilot

Microsoft via Coursera

Overview

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This course prepares you to apply Microsoft Copilot to core generative AI use cases in data science, from model building and data augmentation to risk management and responsible AI practices. Designed for data scientists, ML practitioners, and technically oriented data professionals who want to go beyond prompt-based workflows and understand how generative AI actually works — and how to use it responsibly — this course bridges foundational GenAI concepts with practical, Copilot-powered implementation. Whether you're new to generative AI or looking to formalize how it fits into your data science pipeline, you'll build both the theoretical grounding and hands-on skills needed to integrate Copilot into sophisticated, real-world workflows. By the end of this course, you will be able to: Explain core generative AI concepts — including GANs, VAEs, and Transformers — and describe how Microsoft Copilot applies them to data science tasks such as code generation, data analysis, and bias mitigation Use Copilot to perform practical data science tasks, including generating code snippets, augmenting datasets, and implementing generative models Design and evaluate generative models tailored to specific data science tasks, comparing performance against traditional methods Identify data security and privacy risks in generative AI workflows and develop responsible AI strategies to mitigate bias, protect sensitive data, and prevent misinformation Who should take this course: This course is best suited for data scientists, ML engineers, and technically proficient data professionals who want to incorporate generative AI into advanced data science workflows. Familiarity with data science concepts and experience working with datasets is recommended. This course builds on the skills introduced in Courses 1 and 2, though prior completion is not required. Prerequisites: Familiarity with data science concepts and working with datasets Basic understanding of machine learning is helpful but not required No prior generative AI or Copilot knowledge required Access to Microsoft Copilot Required Course Materials: A Copilot license is required to complete this course. If you don't have a Microsoft 365 Personal or Family license, you can start a free 30-day trial using the link provided in the course.

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.

Taught by

Microsoft

Reviews

4.7 rating at Coursera based on 40 ratings

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