Generative AI for Data Science

Generative AI for Data Science

Taught in English


Gain insight into a topic and learn the fundamentals


Instructor: Microsoft

Intermediate level

Recommended experience

3 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Proficiency in Gen AI within the data science landscape, specifically in marketing and advertising.

  • Exposure to many Gen AI applications, including data augmentation, feature engineering, anomaly detection, and generative modeling.

  • Run large language models locally, identify and implement specific use cases for GenAI, and join conversations about data security and privacy.

  • Explore ethical and operational implications of Gen AI in data science, and integrate its innovative potential into practices through integrity.

Details to know

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June 2024


5 assignments

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There are 5 modules in this course

Upon completing this course, you will be proficient in harnessing the transformative capabilities of generative AI (GenAI) within the data science landscape, specifically in marketing and advertising. Additionally, you will explore the ethical and operational implications of GenAI in data science. By the end of the course, you will be equipped to integrate the innovative potentials of GenAI technologies into your practices, effectively balancing innovation with integrity.

What's included

1 video1 reading

By the end of this lesson, you will understand how Generative AI is transforming Data Science. We'll explore how these models identify data patterns to create original content, improve fluorescence microscopy by reducing cell damage, enhance anomaly detection in datasets, and revolutionize SMS marketing to keep brand consistency. This lesson will show the wide applications and benefits of Generative AI in various data science challenges.

What's included

5 videos1 assignment

By the end of this lesson, you will learn about the applications and benefits of Generative AI in data science, especially for optimizing local LLM (Large Language Model) deployments. We'll cover the advantages of running models locally, such as faster iteration speeds, and the computational demands of large models. You'll also learn about quantization techniques to enhance training and reduce memory usage, as well as the LoRA technique for fine-tuning. Finally, you'll see a practical demo of fine-tuning an open-source model using both LoRA and quantization, giving you practical skills to improve AI model efficiency locally.

What's included

5 videos1 assignment

By the end of this lesson, you will learn how generative AI improves feature engineering in SMS campaign data. This AI automates the extraction of complex patterns and relationships, making it more efficient and powerful than traditional manual methods. We'll also discuss how previous techniques required extensive domain expertise and often lacked scalability and adaptability. Additionally, you'll get a tutorial on using a generative AI model to automatically label different parts of SMS campaign messages with a step-by-step code walkthrough in Python. This approach will show you how generative AI transforms raw data into actionable insights for better campaign management.

What's included

3 videos4 readings1 assignment

By the end of this lesson, you will be able to analyze the security and privacy impacts of Generative AI in data science. We'll explore ethical issues like data privacy, consent, and bias, and discuss how to develop and deploy AI responsibly. You'll learn about creating synthetic data using methods like differential privacy and data anonymization to ensure ethical compliance. This lesson aims to help you make responsible decisions and think critically about ethical issues in AI applications, preparing you to handle complex challenges in data science.

What's included

4 videos1 reading2 assignments


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