Overview

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Did you know Generative AI can enhance data accuracy and operational efficiency in data science? This Short Course was created to help data scientists and AI enthusiasts unlock the full potential of Generative AI in their data-driven projects. Within this 3-hour-long commitment, you will learn how to explore and leverage GenAI applications, identify key use cases like data augmentation and anomaly detection, and analyze crucial data security and privacy issues. By completing this course, you'll be able to apply advanced AI techniques to real-world data challenges, ensuring your projects are both innovative and ethically sound. Blending cutting-edge AI technology with practical, industry-specific applications makes this course unique. To be successful in this project, you will need a solid foundation in Python, basic machine learning principles and an understanding of fundamental data science concepts.

Syllabus

Generative AI for Data Science

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.

Lesson 1: Versatility and Impact of GenAI in Data Science

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.

Lesson 2: Running and deploying an LLM locally for Data Science:

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.

Lesson 3: Feature Engineering with High Performance SMS Campaign Data

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.

Lesson 4: Ethical Considerations of GenAI

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.