Generative AI for Data Scientists Specialization Course Syllabus

Overview: This specialization offers a comprehensive, intermediate-level journey into applying generative AI within data science workflows. Designed by IBM, the program blends foundational knowledge with hands-on practice across three core courses. With approximately 28 hours of content, learners engage in self-paced study, exploring key tools and techniques used in real-world data science roles. The curriculum emphasizes practical application through projects and prepares learners for AI-enhanced data science careers.

Module 1: Generative AI: Introduction and Applications

Estimated time: 7 hours

• Introduction to generative AI and core concepts
• Understanding capabilities and limitations of generative models
• Real-world applications across industries
• Overview of generative AI tools and platforms

Module 2: Generative AI: Prompt Engineering Basics

Estimated time: 7 hours

• Foundations of prompt engineering
• Zero-shot and few-shot prompting techniques
• Strategies for refining and optimizing prompts
• Using tools like Prompt Lab for prompt design

Module 3: Generative AI: Elevate Your Data Science Career

Estimated time: 14 hours

• Integrating generative AI into data preparation workflows
• Applying AI for data augmentation and feature engineering
• Enhancing model development with generative techniques
• Using AI for data visualization and storytelling

Module 4: Tools for Generative AI in Data Science

Estimated time: 8 hours

• Hands-on with IBM Watsonx for generative AI tasks
• Exploring Dust for AI workflow automation
• Using Spellbook to streamline AI-assisted coding
• Integrating tools into real-world data pipelines

Module 5: Real-World Applications and Projects

Estimated time: 10 hours

• Designing a generative AI-augmented data analysis project
• Applying prompt engineering to solve data challenges
• Building an end-to-end data science workflow with AI

Module 6: Final Project

Estimated time: 10 hours

• Deliverable 1: Develop a data augmentation pipeline using generative AI
• Deliverable 2: Create a data visualization and narrative enhanced by AI-generated insights
• Deliverable 3: Submit a final report demonstrating prompt engineering and tool integration

Prerequisites

• Familiarity with basic data science concepts
• Some experience with data analysis or programming (helpful but not required)
• Access to IBM tools used in the course

What You'll Be Able to Do After

• Understand and explain the role of generative AI in data science
• Apply prompt engineering techniques to improve AI outputs
• Use generative AI tools like IBM Watsonx, Prompt Lab, and Spellbook effectively
• Enhance data workflows through AI-powered augmentation and visualization
• Advance your data science career with practical AI integration skills