What will you learn in this Generative AI for Data Scientists Specialization Course
Understand the fundamentals of generative AI and its applications in data science.
Master prompt engineering techniques to optimize AI outputs.
Utilize generative AI tools like IBM Watsonx, Prompt Lab, Spellbook, and Dust.
Apply generative AI in data augmentation, feature engineering, model development, and visualization.
Gain hands-on experience through real-world projects and scenarios.
Program Overview
Module 1: Getting Started
Course 1: Generative AI: Introduction and Applications
⏳ 7 hours
- Learn the basics of generative AI, its capabilities, and real-world use cases across various industries.
Course 2: Generative AI: Prompt Engineering Basics
⏳ 7 hours
- Delve into prompt engineering concepts, exploring techniques like zero-shot and few-shot prompting, and tools to create effective prompts.
Course 3: Generative AI: Elevate Your Data Science Career
⏳ 14 hours
- Apply generative AI tools and techniques in data science processes such as data preparation, analysis, visualization, and storytelling.
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Job Outlook
Completing this specialization prepares you for roles such as Data Scientist, AI Specialist, or Machine Learning Engineer.
The skills acquired are applicable across various industries that utilize data science and AI technologies.
Enhance your employability by gaining practical experience in applying generative AI to data science workflows.
Specification: Generative AI for Data Scientists Specialization
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FAQs
- Basic knowledge of Python and data science is recommended.
- Familiarity with machine learning concepts is helpful but not mandatory.
- The course introduces generative AI techniques progressively.
- Designed to build both foundational and applied AI skills.
- Suitable for data scientists aiming to leverage generative AI in projects.
- Automates feature engineering and data preprocessing.
- Generates synthetic datasets for testing and training models.
- Enhances predictive modeling and scenario simulation.
- Supports visualization and reporting with AI-generated insights.
- Accelerates experimentation and hypothesis testing in analytics.
- Popular AI/ML libraries such as TensorFlow, PyTorch, or Hugging Face.
- Platforms for deploying and fine-tuning generative models.
- Python-based pipelines for data preparation and analysis.
- Integration with visualization and BI tools for AI-driven insights.
- Hands-on experience with real-world datasets and models.
- Useful in finance, healthcare, marketing, and technology.
- Supports simulation, anomaly detection, and predictive modeling.
- Helps generate synthetic data for research and testing.
- Valuable in startups, large enterprises, and consulting roles.
- Skills are transferable to roles like data scientist, AI engineer, and ML consultant.
- Includes projects using generative AI on real datasets.
- Encourages end-to-end workflow development and deployment.
- Provides exercises for building and fine-tuning models.
- Offers portfolio-ready assignments to demonstrate applied skills.
- Reinforces theoretical knowledge with practical AI applications.
