What you will learn in Generative AI for Data Engineers Specialization Course
Understand the fundamentals of generative AI and its distinction from discriminative AI.
Master prompt engineering techniques to effectively guide generative AI models.
Apply generative AI tools in data engineering tasks such as data generation, augmentation, anonymization, and infrastructure setup.
Implement generative AI in building data pipelines, ETL workflows, and querying databases.
Gain hands-on experience through labs and projects to solidify your understanding and skills.
Program Overview
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 Engineering Career
⏳ 13 hours
- Apply generative AI tools and techniques in data engineering processes such as data warehouse schema design, infrastructure setup, data pipelines, and ETL workflows.
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Job Outlook
Completing this specialization prepares you for roles such as Data Engineer, Data Analyst, or Business Intelligence Analyst.
The skills acquired are applicable across various industries that utilize data engineering and AI technologies.
Enhance your employability by gaining practical experience in applying generative AI to data engineering workflows.
Specification: Generative AI for Data Engineers Specialization
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FAQs
- No prior AI or data engineering experience is required.
- Suitable for beginners with a basic understanding of databases or programming.
- Focuses on practical integration of generative AI into workflows.
- Step-by-step labs help learners gain hands-on experience.
- Encourages understanding AI concepts alongside real-world applications.
- Prepares learners for roles like Data Engineer, Data Analyst, or BI Analyst.
- Teaches practical use of generative AI in data pipelines and ETL workflows.
- Enhances employability by providing modern AI integration skills.
- Builds a portfolio of hands-on projects to demonstrate expertise.
- Knowledge is applicable across industries using AI and data engineering.
- Access to cloud-based AI tools and platforms for lab exercises.
- Basic programming environment (Python) for scripting AI workflows.
- Familiarity with databases is helpful but not mandatory.
- Course provides guidance on using required software effectively.
- No expensive or proprietary tools are required for beginners.
- Regular hands-on exercises help reinforce concepts and techniques.
- Work on small data projects to understand AI integration.
- Repeat prompt engineering tasks to improve accuracy and efficiency.
- Experiment with data augmentation and generation to build confidence.
- Review completed projects to refine workflow and troubleshooting skills.
- Explore advanced AI and machine learning specializations.
- Study optimization, model fine-tuning, and large-scale AI deployments.
- Join AI and data engineering communities for collaboration and guidance.
- Experiment with real-world data sets and production workflows.
- Build a portfolio of projects to strengthen professional opportunities.
