What is this course all about?

This course is all about helping Data Professionals integrate Generative AI into their daily workflows. We’ll take a deep dive into how Generative AI is transforming each step of the data engineering lifecycle, giving practical skills to boost productivity and impact. This isn’t just theory - it’s a hands-on experience filled with real-world insights and techniques to help data professionals navigate the evolving data landscape with confidence. 

How can Generative AI impact Data Engieering?

In this course, we'll focus on seven key archetypes that illustrate the various ways Generative AI impacts Data Engineering.

  • Data Generation and Augmentation

  • Writing Generative AI Code with Gen AI

  • Data Parsing and Extraction

  • Gen AI Data Engineering Tools

  • Data Querying and Analysis

  • Data Enrichment, Normalization, and Standardization

  • Anomaly Detection and Compression

What will you learn?

  • Integrate Generative AI

    Discover how to effectively embed Generative AI into your workflows as a Data Professional. We’ll cover a range of applications, including data generation, analysis, storage, visualization, pipelines, and more. You'll gain practical skills to enhance your daily tasks and incorporate AI into your data processes.

  • Boost Productivity

    Generative AI can significantly improve your productivity. Research from McKinsey shows that it can help you complete data tasks up to 20% faster, and you may find even greater efficiency when incorporating coding into your workflows. You’ll learn strategies to maximize your output and streamline your work.

  • Expand Your Capabilities

    With Generative AI, you’ll be able to take on data tasks that were previously difficult to achieve. For instance, you’ll learn how to extract insights from unstructured text and enhance textual data, enabling better understanding and decision-making. By the end of the course, you’ll feel equipped to address complex data challenges with confidence.

Course curriculum

    1. About the course

    2. How Generative AI impacts Data Engineer Tasks

    3. Course Roadmap

    4. Caveats about Using Generative AI

    5. About the Instructor

    6. Keys to Success

    7. Ways to Contact

    8. Leave a Rating

    1. Environment Setup

    2. Option 1 Download Python, VSCode, and Jupyter Lab

    3. Option 2 Google Colab

    4. Set up OpenAI API

    5. Resources

    1. Introduction to using Generative AI for Data Generation and Augmentation

    2. Generating Synthetic Data with Generative AI

    3. Augmenting Existing Data with Generative AI

    4. Creating Time Series Data

    5. Generating Edge Cases in Data Engineering

    6. Handling PII Data with Generative AI

    7. Balancing Imbalanced Datasets in Data Engineering

    8. Data Augmentation App Walkthrough

    9. Creating Functions for Data Engineering

    10. Running the Backend

    11. Adding Front-End Components

    12. Running the Web App (GenAI for Data Engineering)

    1. Introduction to using Generative AI for Writing Data Engineering Code with Gen AI

    2. Data Cleaning and Modeling with Generative AI

    3. Documenting Code for Data Projects

    4. Creating Data Schemas, Systems, and Pipelines

    5. Transferring Data with Generative AI

    1. Introduction to using Generative AI for Gen AI Data Engineering Tools

    2. Use ChatGPT for Data Engineering

    3. Build a Data Engineering App with Claude

    4. Custom GPTs for Data Engineering

    5. Custom LLM or Generative AI tools for Data Engineering

    6. Copilot for Azure Data Factory and Gemini for BigQuery

    1. Introduction to using Generative AI for Data Parsing and Extraction

    2. Parsing Data (Data Engineering)

    3. Extracting Data from Web Scrapes and Images

    4. Performing Named Entity Recognition

    5. Extracting Data from Contracts

About this course

  • 54 lessons
  • 5.5 hours of video content

About the Instructor

Henry Habib

Productivity Educator

As a manager at one of the world's top management consulting firms, he advises F500 companies on growth strategy, operation, and analytics. He runs a publication called The Intelligent Worker, which teaches people how to be more productive at work with AI, automation, no-code, and other technologies. As a professor, Henry is passionate about teaching students on how to succeed, on work productivity topics (AI, automation, no-code, etc.). His courses have been purchased by over 200K students.

Testimonials

“Overall, everything is really well explained and the examples are a great way to follow along.”

Michelle Fitzpatrick

“Clear and concise so far.. detailed as well.”

Brian Willis

“Very nice precise course till now”

Saravana Kumar

“Clear steps and explanation”

Stella Birve

“Very well structured !”

Sameet Kumar

FAQ

  • What is this course all about?

    This course offers practical, hands-on training in using Generative AI for Data Engineering and as a Data Professional. You’ll learn to work with Python, the OpenAI API, and Jupyter Notebooks, focusing on real-world applications. Generative AI allows data professionals to complete tasks up to 16% faster and over 45% faster for those who regularly code. It also enables new capabilities, such as extracting insights from unstructured data. With over 5.5 hours of instructional video content, this course equips you with the skills to effectively integrate Generative AI into your daily workflows, enhancing your productivity and impact in the data engineering lifecycle.

  • Who is this course for?

    This course is designed for data engineers looking to incorporate Generative AI into their workflows, as well as all data professionals, including data analysts, data scientists, and data managers. It’s perfect for those who want to utilize Generative AI for various data tasks and enhance their skills in data engineering and AI integration. Developers aiming to build data engineering applications will also find valuable insights here. Additionally, anyone curious about leveraging AI to streamline data workflows will benefit from the practical knowledge and techniques covered in this course.

  • What are the requirements of this course?

    To get the most out of this course, you should have a basic familiarity with data engineering concepts, such as data cleaning and SQL queries. A fundamental understanding of programming, particularly in Python, is also essential, as we will be writing and executing code throughout the course. Additionally, familiarity with coding tools like Jupyter Notebooks and Visual Studio Code will help you navigate the practical exercises more effectively.

  • Is there a money back guarantee?

    Yes! Email us within 7 days of purchase and we will give you your money back, no questions asked.

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