Generative AI for Data Engineering and Data Professionals

Note: this is as practical hands-on-keyboard course on how to use Generative AI in Data Engineering (and as a Data Professional). We will be using Python, OpenAI API, and Jupyter Notebooks to write and execute code.

Generative AI is changing the game in data and data engineering for two reasons:

• Do tasks faster - Data professionals who use Generative AI complete tasks 16% faster. This increases to more then 45% if you code / analyze data on a day-to-day basis

• Do new tasks - Generative AI enables data engineers and analysts to do so much more. In fact, some tasks like extracting features / insights from unstructured data or augmenting textual data is now only possible with Gen AI.

This is why GenAI is revolutionizing each step of the data engineering lifecycle. It doesn't matter if you're a data analyst, data scientist, data engineer, data professional, or data manager - you need to learn how to embed Generative AI in your day-to-day workflows.

That's what this course is all about - to make you more powerful and productive as a data professional with Generative AI.

Learn from more than 5.5 hours of relevant instructional video content, with the only course that will practically teach you the different ways that Generative AI is impacting the data engineering and data professional lifecycle, and then apply that to real-life end-to-end examples.

What is this course all about?

This course is all about how you can practically embed Gen AI into your day-to-day workflows as a Data Engineer or Data Professional. It's a deep practical guide on how Generative AI is revolutionizing each step of the data engineering lifecycle, making you more productive and powerful. This is a technical and practical course (it's not theoretical or hand-wavy).

Why learn Generative AI as a Data Professional or Data Engineer?

There are two reasons: productivity and power. Generative AI can do certain things faster - like writing SQL queries, documentation, creating schemas, and analyzing simple data. Generative AI can do things that were not possible before, like extracting insights from unstructured text, imputing textual data, or augmenting data while maintaining context. You must know how to use Gen AI to avoid being left behind.

How can Generative AI impact Data Engineering?

Gen AI impacts Data Engineering in many different ways. Specifically, we'll look at 7 different archetypes:

1. Data Generation and Augmentation

2. Writing Generative AI Code with Gen AI

3. Data Parsing and Extraction

4. Gen AI Data Engineering Tools

5. Data Querying and Analysis

6. Data Enrichment, Normalization, and Standardization

7. Anomaly Detection and Compression

What will you learn?

• Integrate Generative AI - Learn how to fully embed Generative AI as a Data Professional in your workflows (including data generation, analysis, storage, visualization, pipelines, and more)

• Be more productive - Generative AI is a productivity game changer - it can help you complete data tasks up to 20% faster (McKinsey), and even more if you write or use code

• Be more powerful - Learn how to do more data tasks that weren't possible without Generative AI, like extracting insights from unstructured text or augmenting textual data

Why choose this course?

• Complete guide - this is the 100% start to finish, zero to hero, basic to advanced guide on using Generative AI as a Data Engineer or Data Professional. There is no other course like it that teaches you everything from start to finish. It contains over 5.5 hours of instructional content!

• Structured to succeed - this course is structured to help you succeed. We first go through the fundamentals on how Generative AI can be used for Data Engineering. Then, we go through the 7 different archetypes of how Gen AI can be embedded into your workflows. We go through each, one-by-one, in full detail.

• Fully instructional - we not only go through important concepts, but also apply them. This is a practical hands-on-keyboard type course. This is not only a walkthrough of the all the features and theoretical concepts, but a course that actually uses real-life examples and integrates workflows with you.

• Step by step - we go through every single method of how Generative AI can impact Data Engineering step-by-step. We start with examples, then complete full end-to-end activities to apply what we've learned.

• Teacher response - if there's anything else you would like to learn, or if there's something you cannot figure out, I'm here for you! Look at the ways to reach out video.

Course overview

• Introduction to Generative AI for Data Engineering - Get an overview of the course, learn how Generative AI impacts Data Engineering tasks, and become familiar with the course roadmap.

• Environment Setup - Set up your workspace with two options: download Python, VSCode, and Jupyter Lab, or use Google Colab. We'll also guide you through setting up the OpenAI API.

• Data Generation and Augmentation - Generate and augment data with Generative AI. Learn to create synthetic data, handle PII, balance datasets, and more. We'll also build a data augmentation app, from backend to frontend.

• Writing Data Engineering Code with Generative AI - Discover how to use Generative AI for writing data engineering code. This section includes data cleaning, modeling, documenting code, creating data schemas, and transferring data.

• Gen AI Data Engineering Tools - Explore tools like ChatGPT, Claude, custom GPTs, and other Gen AI tools for data engineering.

• Data Parsing and Extraction - Parse and extract data from unstructured text using Generative AI, including data from web scrapes, images, contracts, invoices, receipts, and perform named entity recognition.

• Data Querying and Analysis - Master querying and analyzing data with Generative AI. Optimize your queries, develop and run query apps, and convert them to web apps with front-end components.

• Data Enrichment, Normalization, and Standardization - use Generative AI to enrich, normalize, and standardize your data, covering feature enrichment, data imputation, and standardizing textual data for better models.

• Conclusion - this covers the certificate, next steps, and ways to get in touch.

If you want to learn how to improve your productivity and be more powerful as a data engineer (in practice, not in theory) using Generative, then this is the course for you. We're looking forward to having you in the course and hope you earn the certificate.