Work Faster with Practical Gen AI for Data Engineering | For all Data Professionals (Engineers, Analysts, Scientists)
What you'll learn
Integrate Generative AI into existing data flows and data engineering lifecycle
Learn how Gen AI is revolutionizing each step of the data engineering lifecycle, with hands-on practice and activities
Understand the role of Generative AI in Data Engineering, especially on when to use it and when not to use it
Generate and augment data using Generative AI
Write data engineering code with Generative AI
Explore Generative AI tools for data engineering
Parse and extract insights and data from unstructured text using Generative AI
Query and analyze data in data engineering with Generative AI
Enrich, normalize, and standardize data using Generative AI features

Requirements
Familiarity with basic data engineering concepts (data cleaning, SQL queries)
Basic understanding of programming, especially Python
Familiarity with coding tools like Jupyter notebooks and VS Code

Description
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 basisDo 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:Data Generation and AugmentationWriting Generative AI Code with Gen AIData Parsing and ExtractionGen AI Data Engineering ToolsData Querying and AnalysisData Enrichment, Normalization, and StandardizationAnomaly Detection and CompressionWhat 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 codeBe 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 dataWhy 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 overviewIntroduction 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.

Overview
Section 1: Introduction
Lecture 1 About the course
Lecture 2 Course Tips
Lecture 3 How Generative AI impacts Data Engineer Tasks
Lecture 4 Course Roadmap
Lecture 5 Caveats about Using Generative AI
Lecture 6 About the Instructor
Lecture 7 Keys to Success
Lecture 8 Ways to Contact
Lecture 9 Leave a Rating
Section 2: Environment Setup
Lecture 10 Environment Setup
Lecture 11 Option 1 Download Python, VSCode, and Jupyter Lab
Lecture 12 Option 2 Google Colab
Lecture 13 Set up OpenAI API
Lecture 14 Resources
Section 3: Data Generation and Augmentation
Lecture 15 Introduction to using Generative AI for Data Generation and Augmentation
Lecture 16 Generating Synthetic Data with Generative AI
Lecture 17 Augmenting Existing Data with Generative AI
Lecture 18 Creating Time Series Data
Lecture 19 Generating Edge Cases in Data Engineering
Lecture 20 Handling PII Data with Generative AI
Lecture 21 Balancing Imbalanced Datasets in Data Engineering
Lecture 22 Activity: Data Augmentation App Walkthrough
Lecture 23 Activity: Creating Functions for Data Engineering
Lecture 24 Activity: Running the Backend
Lecture 25 Activity: Adding Front-End Components
Lecture 26 Activity: Running the Web App (GenAI for Data Engineering)
Section 4: Writing Data Engineering Code with Gen AI
Lecture 27 Introduction to using Generative AI for Writing Data Engineering Code with Gen A
Lecture 28 Data Cleaning and Modeling with Generative AI
Lecture 29 Documenting Code for Data Projects
Lecture 30 Creating Data Schemas, Systems, and Pipelines
Lecture 31 Transferring Data with Generative AI
Section 5: Gen AI Data Engineering Tools
Lecture 32 Introduction to using Generative AI for Gen AI Data Engineering Tools
Lecture 33 Use ChatGPT for Data Engineering
Lecture 34 Build a Data Engineering App with Claude
Lecture 35 Custom GPTs for Data Engineering
Lecture 36 Custom LLM or Generative AI tools for Data Engineering
Lecture 37 Copilot for Azure Data Factory and Gemini for BigQuery
Section 6: Data Parsing and Extraction
Lecture 38 Introduction to using Generative AI for Data Parsing and Extraction
Lecture 39 Parsing Data (Data Engineering)
Lecture 40 Extracting Data from Web Scrapes and Images
Lecture 41 Performing Named Entity Recognition
Lecture 42 Activity: Extracting Data from Contracts
Section 7: Data Querying and Analysis
Lecture 43 Introduction to using Generative AI for Data Querying and Analysis
Lecture 44 Querying Data with Generative AI
Lecture 45 Optimizing Data Queries
Lecture 46 Activity: Developing Data Engineering Query Apps
Lecture 47 Activity: Running Data Engineering Query Apps
Lecture 48 Activity: Converting to a Web App with Front-End
Section 8: Data Enrichment, Data Normalization and Standardization
Lecture 49 Introduction to using Generative AI for Data Enrichment, Data Normalization and
Lecture 50 Enriching Features for Data Models
Lecture 51 Data Imputation and Normalization with Generative AI
Lecture 52 Imputation for Time Series Data Engineering
Lecture 53 Standardizing and Normalizing Textual Data with Generative AI
Section 9: Conclusion
Lecture 54 Conclusion and Certificate
Lecture 55 Ways to contact
Section 10: Bonus
Lecture 56 Bonus
Data engineers who want to incorporate Generative AI into their workflows,All data professionals (data engineers, data analysts, data scientists, data managers),Data professionals or engineers who want to use Generative AI for data tasks,Anyone wanting to enhance their skills in data engineering and Gen AI integration,Developers aiming to build data engineering applications,Individuals curious about leveraging AI to streamline data workflows

Homepage