Generative AI for Data Professionals

About Course

This hands-on course is designed for data analysts, data scientists, data engineers, and anyone working with data who wants to harness the power of Generative AI to supercharge their workflows.

Generative AI is changing the game in data 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 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.

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?

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.

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, 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

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: using 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.
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.

1. Learn how to send requests to GenAI APIs and structure responses using JSON.
2. Generate synthetic and time-series datasets using LLMs.
3. Augment, balance, and anonymize data for real-world use cases
4. Parse unstructured data from text, images, and web sources
5. Extract entities and insights using AI-powered natural language techniques
6. Perform and optimize SQL queries with the help of Generative AI
7. Automate documentation, debugging, and repetitive tasks
8. Integrate GenAI seamlessly into data pipelines and tools

Course Content

GenAI for Data Professional: Introduction and Setup
Kickstart your journey by understanding what Generative AI is, why it matters for data professionals, and how to set up your coding environment with API access.

Introduction and Setup

10:07

GenAI for Data Professionals: Send Requests to an API
Learn how to interact with large language models by making API calls, formatting prompts, and retrieving responses programmatically.

GenAI for Data Professionals: Structure Data Using JSON
Master the art of shaping LLM responses into structured JSON — a key step for integrating AI outputs into data pipelines and tools.

Data Generation and Augmentation Module: Synthetic Data Generation
Learn how to generate realistic, high-quality datasets using GenAI — ideal for testing, simulations, or when real data is unavailable.

Data Generation and Augmentation Module: Augmenting Existing Data
Use LLMs to intelligently enrich, expand, or transform your current datasets without manual effort.

Data Generation and Augmentation Module: Time Series Data and Edge Case Simulation
Generate temporal datasets that include realistic trends, seasonality, and anomalies using AI-driven prompts. Create hard-to-find but critical test scenarios and corner cases to make your data models more robust.

Data Generation and Augmentation Module: Handling PII and Imbalanced Datasets
Learn safe techniques for replacing sensitive information with privacy-preserving synthetic data that maintains data utility. Automatically generate underrepresented classes to balance skewed datasets and improve model performance.

Data Parsing and Extraction Module: Introduction to Parsing
Understand how to convert unstructured text, logs, or documents into structured formats usable in data workflows.

Data Parsing and Extraction Module: Web Scrapes and Images
Use GenAI to extract relevant data from HTML pages, screenshots, and visual content with minimal coding.

Data Parsing and Extraction Module: Named Entity Recognition (NER)
Learn how to use AI to extract names, dates, locations, and other key entities from raw text, boosting your data enrichment pipeline.

Data Querying, Analysis, and Optimization Module: AI-Powered Analysis
Leverage LLMs to perform quick data analysis, draw insights, and summarize large datasets in plain language.

Data Querying, Analysis, and Optimization Module: Query Optimization
Use GenAI to refactor slow or inefficient SQL queries for better readability, performance, and scalability.

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Generative AI for Data Professionals

About Course

What Will You Learn?

Course Content

GenAI for Data Professional: Introduction and Setup
Kickstart your journey by understanding what Generative AI is, why it matters for data professionals, and how to set up your coding environment with API access.

Introduction and Setup

GenAI for Data Professionals: Send Requests to an API
Learn how to interact with large language models by making API calls, formatting prompts, and retrieving responses programmatically.

Send your first API request

GenAI for Data Professionals: Structure Data Using JSON
Master the art of shaping LLM responses into structured JSON — a key step for integrating AI outputs into data pipelines and tools.

Structure data using JSON

Data Generation and Augmentation Module: Synthetic Data Generation
Learn how to generate realistic, high-quality datasets using GenAI — ideal for testing, simulations, or when real data is unavailable.

Synthetic Data Generation

Data Generation and Augmentation Module: Augmenting Existing Data
Use LLMs to intelligently enrich, expand, or transform your current datasets without manual effort.

Augmenting Existing Data

Data Generation and Augmentation Module: Time Series Data and Edge Case Simulation
Generate temporal datasets that include realistic trends, seasonality, and anomalies using AI-driven prompts. Create hard-to-find but critical test scenarios and corner cases to make your data models more robust.

Time Series Data and Edge Case Simulation

Handling PII and Imbalanced Datasets

Data Parsing and Extraction Module: Introduction to Parsing
Understand how to convert unstructured text, logs, or documents into structured formats usable in data workflows.

Introduction to Data Parsing with GenAI

Data Parsing and Extraction Module: Web Scrapes and Images
Use GenAI to extract relevant data from HTML pages, screenshots, and visual content with minimal coding.

Web Scrapes and Images

Data Parsing and Extraction Module: Named Entity Recognition (NER)
Learn how to use AI to extract names, dates, locations, and other key entities from raw text, boosting your data enrichment pipeline.

Named Entity Recognition (NER)

Data Querying, Analysis, and Optimization Module: AI-Powered Analysis
Leverage LLMs to perform quick data analysis, draw insights, and summarize large datasets in plain language.

Data Querying and Analysis

Data Querying, Analysis, and Optimization Module: Query Optimization
Use GenAI to refactor slow or inefficient SQL queries for better readability, performance, and scalability.

Query Optimization

Quick links

About Course

What Will You Learn?

Course Content

GenAI for Data Professional: Introduction and Setup Kickstart your journey by understanding what Generative AI is, why it matters for data professionals, and how to set up your coding environment with API access.

Introduction and Setup

GenAI for Data Professionals: Send Requests to an API Learn how to interact with large language models by making API calls, formatting prompts, and retrieving responses programmatically.

Send your first API request

GenAI for Data Professionals: Structure Data Using JSON Master the art of shaping LLM responses into structured JSON — a key step for integrating AI outputs into data pipelines and tools.

Structure data using JSON

Data Generation and Augmentation Module: Synthetic Data Generation Learn how to generate realistic, high-quality datasets using GenAI — ideal for testing, simulations, or when real data is unavailable.

Synthetic Data Generation

Data Generation and Augmentation Module: Augmenting Existing Data Use LLMs to intelligently enrich, expand, or transform your current datasets without manual effort.

Augmenting Existing Data

Data Generation and Augmentation Module: Time Series Data and Edge Case Simulation Generate temporal datasets that include realistic trends, seasonality, and anomalies using AI-driven prompts. Create hard-to-find but critical test scenarios and corner cases to make your data models more robust.

Time Series Data and Edge Case Simulation

Handling PII and Imbalanced Datasets

Data Parsing and Extraction Module: Introduction to Parsing Understand how to convert unstructured text, logs, or documents into structured formats usable in data workflows.

Introduction to Data Parsing with GenAI

Data Parsing and Extraction Module: Web Scrapes and Images Use GenAI to extract relevant data from HTML pages, screenshots, and visual content with minimal coding.

Web Scrapes and Images

Data Parsing and Extraction Module: Named Entity Recognition (NER) Learn how to use AI to extract names, dates, locations, and other key entities from raw text, boosting your data enrichment pipeline.

Named Entity Recognition (NER)

Data Querying, Analysis, and Optimization Module: AI-Powered Analysis Leverage LLMs to perform quick data analysis, draw insights, and summarize large datasets in plain language.

Data Querying and Analysis

Data Querying, Analysis, and Optimization Module: Query Optimization Use GenAI to refactor slow or inefficient SQL queries for better readability, performance, and scalability.

Query Optimization

Student Ratings & Reviews

Quick links

GenAI for Data Professional: Introduction and Setup
Kickstart your journey by understanding what Generative AI is, why it matters for data professionals, and how to set up your coding environment with API access.

GenAI for Data Professionals: Send Requests to an API
Learn how to interact with large language models by making API calls, formatting prompts, and retrieving responses programmatically.

GenAI for Data Professionals: Structure Data Using JSON
Master the art of shaping LLM responses into structured JSON — a key step for integrating AI outputs into data pipelines and tools.

Data Generation and Augmentation Module: Synthetic Data Generation
Learn how to generate realistic, high-quality datasets using GenAI — ideal for testing, simulations, or when real data is unavailable.

Data Generation and Augmentation Module: Augmenting Existing Data
Use LLMs to intelligently enrich, expand, or transform your current datasets without manual effort.

Data Generation and Augmentation Module: Time Series Data and Edge Case Simulation
Generate temporal datasets that include realistic trends, seasonality, and anomalies using AI-driven prompts. Create hard-to-find but critical test scenarios and corner cases to make your data models more robust.

Data Parsing and Extraction Module: Introduction to Parsing
Understand how to convert unstructured text, logs, or documents into structured formats usable in data workflows.

Data Parsing and Extraction Module: Web Scrapes and Images
Use GenAI to extract relevant data from HTML pages, screenshots, and visual content with minimal coding.

Data Parsing and Extraction Module: Named Entity Recognition (NER)
Learn how to use AI to extract names, dates, locations, and other key entities from raw text, boosting your data enrichment pipeline.

Data Querying, Analysis, and Optimization Module: AI-Powered Analysis
Leverage LLMs to perform quick data analysis, draw insights, and summarize large datasets in plain language.

Data Querying, Analysis, and Optimization Module: Query Optimization
Use GenAI to refactor slow or inefficient SQL queries for better readability, performance, and scalability.