Build with AI: Data Pipelines with Cursor, Neon, and Streamlit

Course details

Learn how to build complete data projects using AI-powered coding tools in this hands-on course. Join instructor Vlad Gheorghe as he shows you how to create an end-to-end stock market data pipeline that fetches data from the Alpaca API, stores it in a remote Postgres database, and displays insights through a deployed Streamlit dashboard. Throughout the project, you'll explore ways to use Cursor IDE with AI assistance to write code faster and more efficiently. An ideal fit for software engineers, data engineers, and data analysts, this course equips you with a portfolio-ready project showcasing both technical skills and AI tool proficiency that you can share on LinkedIn, GitHub, and elsewhere.

Learning objectives
Plan a data pipeline project with an AI assistant.
Create and manage a Python codebase with GitHub and Cursor.
Code an end-to-end data pipeline with Cursor.
Manage a remote Postgres database with Neon and connect your models to it via MCP.
Deploy a data dashboard with Streamlit cloud.

Concepts

Introduction

Course overview - What you'll build and learn
Coding a pipeline with one prompt

Setting Up Your Project

Overview - What you will build
GitHub setup
Git version control - First commit for data pipeline projects
Cursor AI tutorial - Code data pipelines with AI-powered IDE
Python virtual environment setup - Data pipeline best practices

Building Your Project

OpenAlex quick start - Analyze data for Python data pipelines
Data extraction layer - Get data from OpenAlex
Neon database setup - Cloud PostgreSQL for data pipeline projects
Design table schema and create a table in the database
Process and load your data
Data quality testing
Consolidate pipeline logic
Build a Streamlit dashboard
Deploy the Streamlit dashboard

Build with AI: Data Pipelines with Cursor, Neon, and Streamlit

Authors

Vlad Gheorghe

Course details

Concepts

Introduction

Setting Up Your Project

Building Your Project

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