FreeCourse

Learn to Build a Python Real Estate Data Pipeline

Master real-world Python skills by building a complete automated data pipeline. In this hands-on course, you'll scrape real estate listings, clean and analyze data, build an interactive dashboard, and implement automation to track market trends.

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This course includes

  • AI assistance for coding help
  • Hands-on projects
  • Quizzes to test your knowledge
  • A certificate of completion

Skill level

Intermediate

Time to complete

12 hours

Projects

4

Prerequisites

Basic Python

About this course

This course teaches real-world data scraping, analysis, and automation. You will gain experience working with Selenium, Pandas, and Streamlit to build an interactive and automated real estate data pipeline.

Skills you'll gain

  • Web Scraping with Selenium
  • Data Cleaning with Pandas
  • Data Visualization with Matplotlib & Seaborn
  • Building Interactive Dashboards with Streamlit
  • Automating Data Pipelines with APScheduler

Syllabus

Learn how to extract real estate listings using Selenium. You'll scrape property details, manage JavaScript-heavy pages, and store structured data for analysis.

  • Introduction to Web Scraping
  • Setting up Selenium for Web Scraping
  • Extracting Property Listings
  • Handling JavaScript-heavy Websites
  • Storing Scraped Data
  • Quiz - Web Scraping Fundamentals

Transform raw scraped data into clean, structured insights. You'll handle missing values, standardize data formats, and visualize real estate trends.

  • Loading and Inspecting Scraped Data
  • Handling Missing Values and Inconsistencies
  • Standardizing Data Formats
  • Analyzing Price Trends and Property Distributions
  • Visualizing Data with Matplotlib & Seaborn
  • Quiz - Data Cleaning and Analysis

Create an interactive dashboard using Streamlit to explore real estate data. Implement dynamic filters, charts, and an interactive map to visualize market trends.

  • Setting Up Streamlit
  • Building Data Visualizations
  • Filtering and Interacting with Data
  • Integrating Interactive Maps
  • Preparing the Dashboard for Deployment
  • Quiz - Interactive Dashboard Development

Automate the real estate data pipeline with APScheduler. Implement historical tracking to analyze long-term price trends and listing activity over time.

  • Introduction to Automation with APScheduler
  • Setting Up Scheduled Scraping
  • Storing Timestamped Data
  • Processing and Visualizing Long-Term Price Trends
  • Enhancing the Dashboard with Historical Tracking
  • Quiz - Automation and Historical Data Tracking
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Projects in this course

Practice Project

Real Estate Data Scraper

Develop a Selenium-based scraper for fetching real estate listings.

Practice Project

Data Cleaning & Analysis

Clean and analyze the scraped real estate data to extract useful insights.

Practice Project

Interactive Dashboard

Build a real estate data dashboard using Streamlit and visualization tools.

Practice Project

Automated Data Pipeline

Automate the entire pipeline with scheduled scraping, historical tracking, and real-time updates.

Meet the creator of the course

Meet the full team →
Dr. Robert Johns
Dr. Robert Johns

Curriculum Director

Dr. Robert Johns is a seasoned data scientist and educator with years of experience in web scraping, data automation, and AI-driven analytics. He has developed multiple online courses focused on Python programming, automation, and data pipelines.

Frequently Asked Questions

Basic knowledge is helpful but not required.

You'll work with Selenium, Pandas, Matplotlib, Seaborn, Streamlit, and APScheduler.

Yes, you will receive a certificate upon completion.

Web scraping is the process of extracting data from websites using automated scripts or bots.

Automation saves time by performing repetitive tasks like data collection, cleaning, and visualization without manual effort.

This course focuses on real-world automation and data pipelines, rather than just basic Python syntax.

Yes! The skills you learn here can be applied to tracking property prices, analyzing market trends, and making data-driven decisions.

It depends on the website's terms of service. Always follow ethical scraping practices and comply with legal regulations.

We cover techniques like using rotating proxies, headless browsers, and request throttling to help you avoid getting blocked.

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