Stronger HTTP understanding - You will learn to inspect HTTP requests and responses, read status codes and headers, and understand how communication works, so you can troubleshoot issues and build cleaner integrations.
Efficient data extraction - You will learn how to locate elements in HTML and the DOM with CSS selectors and XPath, so you can pull specific data from detail pages, result lists, and paginated websites.
Python scrapers from scratch - You will practice downloading pages with requests and parsing them with BeautifulSoup, so you can build your own scripts that export structured data to CSV and JSON files.
Confident API usage - You will master sending API requests, handling JSON, working with API keys and tokens, and dealing with errors and rate limits, so you can connect external services with confidence.
Combining multiple data sources - You will learn how to collect information from different APIs and websites, then merge it into one consistent dataset that you can use for reporting, analysis, or further automation.
Automation for dynamic pages - You will use Selenium to click through pages, fill out forms, log in, and capture JavaScript-generated content when scraping static HTML alone is not enough for the task.
More reliable scripts - You will learn practical rules for rate limiting, robots.txt awareness, error handling, and reducing the risk of blocks, so your scraping scripts run more reliably and with fewer interruptions.
Business-ready use cases - You will turn the techniques into practical outcomes by preparing solutions for price monitoring, market data aggregation, automated reporting, and data integrations that support business decisions.
HTTP mechanics are the foundation for both scraping and API work in Python. The section explains the client-server model, request and response structure, methods such as GET or POST, headers and status codes, and using DevTools to inspect network traffic.
Before writing scraping scripts, it is necessary to understand when this technique is appropriate and how websites are structured. The section introduces HTML and DOM, locating elements with CSS selectors and XPath, and operational constraints such as robots.txt and rate limiting.
With requests and BeautifulSoup, data can be collected directly from HTML documents without browser automation. The section covers downloading pages, parsing document structure, extracting target content, handling pagination and multiple subpages, and saving results to CSV or JSON.
APIs define how applications exchange data, and this section explains that communication model in practice. It covers REST conventions, authorization with keys or tokens, request and response handling, JSON processing, and dealing with errors or request rate limits.
Using API in day-to-day work means connecting external services with internal processes. This part covers retrieving and transforming data, merging information from different sources, and building automations that rely on API calls instead of manual operations.
Selenium is used when data appears only after browser actions or JavaScript execution. Participants work with page elements, forms, sessions, and login flows, then automate browser tasks in both standard and headless mode to extract data from dynamic websites.
Reliable automation depends on more than working code. Here the focus is on improving script stability, organizing code for reuse, and managing execution time and performance, while also reducing the risk of blocks or bot detection during repeated data collection.
From a business perspective, scraping and API integrations support ongoing price tracking, competitor observation, and market data aggregation. The section also shows how collected data feeds automated reporting and how multiple systems or sources can be connected into a consistent information flow.
Basic Python skills - You should be comfortable running scripts and using variables, conditions, loops, and functions, so you can focus on networking, APIs, and data processing during the course.
Terminal and package basics - You should know how to use a terminal, install libraries with pip, and work in a simple coding environment, so you can prepare your exercises and follow the examples smoothly.
Basic HTML understanding - You should recognize common page elements such as headings, links, tables, and forms, so you can better understand document structure and identify the data to extract.
Analytical problem solving - You should be able to break a problem into steps and verify what your script returns, because scraping and API work often require you to investigate the source of errors.
