Unusual Data Sources You Can Scrape and Analyze for Practice

The majority of people learned scraping from the same three sites and subsequently lost interest. The fix might be very simple: pick data sources that have movement – prices change, events come and go, apps ship updates, services break and recover.

When the data shifts, your analysis suddenly matters, even if it’s “just practice.” In this guide, you’ll get six unusual sources for data you can collect with basic scraping skills. We will make it very easy for the beginners: what to collect, what to monitor over a period of time, and what questions to ask after you get your project data.

What Makes a “Good” Dataset for Practice and Why Cool Data Wins

A “good” practice dataset is not the size but the one that develops real habits: cleaning messy data, taking care of missing fields, and monitoring variations over time. The top data sources also provide you rapid feedback – thus, you would be aware whether your script has worked well and your analysis has been reasonable.

Here’s what to look for when choosing types of data sources:

• Clear structure (at least partially): repeated patterns like cards, rows, or item pages.
• Stable identifiers like product IDs, job post URLs, app versions. Anything that can eliminate duplicates during your web scraping process tomorrow – are helpful.
• Time value: the content changes, so you can build a history. It's fine to take one snapshot, but you'll learn the most from a changing dataset.
• Correct question. "Did prices go up this month?" is much better than "I have a dataset containing 10,000 rows." The correct question gives clarity to your research.
• Ethical access like public pages, low request rates, and respect for rules. Practice should not stress someone else’s site.

Beginners also worry about “real value.” People ask, “what are some data sources that a business can use”– and the honest answer is: many of the same public data sources you can practice on, as long as you collect them responsibly and don’t grab personal details you don’t need. A tiny, well-made dataset can teach you more than a massive messy one.

6 Unusual Sources to Scrape Data

Below are data source examples you can treat like mini-labs. These sources aren’t exotic – just overlooked. Each one is good for practice because you can build a dataset, refresh it, and learn from the changes:

These are great sources for data because they’re usually public, readable, and updated often. They also show different types and sources of data: some fields are neat and structured (dates, versions), others are messy human text (descriptions, updates).

Price Changes in Online Catalogs

Online catalogs are common data sources, but “price history” is where they become unusual in a good way for an e-commerce scraper. The process involves scraping the same set of item pages on a regular basis, instead of just one-time scraping, and storing the snapshots.

What to capture from these:

• Item name and brand;
• Current price and currency;
• Discount labels (“sale,” “bundle,” “limited offer”);
• Availability (“in stock,” “preorder,” “out of stock”);
• Category, tags, and maybe shipping info if it’s visible.

A very easy method to begin with: choose 50-200 items and monitor them. This would provide you with plenty of project data to understand joins, to do group-by summaries, and to create charts. Moreover, you will be able to practice the "change detection": has the price changed since the last run? Did the stock status flip?

Try to analyze:

• The average price for each category over time;
• The items that are discounted most often and by how much;
• The "out of stock" patterns (do weekends have an impact?);
• Price "stickiness" (some items barely move, while others bounce back and forth).

Job Ads as a Skills and Salary Dataset

Job boards and company career pages are underrated data sources because each job post is basically a semi-structured document: role, skills, seniority, location, and sometimes salary. Even when salary is missing, skills and keywords are rich.

What to scrape from these sources:

• Job title and company;
• Location (or “remote/hybrid”);
• Required skills and “nice to have” skills;
• Salary range (if present);
• Posting date (or “days ago”);
• Job type (full-time/contract).

Here's how to turn it into a dataset that you can use:

• Make skill names normal (e.g., "PostgreSQL" vs. "Postgres").
• Take the numbers from the salary text and put them in min/max columns.
• Store the URL as the unique ID so you can track edits or removals.
• Keep the raw text, too. It's helpful when your parsing fails.

App Release Notes as a Product-Change Dataset

Release notes are wholesome data sources for analysis as they come pre-sorted by version and date.

What to collect:

• App name;
• Version number;
• Release date;
• Bullet items (features, improvements, fixes);
• Keywords you tag yourself (security, performance, UI, payments, stability).

Here are some simple ideas for analysis:

• Count how often categories appear, like "fix," "improve," and "new."
• Track how often releases happen.
• Identify the topics that are mentioned more than the others, such as "crashes," "sync," and "login."
• Compare "new features" and "bug fixes" over time.

Local Events as a Demand and Seasonality Dataset

Local event listings are practical data sources for learning seasonality. Events have time, place, categories, and often pricing. You can scrape one city or a handful of venues and still get an interesting dataset quickly.

Capture from these sources:

• Event title;
• Date and time;
• Category (music, tech, kids, sports, etc.);
• Venue and neighborhood;
• Ticket price or “free”;
• Organizer (optional).

What you can do with the dataset:

• Counts per day of week (are Fridays heavier?);
• “Free vs paid” by category;
• Peaks across months (festival season, holidays);
• A simple “what’s coming this weekend?” feed you generate from your own data.

Menus as a Pricing and Dietary Dataset

Restaurant menus seem simple to understand, but comparing them is challenging. There are playful names for dishes, different types of categories and sometimes the prices can be hidden in PDFs or images. However, if you get the HTML menus, they turn out to be wonderful practice data sources.

What to scrape:

• Restaurant name and location;
• Dish name;
• Price;
• Category (starter, main, dessert, drinks);
• Dietary tags (vegan, gluten-free, nuts) if listed.

Beginner analysis ideas:

• Median price by cuisine;
• Dietary options by neighborhood;
• Price changes by season;
• Common ingredients (if descriptions are available)

Status Pages as an Uptime and Incident Dataset

Status pages are different from other data sources since they openly monitor and report failures. Typically, they provide the following details: incidents, affected components, start/end times, and short updates. Scraping them teaches you how to work with event logs (not just lists).

Collect from these sources:

• Incident title;
• Start time, end time (or “ongoing”);
• Duration (you can compute this);
• Affected components (API, web app, payments, etc.);
• Severity level (if shown);
• Updates (timestamps + messages).

What to analyze:

• Which components fail most often?;
• Average incident duration over months;
• Time-of-day patterns (maintenance vs surprise outages);
• “Noisy weeks” vs calm weeks.

Conclusion

Static data sources usually make the scraping practice feel monotonous. If you target data that constantly change, such as prices, jobs, releases, events, menus, and incidents, your work will naturally be interesting. You will get to run the scrapes repeatedly, have better logs and a cleaner dataset with history (instead of just a pile of rows).

Keep a short checklist: show respect to the sites, familiarize yourself with their rules, restrict your requests, and do not collect personal data. Also, keep proxies in mind. Reliable proxy servers act as a safety net against IP blocks, keeping your data flow stable – especially as your projects scale up.

Are you up for learning about the tech side of scraping? Here are some guides worth reading:

• Techniques for Scraping Websites with Dynamic Content;
• AI Web Scraping with ChatGPT;
• Amazon Scraping with Froxy.

Pick one of the sources for data above, define your fields, and start small. After a few weeks, you’ll (hopefully) be doing real fun with analytics – because you’ll be answering questions that move with the world, not frozen snapshots.