Before you can analyze data, you first have to find it. Data doesn't magically appear in your analysis tools; it resides in various places, waiting to be collected. Think of this step like gathering ingredients before cooking. Depending on what you want to make (or what question you want to answer), you'll need different ingredients found in different locations. This section introduces common places where data lives.

Data Stored in Files

Perhaps the most straightforward way data is stored and shared is in individual files. You've likely encountered many of these already.

Common File Formats

CSV (Comma-Separated Values): This is a very common format for tabular data. Imagine a simple spreadsheet saved as plain text. Each line in the file represents a row of data, and values within that row are separated by commas. Because they are plain text, CSV files are widely compatible across different software.
Excel Spreadsheets (.xlsx, .xls): Microsoft Excel files are prevalent in business settings. They can store data in tables across multiple sheets and can also include formatting, formulas, and charts. While powerful, they are a proprietary format, though many data science tools can read them.
JSON (JavaScript Object Notation): JSON is a text format often used for transmitting data between web servers and web browsers, especially through APIs (which we'll discuss next). It uses human-readable text to transmit data objects consisting of attribute-value pairs and array data types. It's less tabular than CSV but flexible for representing more complex, nested structures.

These files might be downloaded from websites, received as email attachments, generated by other software, or stored on shared network drives. For many introductory data science tasks, you'll start by loading data from one of these file types.

Data from Databases

Organizations often store large amounts of operational data in databases. A database is an organized collection of data, generally stored and accessed electronically from a computer system.

Think of a database as a highly structured digital filing cabinet. Instead of simple files, it uses specialized software (a Database Management System or DBMS) to store, manage, and retrieve data efficiently.

Relational Databases: These are a common type you'll hear about. They store data in tables with predefined columns (attributes) and rows (records), similar to spreadsheets but much more powerful for managing relationships between different tables (e.g., connecting a Customers table to an Orders table). Interacting with these databases typically involves using a language called SQL (Structured Query Language). While you don't need to be an SQL expert right away, it's good to know that databases are a primary source of data in many real-world scenarios.

Accessing data from a database usually requires specific connection details (like an address, username, password) and often involves writing queries to request the exact pieces of data you need.

Data via APIs

APIs, or Application Programming Interfaces, are sets of rules and protocols that allow different software applications to communicate and exchange data with each other.

Imagine you want the current weather for London. Instead of trying to read the complex systems of a weather service directly, the service might provide an API. Your program can send a request to a specific API web address (an endpoint), perhaps specifying "London," and the API will send back the current weather data, often formatted in JSON.

Many web services offer APIs to access their data:

Social media platforms (e.g., Twitter/X)
Financial data providers (e.g., stock prices)
Government agencies (e.g., census data, transit schedules)
Mapping services (e.g., Google Maps)

Using APIs often requires understanding how to make specific web requests and sometimes involves obtaining access keys or tokens for authentication. They are a powerful way to get dynamic, up-to-date data directly from a source.

Data from Web Pages (Web Scraping)

Sometimes, the data you want is visible on a website but isn't available through a convenient file download or API. In these cases, it's sometimes possible to write code that automatically extracts information directly from the web page's HTML structure. This process is called web scraping or web harvesting.

While useful, web scraping should be approached with caution:

Legality and Ethics: Always check a website's terms of service (often found in a robots.txt file or a "Terms of Use" page) before scraping. Excessive scraping can overload a website's server, and scraping private or copyrighted data may be illegal.
Fragility: Websites change their layout frequently. A scraper built for a specific structure might break if the website is updated.

For beginners, it's usually more practical to start with data from files, databases, or well-defined APIs.

Other Potential Sources

Data can also come from less common, or more specialized sources:

Sensors: Internet of Things (IoT) devices, scientific instruments, and environmental monitors generate streams of data.
Surveys: Data collected directly from individuals through questionnaires.
Open Data Portals: Many governments, research institutions, and organizations maintain public portals where they share datasets freely (e.g., data.gov in the US, Kaggle Datasets).

Knowing where data might come from is the first step. The next challenge, which we'll cover soon, is how to actually bring that data into your analysis environment – a process often called importing or loading data. The source you choose will depend heavily on the problem you're trying to solve and the data available to you.