π Topic Summary
Data acquisition is the process of gathering, filtering, and cleaning data before it can be used for analysis or machine learning. It's a crucial first step in any data science project. Imagine you want to train an AI to recognize cats in photos. You'll need to *acquire* a large dataset of cat images, clean it (remove blurry pictures), and format it properly so the AI can learn from it. Data acquisition can involve various methods like web scraping, APIs, surveys, or even directly measuring data with sensors.
Think of it like this: before you can bake a cake, you need to gather all the ingredients! Data acquisition is the process of getting all the 'ingredients' for your AI 'cake'. Without good data, even the smartest AI algorithm will struggle to produce useful results.
π Part A: Vocabulary
Match the terms with their definitions:
| Term |
Definition |
| 1. API |
A. The process of collecting data from the internet using automated scripts. |
| 2. Web Scraping |
B. Data that is easily structured and fits neatly into rows and columns. |
| 3. Structured Data |
C. A set of rules and specifications that software programs can follow to communicate with each other. |
| 4. Sensor |
D. Raw, unorganized facts that need processing. |
| 5. Raw Data |
E. A device that detects and measures a physical property. |
π Part B: Fill in the Blanks
Fill in the blanks with the correct words from the following list: unstructured, cleaning, surveys, acquire, database.
To begin any data project, you must first ________ the necessary data. This can be done through methods like ________, web scraping or directly from a ________. Often, the data you get may be ________ and require a process of ________ before being ready for analysis.
π€ Part C: Critical Thinking
Imagine you are building an AI model to predict traffic patterns in your city. What are three different data sources you could use to acquire the data needed for this project? Explain why each data source would be valuable.