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Data collection is fundamental in data science, involving methods for acquiring information from various sources, including files and online platforms. Techniques such as reading data from CSV, Excel, and APIs are crucial, along with web scraping and database interactions. Understanding these methods equips individuals to handle and analyze data effectively.
Chapter 411 sections
Data collection is fundamental in data science, involving methods for acquiring information from various sources, including files and online platforms. Techniques such as reading data from CSV, Excel, and APIs are crucial, along with web scraping and database interactions. Understanding these methods equips individuals to handle and analyze data effectively.
Data can be collected from offline files, APIs, websites, and databases.
Pandas simplifies reading data from CSV, Excel, and JSON formats.
APIs provide real-time, structured access to external data.
Web scraping helps extract content from webpages when APIs aren’t available.
Databases are essential for working with large or complex datasets.
Offline and online locations where data can be obtained, including files and APIs.
A Python library used for data manipulation and analysis, particularly for reading different types of data files.
Application Programming Interfaces that allow developers to access external data and services.
A technique used to extract information from web pages that are not provided through APIs.
Structured collections of data stored in a way that enables efficient retrieval and management, such as SQLite, MySQL, and MongoDB.
Total Questions
2
Estimated Time
4 min
Passing Score
70%
3 more questions available