Podcast
Questions and Answers
What is a key feature that distinguishes structured data from unstructured data?
What is a key feature that distinguishes structured data from unstructured data?
Which method is typically used to collect statistical data from a group of individuals?
Which method is typically used to collect statistical data from a group of individuals?
What is data anonymization, and why is it important?
What is data anonymization, and why is it important?
What is the primary purpose of an API in data acquisition?
What is the primary purpose of an API in data acquisition?
Signup and view all the answers
Which of the following best describes web scraping?
Which of the following best describes web scraping?
Signup and view all the answers
What is the purpose of data acquisition in data science?
What is the purpose of data acquisition in data science?
Signup and view all the answers
Which of the following describes structured data?
Which of the following describes structured data?
Signup and view all the answers
What distinguishes public datasets from private datasets?
What distinguishes public datasets from private datasets?
Signup and view all the answers
Which of the following best defines an API?
Which of the following best defines an API?
Signup and view all the answers
What is a common challenge in web scraping?
What is a common challenge in web scraping?
Signup and view all the answers
Which of the following types of databases is characterized by storing data in a nonlinear format?
Which of the following types of databases is characterized by storing data in a nonlinear format?
Signup and view all the answers
What is a key ethical consideration in data collection?
What is a key ethical consideration in data collection?
Signup and view all the answers
What is a primary advantage of using JSON over XML?
What is a primary advantage of using JSON over XML?
Signup and view all the answers
Study Notes
Data Acquisition in Data Science
- Data acquisition is the process of collecting and measuring information from various sources to analyze and make informed decisions.
- Its importance lies in enabling data scientists to gather accurate, relevant data needed for analysis, informing predictions, and strategic decisions.
Data Acquisition Process
- The data acquisition process involves identifying data sources, collecting data, processing it, and storing it for analysis.
- Steps include data collection, cleaning, transforming, and integrating data for further analysis.
Data Types
- Structured Data: Organized in a fixed format or schema, such as databases (e.g., tables).
- Semi-Structured Data: Contains both structured and unstructured elements, e.g., JSON or XML files.
- Unstructured Data: Lacks a predefined format, e.g., text documents, images, and videos.
Data Sources
- Data sources can be internal (company databases) or external (public datasets, APIs).
- Examples include sensor data, web scraping, and surveys.
Public vs. Private Datasets
- Public datasets are generally free and accessible, allowing broad usage but potentially less reliability.
- Private datasets require permission for access, offering more accuracy but may come with limitations on usage.
APIs in Data Acquisition
- APIs (Application Programming Interfaces) facilitate data access, allowing applications to communicate and retrieve data from other services or platforms.
Web Scraping
- Web scraping is a technique to extract data from websites, using automated tools to collect information for analysis.
- Challenges include navigating site structures and handling legal implications of data usage.
Role of Databases
- Databases are vital for storing, organizing, and retrieving data.
- Types include SQL (relational databases) for structured data and NoSQL (non-relational databases) for unstructured data.
Data Collection Methods
- Surveys, experiments, and sensor data are primary methods for gathering data in research.
- Surveys provide direct feedback, experiments allow for controlled data collection, and sensors offer real-time data.
Key Data Formats
- CSV (Comma-Separated Values) files are popular for data storage due to their simplicity and human-readable format.
- JSON (JavaScript Object Notation) is favored over XML for data interchange due to its lightweight nature and ease of use.
Ethical & Legal Considerations
- Licensing and copyright are crucial to ensure proper use of datasets and avoid legal issues.
- Consent is necessary for ethical data collection, especially regarding sensitive information from human subjects.
Data Anonymization
- Data anonymization involves removing identifying information to protect individuals' privacy, a critical aspect of ethics in data science.
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Description
This quiz covers key concepts of data acquisition in data science, including its definition, importance, and the process involved. Additionally, it explores the differences between structured, semi-structured, and unstructured data, providing a comprehensive overview of the topic.
