Exploratory Data Analysis Quiz

The steps involved in solving problems with data in data science include collecting and understanding data, cleaning and formatting data, and using data to create a solution through data analysis and/or machine learning.

Internal data for data science comes from business-centric data in organizational databases, recording day-to-day operations, and scientific or experimental data.

Existing external sources of data for data science include public government databases, stock market data, Yelp reviews, and other data available for free or a fee.

When using online data for data science, caution should be exercised as not all accessible data is suitable for use.

The two methods of obtaining online data for data science are through APIs (e.g. Google Map API, Facebook API, Twitter API) and web scraping, which involves extracting data from what is displayed on a page or contained in the HTML file.

Internal sources of data for data science can be found in organizational databases recording day-to-day operations or scientific/experimental data.

Not all data accessible online is suitable for use in data science, so caution should be exercised to ensure the quality and reliability of the data.

Existing external sources of data for data science include public government databases, stock market data, Yelp reviews, and other pre-processed data available for free or for a fee.

Web scraping in the context of obtaining online data for data science involves using software, scripts, or manual extraction methods to retrieve data from web pages or HTML files.

Collecting your own data goes beyond the scope of the course and typically involves obtaining raw data from APIs (e.g., Google Map API, Facebook API, Twitter API) or through web scraping methods.