Data collection

Data science involves a process of gathering information or data points from various sources to be used for analysis, modeling, or decision-making purposes. It’s a fundamental step in the data science lifecycle where relevant data is collected and organized to extract meaningful insights or to train machine learning models.

Data collection is about:

• Identifying Data Sources: The first step includes determining where the data can be extracted from and this could be databases, files, APIs, sensors, web scraping, or any other sources.

• Acquiring Data: In order to access and retrieve the data from these sources, these might involve using programming languages like Python or R, SQL queries, or other tools to extract the necessary data.

Groundwork of Data Science:

The process of arriving at a meaningful understanding of the analyzed data set involves the cleaning and Pre-processing component. Once data is collected, it often needs to undergo various processes of analyzing, checking for consistency, removing irrelevant data, and finalizing by transforming existing ones to check for its validity and reliability. This specifically involves handling missing values, removing duplicates, standardizing formats, and transforming the data into a suitable format for analysis or modeling.