Data Selection

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The process of selecting the right type of data for analysis is crucial in data science. Here's how data scientists typically approach this selection:

• Define the Objective: Data scientists start by clearly defining the research or business objective to understand what they need to achieve. This helps identify the data most relevant to solving the problem or answering the question.

• Understand Data Types: Data scientists arm themselves with knowledge by familiarizing themselves with different data types—quantitative  (numeric) and qualitative (categorical). This includes understanding the various sub-types, such as continuous, discrete, ordinal, and nominal data  This understanding prepares them for the diverse data they may encounter in their analysis. 

• Evaluate Data Sources: Selecting the appropriate data source is vital. Data scientists evaluate whether to use existing internal data, need to collect new data, or can use public data sources. This step involves assessing data quality, availability, and relevance. 

• Consider Data Volume and Variety: They consider the volume and variety of data needed. This involves decisions about the granularity of data and the variety of data types necessary to conduct an effective analysis. 

• Data  Storage and Efficiency: Choices about data types also affect storage and query efficiency, particularly when designing databases for ongoing data science tasks. This involves selecting data types that optimize storage use and computational efficiency.

By following these steps, data scientists ensure they select the most appropriate data, which is crucial for effective analysis and achieving meaningful insights. This process provides a solid foundation for data analysis, giving them confidence in their understanding and the results they produce.