Understanding data types is really important in data science, and here's why:
Efficiency: Knowing the differences between structured, unstructured, and semi-structured data helps you pick the right tools to use. For example, structured data fits nicely in tables, which makes it easier to work with using SQL.
Manipulation: If you know common data structures, like arrays and graphs, you can handle data better. Arrays can help you do quick calculations, and graphs are great for looking at relationships between different pieces of data.
Analysis: Different types of data need different ways of looking at them. For instance, if you have text data (which is unstructured), you might need to use something called natural language processing. On the other hand, if you have numbers (structured data), you can use regular math techniques.
In short, understanding data types can make your projects go more smoothly and work better.
Understanding data types is really important in data science, and here's why:
Efficiency: Knowing the differences between structured, unstructured, and semi-structured data helps you pick the right tools to use. For example, structured data fits nicely in tables, which makes it easier to work with using SQL.
Manipulation: If you know common data structures, like arrays and graphs, you can handle data better. Arrays can help you do quick calculations, and graphs are great for looking at relationships between different pieces of data.
Analysis: Different types of data need different ways of looking at them. For instance, if you have text data (which is unstructured), you might need to use something called natural language processing. On the other hand, if you have numbers (structured data), you can use regular math techniques.
In short, understanding data types can make your projects go more smoothly and work better.