Data is raw information that can exist in any form, usable or not. We can easily get data everywhere in our lives; for example, the price of gold on the day of writing was $ 1.158 per ounce. This does not have any meaning, except describing the price of gold. This also shows that data is useful based on context.
With the relational data connection, information appears and allows us to expand our knowledge beyond the range of our senses. When we possess gold price data gathered over time, one piece of information we might have is that the price has continuously risen from $1.152 to $1.158 over three days. This could be used by someone who tracks gold prices.
Knowledge helps people to create value in their lives and work. This value is based on information that is organized, synthesized, or summarized to enhance comprehension, awareness, or understanding. It represents a state or potential for action and decisions. When the price of gold continuously increases for three days, it will likely decrease on the next day; this is useful knowledge.
The following figure illustrates the steps from data to knowledge; we call this process, the data analysis process and we will introduce it in the next section:
In this chapter, we will cover the following topics:
Data analysis and process
An overview of libraries in data analysis using different programming languages
Common Python data analysis libraries