Unstructured data makes up about 80-90% of all the data created today. This kind of data needs to be changed so we can analyze it better. Here’s how we can do that:
Text Analysis: We can use tools like Natural Language Processing (NLP) to pull out important information from text. For example, sentiment analysis can help us understand if the text is positive, neutral, or negative.
Data Wrangling: This means cleaning up and changing unstructured data into a format that’s easier to work with. In fact, about 70% of a data scientist’s time is spent on this step!
Data Structuring: Here are a few ways we can organize the data:
The main goal is to reduce confusion and keep relevant information. This helps us summarize the data so it’s easier to analyze.
Unstructured data makes up about 80-90% of all the data created today. This kind of data needs to be changed so we can analyze it better. Here’s how we can do that:
Text Analysis: We can use tools like Natural Language Processing (NLP) to pull out important information from text. For example, sentiment analysis can help us understand if the text is positive, neutral, or negative.
Data Wrangling: This means cleaning up and changing unstructured data into a format that’s easier to work with. In fact, about 70% of a data scientist’s time is spent on this step!
Data Structuring: Here are a few ways we can organize the data:
The main goal is to reduce confusion and keep relevant information. This helps us summarize the data so it’s easier to analyze.