Normalization is the process of transforming data into a more convenient form. The purpose of normalization is to make sure that data is consistent and accurate and to prevent duplicate data. When data is normalized, it is easier to find and correct errors, and to ensure that data is consistent across different systems.
Normalization usually involves breaking down data into smaller pieces and then recombining them in a way that makes sense. For example, when data is stored in a database, it is often stored in multiple tables. Normalization involves breaking down the data into smaller pieces and then storing those pieces in separate tables. This ensures that data is consistent across different systems and that duplicate data is prevented.
Normalization is a process that is often used in data cleansing, and it is a necessary step in many data analysis tasks. Normalization is also sometimes called data normalization or denormalization.
In the context of text analytics, Normalization usually refers to one of two things:
- The process of putting text into a more consistent form, so that it can be more easily analyzed. This may involve things like converting all dates into a single format, or standardizing the spelling of words. like for example, converting “colour” to “color”.
- The process of reducing a set of text data down to its most essential form, so that it can be more easily analyzed. This may involve things like removing stopwords (common words that don’t add much meaning, like “a”, “the”, or “of”), or lemmatizing (converting words to their base form, like converting “cats” to “cat, (converting words to their base form, like converting”running” to “run”).
What are the benefits of normalization?
There are many benefits to normalizing data, including:
– Ensuring data is consistent across different systems
– Preventing duplicate data
– Making it easier to find and correct errors
– making sure that data is accurate
What are the different types of normalization?
There are several different types of normalization, including:
- Database Normalization
- Content Normalization
- URL Normalization
- Text Normalization
Normalization vs. Other Terms
Normalization is often confused with other terms, such as Data Cleaning, Data Wrangling, and Text Pre-Processing. However, there are some important differences between these terms:
- Data Cleaning refers to the process of identifying and cleaning up inaccuracies and inconsistencies in data. This may involve things like correcting typos, or filling in missing values.
- Data Wrangling is a broader term that refers to the process of manipulating data so that it can be more easily analyzed. This may involve things like re-arranging columns or filtering out unwanted data.
- Text Pre-Processing is a specific type of data wrangling that refers to the process of preparing text for text analysis. This may involve things like tokenization or removing stopwords.