Messy Data and Anomalies in Data Science
In data science, messy data, and anomalies refer to issues and irregularities that can make the analysis of data difficult and can lead to inaccurate or biased results. Here's an explanation of both terms with examples:
Data that is not clean, organized, or arranged in a way that makes it easy to analyze is called "messy data." It can have many problems, such as missing values, record duplication, formatting that doesn't match, and more. Messy data can come from many places, such as mistakes in collecting the data, mistakes made by people, or problems with storing and retrieving the data. Cleaning and preparing messy data are important parts of the data analysis process that help make sure results are accurate and reliable.
Example of messy data: Let's say you're looking at a set of customer information and you notice that some records are missing values for the "Phone Number" field, while others have phone numbers that are formatted in different ways, such as with or without hyphens. Due to mistakes in entering the data, some records could also have multiple entries. For this messy data to be useful for research, it would need to be cleaned up and standardized.
Anomalies (Anomalous Data):
Anomalies (Anomalous Data): Anomalies, which are also called "outliers," are data points that are very different from the usual or expected patterns in a set of data. These differences can be caused by mistakes, noise, or events that happen very rarely. There are two kinds of odd things:
a. Point Anomalies: These are single pieces of data that are very different from the rest of the data. They can have high or low values compared to the rest of the material.
Example of a point anomaly: In a set of monthly sales numbers for a shop, if one month's sales are unusually high or low compared to the other months, this could be a point anomaly. This could be because of a special offer or a mistake in the books.
b. Contextual Anomalies: These are data points that stand out in a certain situation or group of the data, but may not stand out when looked at on their own. They are harder to figure out without looking at more details or features.
Example of a contextual anomaly: In a set of temperature readings, a temperature of 35°C might not be considered an anomaly in July, but it might be in December, based on where the readings were taken and what the weather is usually like there.
In data analysis, it's important to find and deal with anomalies because they can change statistical results, lead to wrong conclusions, or show useful insights or problems that need to be dealt with. Statistical and machine learning methods can be used to find and deal with strange things in datasets.