Last Updated on by
The Concept Of Data Wrangling In Data Science-Towards Career In Data Science
Data Wrangling can b e interpreted as the process of cleaning, restructuring and enriching the raw data so that it can be easily analyzed & converted into actionable insights. Data Wrangling is a crucial step in the enterprises decision making process as it makes the data ready for analysis. The technique of Data Wrangling is very crucial for the enterprises that are having huge reserves of Big Data that has to be analyzed.
If you are intended to build your career in the field of Data Science, then developing skills in Data Wrangling would be very crucial. Master the skills in Data Wrangling along with other core technical aspects involving Data Science by being a part of our advanced Data Science Training In Hyderabad program.
Now, let’s look at the sequence of steps that are involved In the Data Wrangling process in Data Science.
Sequence Of Steps In Data Wrangling-
The very first step involves understanding the data in hand. This will present the Data Scientist to decide whether the data in hand can be analyzed to achieve the desired objective or not.
Data which is collected from different sources usually remains in an unstructured format. Structuring the data is very important as it helps in improving the quality of the output.
The data is then cleaned by treating it for any form of anomalies. Duplicate & missing values are eliminated and the formatting will be standardized to improve the data quality.
Additional data is also added to enhance the quality of it.
The consistency, quality and the security of the data are validated.
The prepared wrangled data is published so that it can be used further down the line
Know more in-depth about the other essential aspects of Data Science model by being a part of the Kelly Technologies Data Science Trainingprogram.