Why is data wrangling needed?
Raw data has to be made useful at the end of the day. Without a clean and an organised dataset we must not expect good results. Many data professionals spend maximum of their time wrangling the data and devote minimal time for modelling. Is it worth it?
So let’s say we want gold that can be used to make ornaments for customers. We all know that mining gold is a very time consuming process. The purest form of gold is not obtained by just mining the gold ores. Rather it has to undergo a large process where it’s cleaned and refined along with needful steps required so that it can be converted into ornaments and we can get them to wear.
Similarly, Data wrangling also follows the same analogy and has evolved as an unavoidable part of data processing. To summarise listed below are some points which explain the need of data wrangling:
- Transforming the raw data into usable data ensuring proper analysis and better results.
- Various data integration tools and techniques are used for cleaning and converting raw data into usable data as it is widely used in business today.
- Data wrangling also creates the stage for the data mining process, which includes gathering of data and understanding the semantics.
Let’s look at the steps involved in data wrangling.
Steps in Data wrangling
To make a dataset reliable and fully prepared to use for training purpose, every project requires an assistance of data wrangling and there are various steps involved in the wrangling process:
Discovery
This segment of data wrangling is where we start from. It’s very important to understand what is there in your dataset that paves the way to analyse it properly. We could handle all the missing values in the dataset that can help us to know critical things to keep in mind while training.
Structuring
Not every dataset is fully training ready. One needs to make sure that our dataset is properly structured according to the needs of the model. One would want to convert a 2d dataset into a 3d dataset and vice-versa as per the requirements.
Cleaning
Data cleaning is the process of cleaning needless data points which will eventually disturb the performance of our model. Cleaning can have different forms like: deleting rows or columns, removing outliers, handling duplicate values, etc.
Enriching
This process is another important factor which can play a game changing role as we can enrich our data by deriving important information from the existing data by analysing patterns etc.
Validating
This process ensures consistent and high quality. In other words, it refers to the process of cross-checking whether the data just suits your demands or not.
Publishing
After all the above tasks are carried out, now the data analysts can send and publish this dataset for training purposes.
Check out this problem - Duplicate Subtree In Binary Tree
Frequently asked questions
Q1. Spot the difference between data wrangling and data cleaning?
Ans: Data cleaning is the process that focuses on removing inaccurate data from the dataset but data wrangling refers to the process of transformation of data’s format, generally by converting raw data into a suitable form.
Q2. Describe the importance of preprocessing of data?
Ans: Data preprocessing is a data mining technique where data is preprocessed into an understandable format. Real data is unstable and it cannot be used directly for model training. Hence, there is a need to preprocess the data before sending it through a model.
Key takeaways
This article covers the introduction to data wrangling, explaining its importance and why it’s used. We also saw different analogies which help us understand why it is often used. Keeping the theoretical knowledge at our fingertips helps us get about half the work done. To gain complete understanding, practice is a must. To achieve thorough knowledge on machine learning you may refer to our machine learning course.
Happy Learning Ninja!!!