Data Science and Artificial Intelligence

What is Data Wrangling? 6 Steps in Data Wrangling

If you have been following data science or have some experience in the field, you will no doubt have heard of data wrangling before. Data wrangling is said to offer many perks to data scientists, but many are still unaware of how it can help them in their analytics. If implemented well, data wrangling could definitely turn out to be one of the most critical practices at your disposal.

What is Data wrangling?

Data wrangling refers to the process of cleaning, restructuring and enriching the raw data available into a more usable format. This will help the scientist quicken the process of decision making, and thus get better insights in less time. This practice is being followed by a large number of top firms in the field, partly owing to the benefits it has and partly because of large amounts of data which is supposed to be analysed. Organizing and cleaning data before analysis has been shown to be extremely useful and helps the firms quickly analyse larger amounts of data.

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What are the Data Wrangling Steps?

Data wrangling, like most data analytics processes, is an iterative one – the practitioner will need to carry out these steps repeatedly in order to produce the results he desires. There are six broad steps to data wrangling, which are:

1.      Discovering

In this step, the data is to be understood more deeply. Before implementing methods to clean it, you will definitely need to have a better idea about what the data is about. Wrangling needs to be done in specific manners, based on some criteria which could demarcate and divide the data accordingly – these are identified in this step.

2.      Structuring

Raw data is given to you in a haphazard manner, in most cases – there will not be any structure to it. This needs to be rectified, and the data needs to be restructured in a manner that better suits the analytical method used. Based on the criteria identified in the first step, the data will need to be separated for ease of use. One column may become two, or rows may be split – whatever needs to be done for better analysis.

3.      Cleaning

All datasets are sure to have some outliers, which can skew the results of the analysis. These will have to be cleaned, for the best results. In this step, the data is cleaned thoroughly for high-quality analysis. Null values will have to be changed, and the formatting will be standardized in order to make the data of higher quality.

4.      Enriching

After cleaning, it will have to be enriched – this is done in the fourth step. This means that you will have to take stock of what is in the data and strategise whether you will have to augment it using some additional data in order to make it better. You should also brainstorm about whether you can derive any new data from the existing clean data set that you have.

5.      Validating

Validation rules refer to some repetitive programming steps which are used to verify the consistency, quality and the security of the data you have. For example, you will have to ascertain whether the fields in the data set are accurate via a check across the data, or see whether the attributes are normally distributed.

6.      Publishing

The prepared wrangled data is published so that it can be used further down the line – that is its purpose after all. If needed, you will also have to document the steps which were taken or logic used to wrangle the said data.

If you want to learn more about data wrangling and data analytics broadly, check out the courses offered by Acadgild!

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