Data wrangling — also known as munging or transformation — is the preparation process of taking raw data and transferring it into a structure that is more conducive to data analysis. Once complete, the product can then be stored in an organized format for later use. The process includes data cleaning, or sifting and organizing.
Since so much data is collected in an unstructured fashion today, wrangling is a critical part of ensuring these figures are easily usable. Technology professionals, such as data analysts and scientists, use this technique prior to completing reports, making decisions, or making predictions with collected information.This assists companies in making better informed decisions efficiently.
In general, the steps involved in wrangling are:
- Discovering or understanding what data is present and needed.
- Structuring and organizing input.
- Cleaning the set of errors and streamlining formatting such as dates and abbreviations.
- Enriching data by asking questions about the figures present.
- Validating the set by ensuring accuracy and consistency as well as security and of the information collected.
- Publishing of wrangled data for future use as needed.
These steps ensure that the product used for analytics has been cleared of errors in entry, outliers, and more so that when it is analyzed, it is available to be used to make the best conclusions and decisions. It is also used to make large amounts of raw data more usable and accessible for analyzing.
What Jobs Require Data Wrangling?
Many professions require this skill. Data scientists, analysts, and engineers need to wrangle input before the analytical process. Often, the time spent wrangling exceeds the time spent analyzing the data.
This skill is often valued even more than the analyzing and many job interviews for the aforementioned positions include a practical piece of wrangling. Since this step is so important in analyzing, many companies post jobs specifically for data wranglers.
Skills Needed For Data Wrangling
In order to perform this task, professionals in this field need a variety of both hard and soft skills. These skills include:
- Be well-versed in programming languages linked to analysis: In order to wrangle, data sets in scripts in the languages SQL and Python, as well as in R, are regularly used in wrangling.
- Microsoft Excel proficiency: When wrangling is done by hand, especially when using manageable amounts of data, Excel spreadsheets are generally used.
- Understand data science packages and libraries: Packages like Pandas and NumPy are used to help structure statistical inputs.
- Knowledge of established wrangling tools: While the best data wranglers perform the process by hand, there are visual tools like Trifacta that allow for a systematic approach that is great for beginners or for those with less experience. Since some companies may employ these tools, it is important for wranglers to have some knowledge of how they work, and also be aware of their limitations. While they provide a set structure, they are not as flexible and are better suited for simple sets of data.
- Commitment to lifelong learning: Wranglers must be willing to continue to add to their toolbox as data science is always changing and evolving as data collection becomes more complex and sets of data increase in size. Tools to work with data also evolve and these professionals must stay on top of trends and new technology in order to stay competitive and effective.
- Be a team player: Working with large sets of raw data requires teamwork. Communication and collaboration are important in this line of work.
How to Learn Data Wrangling Skills
Interested in joining the ranks of a wrangler? These data science bootcamps can help you learn the skills needed to enter this field. Bootcamps are offered online, in-person, and in hybrid formats so that you can find the one that fits your schedule and learning needs.