Data wrangling is important to learn if you want to be able to gather, select, and transform data to answer a question or solve a problem. This is essential for two reasons. First, the work of data wrangling can speed the time to develop and test models. Second, it allows for faster analysis and more accurate conclusions. Data wrangling makes data more usable in an organization. Understanding it can help your work better and make sounder decisions based on the data. Understanding it can help you with other data science courses, lead to professional certifications, or simply help you do better at your job.‎

Careers that use data wrangling are usually in data science, data analysis, or data engineering. Data wrangling is usually part of an operational role that includes A/B testing, data visualization, model selection, and debugging. Some people who learn data wrangling go on to study advanced computer science, and others just want to understand how and why data is segmented and analyzed. Jobs in data wrangling call for the use of many different programming languages. Some organizations use SQL, others use Python, and some use pivot tables and other advanced Microsoft Excel spreadsheet functions. As more and more work will be using big data, the ability to clean and manipulate data will become more important.‎

People who are best suited for roles in data wrangling at the entry-level have basic computer literacy, high-school math, comfort with numbers, and willingness to learn. Cleaning and manipulating data call for a sense of logic and order, too. Many people manage data for presentations to others, so the ability to design slide decks and reports may come into play. Data wrangling is a form of communication with others in the organization who want problems solved and need the information to do it. Good data wranglers have an interest in helping others use data better. Their work in debugging and data visualization contributes to the overall organization's success.‎