You’ll need to parse your data (break it down into its syntactic components) and transform it into a more user-friendly format.
Structuring the data: Most raw data is unstructured and text-heavy. Learn more about exploratory data analysis here. Exploratory data analysis: Carrying out an initial analysis helps summarize a dataset’s core features and defines its structure (or lack of one). Collecting data: The first step is to decide which data you need, where to extract it from, and then, of course, to collect it (or scrape it). So what does data wrangling involve? While the order and number of tasks will differ depending on the dataset, common key steps include: In reality, the latter two are actually subsets of data wrangling…something to keep in mind! For instance, you might hear it used synonymously with activities like data mining or data cleaning. Somewhat confusingly, the term is also sometimes used to describe the individual steps in this process, too. What are the best tools for data wrangling?ĭata wrangling is a commonly used catch-all to describe the early stages of the data analytics process. We’ll start with a quick summary of what data wrangling involves (but you can click straight through to the tools if you prefer): Heard of data cleaning and data mining, too? These are both subsets of data wrangling.īut if you’re new to the field, what data wrangling tools are available to help you? In this post, we highlight a handful of tools you might come across during your early excursions into data. It captures everything from data collection and exploratory data analysis (EDA) to validation, storage, and more. 
An umbrella term, it’s often used to describe the early stages of the data analytics process. One of the first skills that any data professional learns is data wrangling. What are the best data wrangling tools? What are their pros and cons? Read on to find out.
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