The world of data is a fascinating place, and there are so many diffuse methods of approaching datasets and statistical models. Many practitioners in this realm ask, ‘what is data exploration?’ as they seek a greater depth of knowledge in approaching the datasets and analytical tasks that make up their workweek.
Data exploration, simply put, is the larger evaluation of a dataset for its properties rather than the data itself. This is a powerful first step when collating or calculating any components within your ongoing research dataset. Data exploration is like listening to the ocean or forest for direction. It’s a process that allows you, as the data scientist, to follow the patterns that appear within the data sets that you work with.
Data exploration calls on data scientists to hold off on snap decisions regarding data visualization or correlation analysis. In this sense, finding your bearings early on in the process is far and away from the most important step when approaching a new project, trial, or research question. Looking at the information’s scope and magnitude before you is a great way to build a depth of understanding about the collected data that you will use in your programming or research operations for the days, weeks, or months to come.
Besides, data exploration takes place across disciplines. This technique is equally at home in a laboratory setting, testing a new vaccine—in the fight against the coronavirus, perhaps—as it is in the realm of political science with researchers seeking to create new polling paradigms. Each data point represents something unique and magnetically powerful, so allowing these attributes to lead you on the first step through your data exploration technique will give you an edge over others that skip this function.
Exploratory data analysis can help define correlation and outliers in the raw data far faster than jumping right into a manipulation phase. Whether you are working on a deeper analysis of deforestation patterns or working to build a unified theory on particle physics, data preparation and the next steps depend on your commitment to internalizing the dataset during this initial step. Many scientists draw from various sources and must bring all relevant data to create initial patterns and categorical variables that will guide the remainder of their search for answers in any given field. Leaning on your instincts helps understand where the data models point is a powerful initial step when working with large data sheets in Excel or another manual method.
Data visualization is often the end product of this first step of the research. Its utilization is growing across industries as tech takes a firmer hold on all aspects of the enterprise. Utilizing visualization tools like a scatter plot or bar graph can help your team visualize the data in an easy-to-use format and chart the way forward rapidly. Data visualization helps direct your business intelligence tools toward the most promising categorical variables and avenues for growth. In a business setting, the insights gained through this analysis can save an amazing amount of time, energy, and money by charting the best possible course forward toward points of interest or anomalies that show promise in the market. By contrast, without these techniques, your operations will have to sink energy into exploring numerous projects and streams of research at the same time to identify the winning strategy manually. Spreading your resources thin makes progress slower and less efficient.
Rely on the data to guide your path forward. Whether you reside in a corporate board room or a research lab, data exploration is the best way to begin any new project.