What Is Data Exploration?

Data exploration is often the first step in data analysis, allowing you to perform an initial evaluation of your data’s structure and underlying patterns. Utilizing this tool can better inform your data analysis and more accurately represent your data. In this article, learn about the overview of different types of data exploration, what professionals use this method for, its pros and cons, and how to get started in a career that uses data exploration. 

Read more: What Is Data Analysis? (With Examples)

What is data exploration?

Data exploration is a statistical process that lets you see how your data is distributed, identify any outliers, and determine which statistical tests might be most appropriate. When deciding what type of analysis or interpretation is most accurate for your data, preliminary data exploration can help you understand its characteristics.

Types of data exploration

Each of the following three methods can provide you with valuable insights into your data. Descriptive and visual analysis gives a first impression of your data. Once you have a general overview, you can move into more involved statistical analysis to help you progressively build an understanding of your data. 

1. Descriptive analysis 

Descriptive analysis offers the most basic overview of your data. For example, let’s say you are an educator and have a spreadsheet of test scores for your learners. Descriptive analysis would give you a summary of the data and key features. You could look at frequency counts within certain grade ranges, the average grade, any outliers, the range of grades, and how many learners you have data for. Descriptive analysis is like taking an inventory of what you have before deciding what to do with it.

2. Visual analysis 

Visual analysis helps you visualize your data's trends, distribution patterns, outliers, and tendencies. This may be particularly useful if you have large data sets that are difficult to understand with numbers alone fully. You can see the bigger picture of your data by creating visual representations like graphs, charts, and plots. This is also helpful when sharing information about your data with a wider audience, as you can provide a more general summary.

3. Statistical analysis 

Statistical analysis provides a deeper look into your data using mathematical tools. You can use statistical analysis to test your hypotheses, test for independence of variables, look for correlations, assess collinearity, see how variables interact, and understand the probability of certain outcomes. 

Before starting this step, it is important to understand your data structure and how its characteristics might affect statistical tests. For example, several statistical tests require your data to have a normal distribution. By creating a visual representation of your data before starting statistical analysis, you can minimize the likelihood of choosing inappropriate statistical tests for your data.

What is data exploration used for?

Data exploration establishes a basis for more complex analysis. By exploring their data, professionals in many industries can understand the basic structure of their data, identify anomalies or outliers, and validate or challenge their hypotheses. Data exploration is important to ensure the reliability and relevance of subsequent data analysis.

Who uses data exploration?

Careers where you might see data exploration playing a larger role include:

• Data scientists

• Researchers

• Statisticians

• Business analysts

These professionals use data exploration to make informed decisions and to extract meaningful insights from raw data. For instance, a researcher might want to know whether a correlation exists between eating certain foods and disease risk in particular demographics. To decide what statistical tests they should use to perform this analysis and refine their hypothesis, they must take a data exploration approach to characterize the data and identify key attributes.

What industries use data exploration?

You will find that this technique is widely used across various sectors. Here are examples of data exploration being used in different industries:

• In finance, exploratory data analysis helps understand company revenue flow and identify areas of opportunity.

• In health care, it aids in interpreting patient data.

• In retail and marketing, it identifies customer behavior and trends.

By understanding this method and its importance, you can apply it to your specific area and strengthen your data analytics skills. 

Advantages and disadvantages of using data exploration

Data exploration offers many benefits, but recognizing its limitations may help you create strategies best suited to your needs. You can also take steps to reduce the impact of limitations while maximizing the benefits. 

Advantages of data exploration

Data exploration has several benefits, including:

• Offering a comprehensive understanding of the data set

• Highlighting important features and potential issues

• Providing insight into appropriate analysis techniques 

• Guiding future research questions and directions

Disadvantages of data exploration

Limitations of data exploration may include:

• Difficulty visualizing the high-dimensional data from images, video, and speech

• May become complex for complicated data structures

• Risk of personal biases that influence the exploration process 

• Skewed interpretations of findings

• Misrepresenting data by choosing the wrong summary indicator

How to build data exploration skills

To build your data exploration skills, you can start with online courses and workshops offered by industry and academic leaders on learning platforms like Coursera. Consider beginner-friendly courses that offer direct experience so you can gain knowledge with applied practice.

After building basic skills, it’s a good idea to attain practical skills working on real-world data sets. With guided projects, you can participate in data challenges, hackathons, or more advanced academic courses. Document your learning journey with a portfolio showcasing your data exploration projects. This can help set you apart from other candidates when applying for jobs.

If you want to develop your skills through an entry-level role, you can explore positions such as a data analyst or junior data scientist. If you are just beginning, consider a data analytics internship. This is a great way to build your field experience, connect with mentors, and learn from others in a collaborative environment. For an entry-level data analyst in the US, you can expect to earn an average annual base salary of $75,318 [1].

Get started with Coursera.

On Coursera, you can find a range of courses, Specializations, and Professional Certificates focused on data analysis. Starting with foundational courses in data exploration, you can gradually build up to more advanced topics. These courses often include practical projects, which are great for applying the concepts you learn. As a beginner, consider starting with the Google Data Analytics Professional Certificate and learn at your own pace.