Unlocking Insights with Exploratory Analysis

Exploratory data analysis allows you to create deeper insights into your data, uncover patterns, and extract relevant information to inform the next steps of your analytical process. This is an important initial step in the investigative process, and learning how to take advantage of the power of exploratory analysis can help you make more informed decisions in your industry. 

Understanding exploratory analysis

Exploratory data analysis (EDA) is typically the first step you would take to “get a feel” for your data before fully diving in. Utilizing EDA methods allows you to view your data with an open mind and investigate the information present without focusing on confirming specific statistical hypotheses. By removing prior assumptions, you can find errors more clearly, confirm you’re asking relevant questions, and align your analysis methods to produce the most accurate results.

Typically, EDA involves using a mix of visualizations and summary statistics to represent the data as a whole. From this, you can clean your data more effectively, split your data into natural groups for subgroup analysis, and begin to understand the story of your data. In some cases, you may naturally find underlying answers to problems you are trying to solve. 

Goals of exploratory analysis

When you conduct EDA, your goal is to learn as much as you can about your data. This includes the variables in your data, how their values are distributed, and the relationship between your variables. By assessing the shape of your data and the information included, you can spot outliers or unusual values, detect patterns or trends, and gain insight into what type of further analysis may be appropriate.

Going further, depending on your intended outcome, you may have more nuanced goals for EDA. Use EDA to identify the most important variables in your data set, check assumptions for hypothesis testing, or identify the minimum number of variables to explain your data. If you’re using machine learning methods, EDA is an important step toward providing the context you need to create an accurate model. 

Types of exploratory analysis

The three main types of EDA you can use are univariate, bivariate, and multivariate. A good rule of thumb is to start with univariate analysis and work your way to more complex or layered methods. In each case, you can choose graphical or nongraphical approaches, depending on whether you prefer visual or numerical descriptors for your data.

Univariate EDA

As you might guess by the name, univariate involves looking at a single variable. In this case, you would look at the distribution of one variable, identify outliers, and generally understand the patterns in the values of that one variable without looking at how it relates to any other variables.

Imagine you’re analyzing an “age” variable in your data set. You might examine mean, median, mode, standard deviation, and other metrics to gain an initial understanding. These insights help you decide which descriptors to use and which types of further analysis to conduct. For instance, if 95 percent of your participants are between 60 and 80 years old, your research questions and conclusions will likely differ from a scenario where 95 percent are between 10 and 40 years old. Going further, if 95 percent of your participants are between 60 and 80 years old, and the other 5 percent are 20 years of age, you might need to reconsider how you represent the data. In this case, a single average won’t reflect the wide age range, prompting you to find alternative descriptive statistics to capture the story of your data more clearly. 

Bivariate EDA

In bivariate EDA, you’re going one step further to look at how two variables relate. This helps you see how changes in one variable might relate to another variable. This helps you find correlations and relationships within your data set. In this EDA, you might look at correlation coefficients or other correlation values to understand how strongly two variables relate.

Continuing the previous example, imagine you’re now looking at the relationship between age and income. You might use a data visualization method like a scatter plot to assess how the age and income variables relate. You might see that as age increases, income increases as well, which helps you identify a positive correlation between the variables. You might also spot outliers or find age ranges where the trend is more or less consistent. 

Multivariate EDA

Multivariate EDA continues bivariate EDA to include three or more variables. This helps you look at complex interrelationships and identify the interactions between variables. When you have complex or multifaceted data sets, this can help you gain a clearer picture of what is happening within your data. In this case, you might use more advanced techniques like clustering, factor analysis, and regression to create and adjust your models. 

With this type of analysis, you can now look at age, income, and education. You might notice that income increases for age more consistently for participants with a higher education level, while it levels earlier for those with fewer qualifications. This would suggest an interaction between age and education. By looking at multiple variables and their relationships, you can reveal deeper insights and create more informed research hypotheses.

Applications of exploratory analysis

Any profession that utilizes data can benefit from exploratory data analysis to help them understand their data and more accurately set up additional steps. Some ways you might see EDA in action include:

Business

In business, you might use EDA to understand customer demographics, identify demand drivers, develop accurate sales forecast models, and gauge customer satisfaction with a particular product or service.

Banking and finance

In banking and finance, EDA can inform users of pricing strategies, portfolio management, product development, resource allocation, and investment decisions. It can even identify patterns in customer behavior for marketing strategies and campaign development.

Health care

EDA is particularly effective when assessing electronic health care records, understanding patient demographics, planning health care interventions, and uncovering seasonal patterns related to disease incidence.

Public health

In public health, EDA can help you understand the spread of infectious diseases and the prevalence and presentation of different conditions globally. It can also help you identify important variables for public health intervention design.

Tools to start your exploratory analysis

In any professional sphere, having the right roles for EDA can help you uncover trends, spot outliers, and gain insights into your data. Both Python and R are widely used for EDA thanks to their intuitive user interfaces, built-in packages, and flexibility with data manipulation and visualization. When starting, consider the following packages in each to help you automate your EDA process.

Python:

• DataPrep: Helps you clean, validate, and explore your data in one go

• pandas_profiling: Generates a report with descriptive statistics, text analysis, and more

• SweetViz: Reports on data features, target analysis, and even compares data sets

• AutoViML / AutoViz: Provides an in-depth set of visualizations appropriate for any size data set

R:

• skimr: Provides a concise text summary of your data

• corrplot: Creates visualizations showing how your variables relate

• SmartEDA: Generates an EDA report with built-in analytics

• janitor: Cleans data and creates frequency tables to show distributions

Learn more analytical techniques on Coursera

By using a combination of visualizations and summary statistics, exploratory analysis helps you organize your data, inform research questions, and gain comprehensive insights into your information. On Coursera, you can take online courses taught by leading universities and industry leaders at your own pace. To start, consider Data Visualization & Dashboarding with R Specialization by Johns Hopkins University, a four-module self-paced course.