Exploratory data analysis can guide you through the process, transforming those inscrutable figures into invaluable discoveries. With the right techniques, you’ll go from tables and columns to detailed analysis. In just a few simple steps, you’ll uncover the hidden value in your data and gain the skills to tackle any dataset.
Exploratory Data Analysis or EDA is the process of analyzing data to uncover patterns, insights, and relationships. It helps you get familiar with your data to generate ideas and hypotheses to guide modeling and analysis.
Looking for trends
EDA is like detective work where you dig deeper and deeper and find new things. You’re searching for clues in the data that point to trends, outliers, and relationships. Things like steadily increasing sales over time, purchases peaking around the holidays, or product preferences varying by region. Spotting these trends can lead to a clear understanding of the data.
Generating questions
As you explore the data, you’ll come up with questions about what you’re seeing.
- Why are sales dropping for a certain product?
- Why do some customers buy more frequently than others?
EDA is all about generating these questions and perform analysis. With more Questions comes great analysis.
EDA is a crucial first step in any data analysis to get acquainted with your data. While it can feel unstructured, remember that the goal is to find clues, generate questions, and summarize key attributes. So get curious, identify the trends, and see what you can uncover in your data!
EDA Techniques and Tools
Visualization
One of the most useful EDA techniques is data visualization. Creating charts, graphs, and plots allows you to spot patterns, trends, and outliers in your data. Some options include:
- Scatter plots to show the relationship between two variables. Look for clusters, curves, and outliers.
- Histograms to see the distribution and shape of a single variable. Check for normality, skewness, and outliers.
- Box plots also display the distribution of a variable. The box shows the middle 50% of values, the median is the line inside the box, and the whiskers show the minimum and maximum values.
- Heatmaps show the relationship between multiple variables in a grid format using color coding.
Summary Statistics
Calculate summary stats like the mean, median, mode, standard deviation, variance, minimum, maximum, and quartiles. These give you a high-level sense of your data and can reveal skewness or heavy tails.
Correlation Analysis
Check for relationships between variables using the correlation coefficient. Values range from -1 to 1, indicating negative to positive linear relationships. Be aware that correlation does not imply causation. Variables can be correlated without a direct causal relationship.
Hypothesis Testing
Use statistical tests like the t-test, ANOVA, and chi-square to determine if differences between groups or relationships between variables are statistically significant. Set a significance level (like 0.05) and check if your p-value is below that level. If so, you can reject the null hypothesis that there is no difference or relationship.
Exploratory data analysis is an iterative process. Visualize your data, calculate summaries, check for correlations and test hypotheses. Then go back and do it all again. Reveal the layers of your data one by one to reveal key insights that can drive business decisions and guide your modeling approaches. The tools and techniques of EDA are simple but extremely powerful for understanding what your data can tell you.
Step-by-Step EDA Process
Exploratory data analysis is an iterative process. As a Data analyst, you get to know your data by diving in and exploring, then coming up for air and evaluating what you’ve learned. The key is not to get overwhelmed by the details. Follow these broad steps to conduct EDA:
Look at the Big Picture
Start by importing your data and checking the basic attributes like number of rows and columns, data types, and missing values. Look for any obvious errors or inconsistencies. This helps you get the lay of the land before zooming in.
Analyze Each Variable
Now examine each variable individually. Look at summaries like mean, median, mode, minimum and maximum to understand the distribution. Check for outliers or skewness. See how each variable relates to your target or dependent variable. This will help determine which factors may be most important in your analysis.
Find Patterns and Relationships
Next look for relationships between variables. Try creating scatterplots, heatmaps or correlation matrices to visualize connections. Strong correlations may indicate redundancy or confounding factors in your data. Look for interesting patterns that provide insights into your research questions.
Test Your Assumptions
EDA is also about challenging any assumptions you have about the data. Try segmenting the data in new ways, stratifying by certain attributes or running analyzes on subsets. See if the patterns hold or if new insights emerge. Let the data speak for itself rather than imposing your preconceptions.
Repeat and Refine
EDA is an iterative process, so keep looping back over your data as new questions arise. Revisit summaries and visualizations, drill down into details or take a step back for a fresh perspective. Each pass may reveal new insights to guide your analysis. With practice, EDA becomes a habit of mind for unlocking the secrets hidden in your data.
Applying EDA: Real-World Examples and Use Cases
Identifying Outliers
EDA is great for spotting outliers, data points that are very different from the rest. These could be errors, or they could point to something interesting. For example, say you have data on customers’ monthly spending at your store. Most people spend between $200 to $500 per month, but you notice one customer spends over $2,000 each month. This could indicate a data entry error, or it could be a highly valuable customer you want to give extra attention. EDA helps you find these outliers so you can investigate further.
Detecting Trends
Looking at data over time is one of the best ways to identify trends. For example, you may plot monthly sales or website traffic over the course of a year. You might notice an upward trend, indicating growth, or a downward trend showing a decline. Spotting these trends early on allows you to take action, such as ramping up marketing during slow months or allocating extra resources to support increasing demand.
Exploring Relationships
EDA also helps explore relationships between variables in your data. For example, if you have data on customers’ locations and purchases, you can look for patterns to see if customers from certain areas tend to buy more of a particular product. Or you may find that higher income customers have larger order sizes. Uncovering these relationships can help you tailor your marketing and product offerings.
EDA is a powerful first step in understanding your data and unlocking valuable insights. By applying EDA techniques to your own data, you’ll gain a deeper understanding of the trends, outliers, and relationships that drive your business—and be able to take action on the findings. The key is exploring with an open and curious mind, not being afraid to ask questions, and letting the data guide you to new discoveries.
Conclusion
Goal is to gain the understanding of the data by EDA so that you can build insights and have a bigger picture on how you should design your reports and dashboards and how that will solve a real world problem. By diving into your data, poking around, slicing and dicing it every which way, you give yourself the chance to see things that were invisible before. The insights are there in your data, just waiting to be discovered.
Gulfam Pervaiz
Consultant
