When I first began working in data science, the question “Have you ever been handed a dataset and then asked to describe it?” confused me. The truth is that exploratory data analysis (EDA) is a critical tool in every data scientist’s toolbox, and the results are invaluable for answering key business questions. My initial response was “What do you mean?” followed by “Can you be more specific?”
A simple definition of an EDA is the process of visualizing data and spotting noteworthy trends like correlated features, missing data, and outliers. EDAs are crucial for offering explanations for why these patterns exist. It’s likely that it won’t show up in your data product, data highlight, or dashboard, but it will provide information for all of these things.
I’ll outline some of the most important advice for completing an efficient EDA below. For the new data scientists, I’ll offer a potential framework that can assist you in beginning your journey. For the more experienced data scientists, the contents of this post may not come as a surprise (rather, a good reminder!). We’ll use a synthetic dataset for illustrative purposes. The information below is not accurate information from Shopify merchants. I want you to take notes as you go through each section as we go step-by-step.
Try to understand the data at a high level before you begin exploring it. Try to get as much context as you can by speaking with the leadership and product before deciding where to concentrate your efforts. According to the intended outcome, you might highlight very different things in your EDA depending on whether you’re interested in performing a prediction task or whether the task is purely exploratory.
Now that you know the background, it’s time to examine your dataset. It’s critical to determine the number of samples (rows) and features (columns) in your dataset. The volume of your data provides information about potential future computational bottlenecks. For instance, it can take a long time to compute a correlation matrix on large datasets. I advise subsampling if your dataset is too large to fit in a Jupyter notebook so you have something that represents your data but is manageable.
It’s usually a good idea to look at the first few rows once you have your data in a setting that is conducive to working with it. The example dataset shown above is one that we can use for our EDA. This dataset is used to analyze merchant behaviour. Here are a few details about the features:
- Get maximum insights from a data set.
- Uncover underlying structure.
- Extract important variables from the dataset.
- Detect outliers and anomalies(if any)
- Test underlying assumptions.
- Determine the optimal factor settings.
How to conduct exploratory data analysis
If you divide the process into steps, conducting exploratory data analysis may be simpler. Following these six essential steps will help you conduct EDA:
1. Observe your dataset
Exploratory data analysis begins with a high-level examination of your dataset. Determine the size of your dataset by counting the number of rows and columns. This can assist you in foreseeing any potential data issues in the future.
2. Find any missing values
You can begin looking for any missing values once you’ve observed your dataset. When you discover missing values, consider the potential causes. If you can identify a pattern in your data, you might be able to use estimates to fill in some missing values.
3. Categorize your values
You can categorize your values after identifying any missing values to determine which statistical and visualization techniques will work with your dataset. You can place your values into these categories:
4. Find the shape of your dataset
Another critical step in the EDA process is determining the shape of your dataset. This step is crucial because you can learn pertinent details about your dataset by looking at its shape. The shape of your dataset shows your datas distribution. Additionally, you can observe data characteristics like skewness and gaps that can teach you more about the dataset. It can also help you identify trends in your dataset.
5. Identify relationships in your dataset
You can start to identify relationships in your dataset as you continue to comprehend it. Try to spot any correlations between values. It is simpler to find correlations and relationships between values when using scatter plots. Make sure to take notes and identify as many parallels as you can. You can begin speculating about the possible causes of certain values’ correlations as you become aware of them.
6. Locate any outliers in your dataset
Finding outliers in your dataset is a crucial step in the EDA process. The values in your dataset that stand out from the rest are known as outliers. Outliers in a dataset can be markedly higher or lower than the other values. It’s critical to spot outliers because they can distort a dataset’s mean, median, mode, or range and change how a visual representation looks. During your EDA, you can find outliers by looking at your graphs or sorting your data in numerical order.
What is exploratory data analysis?
Before beginning to model a dataset, data professionals can use the exploratory data analysis (EDA) technique to better understand it. Some people refer to EDA as data exploration. Identifying the characteristics of the dataset is the aim of the EDA process. EDA can assist data analysts in forming hypotheses and predictions about the data. Data visualization is frequently used in EDA, including the creation of graphs like histograms, scatter plots, and box plots.
Prior to starting an exploratory data analysis, it’s crucial to comprehend a few key concepts:
Benefits of conducting exploratory data analysis
Before beginning to model a dataset, it is helpful to conduct exploratory data analysis to better understand it. Some of the benefits of conducting EDA include:
Organizing a dataset
You can organize a dataset before modeling it thanks to exploratory data analysis, which is a significant advantage. Making assumptions and predictions about your dataset using this information can help. Before you model your data, it can also assist you in making decisions.
Understanding variables
Understanding the variables in your dataset is another advantage of EDA. This can aid in the organization of your dataset and the start of the crucial data analysis step of identifying relationships between variables.
Identifying relationships between variables
EDA can also assist you in determining the connections between the variables in your dataset. Finding the connections between the variables is essential for drawing inferences from a dataset.
Choosing the right model
EDA can also help you select the appropriate model for your dataset, which is a significant advantage. You can choose a data model using all the information you learn from conducting an EDA. It’s crucial to select the appropriate data model because it can make your organization’s data easier for everyone to understand. You can select from a variety of frequently used data models, including:
Finding patterns in a dataset
Additionally, you can use EDA to find patterns in a dataset. It’s crucial to look for patterns in datasets because they can aid in predictions and estimates. This can assist your business in making future plans and identifying potential issues and solutions.
Exploratory Data Analysis
FAQ
How would you conduct exploratory data analysis?
- Data Collection. Data collection is an essential part of exploratory data analysis.
- Data Cleaning. Data cleaning is the process of eliminating any extraneous variables and values from your dataset as well as any errors.
- Univariate Analysis. …
- Bivariate Analysis.
What is exploratory data analysis example?
When using EDA, you are prepared for any number of people to purchase any number of various styles of shoes. You use exploratory data analysis to visualize the data and discover that most customers purchase 1-3 different types of shoes. The most popular types of footwear appear to be sneakers, dress shoes, and sandals.
What are the tools of exploratory data analysis?
TYPES OF EXPLORATORY DATA ANALYSIS: Univariate Non-graphical. Multivariate Non-graphical. Univariate graphical. Multivariate graphical.
What are two methods used in exploratory data analysis?
Histograms, stem and leaf plots, and box plots are the three primary techniques for this kind of analysis.
