We've all been there: You have a question, and you have data. But as soon as you start to dig in, things get messy fast. The data doesn't make sense on its own, and what seems like an easy fix—like adding more columns or rows—turns out to be more complicated than it looks. This is where exploratory data analysis comes in handy.
EDA helps you understand your data by asking questions about it and then looking at the answers through visualizations or statistical analyses (or both!). You can use EDA before beginning any other type of analysis or modeling project so that your project has a solid foundation from the start.
The Challenges of Data Analysis
Data analysis is an important part of any business, but it can be challenging to get the job done. The first step in any data analysis project is to gather data from all the different sources, which can be messy and unstructured. Then you need to clean up that messy data so that it's ready for analysis.
This often involves removing duplicates or correcting spelling errors (like "United States" vs "united states") as well as ensuring that each piece of information has been recorded consistently across multiple documents or sources--otherwise known as harmonization.
Once you've got your clean set of numbers in front of you, there are still many challenges ahead: how do we know how accurate these numbers are? Is our sample size large enough? Are there any biases present? Are some values missing entirely? And finally--what do all these numbers mean together?
Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA) is a tool for exploring your data. It's used to understand what is in your data, and how it can be interpreted.EDA is not the same as analysis. While some people use these terms interchangeably, there are subtle differences between them: The analysis uses statistical methods to make inferences about populations based on samples (i.e., analyzing survey results).
EDA seeks patterns in individual observations or cases; this might include looking at relationships between variables or clusters of similar observations.
This is an important step in the data science process, as it allows data scientists to gain insights about the data and identify potential issues before building models or making predictions. It also helps to identify any missing or inaccurate data, which can affect the accuracy of the final model. By performing EDA, data scientists can gain a better understanding of the data, which can lead to more effective and efficient modeling and prediction.
The Steps of EDA
Understand the data: This is the first and most important step, as it helps you to contextualize all of your findings in a way that makes sense.
Identify patterns and relationships: Once you have an understanding of the data, it's time to identify any interesting patterns or relationships that might exist within it. You can do this by looking at scatter plots or histograms (or both), which can help you spot outliers and missing values.
Look for outliers: Outliers are points on a graph that are farther away from most points on that same graph--they're not part of what's typically expected in this kind of dataset! They often represent anomalies or errors in data collection; however, sometimes they're also just oddities due to chance alone--therefore identifying them will help guide how you interpret future findings related specifically thereto.
Look for missing values: Missing values occur when there is no measurement recorded at all during some point during the collection process; these may occur because someone forgot their phone while taking pictures during their vacation trip so they don't know exactly how many hours they spent swimming each day but we'll never know unless those photos get uploaded somewhere online someday soon...
Overview of the different techniques and tools used in EDA
There are many different techniques and tools that can be used for EDA, including:
- Data visualization: Creating visualizations to understand the distribution and relationship of variables. Tools such as Matplotlib, Seaborn, Plotly, and Bokeh are commonly used for data visualization.
- Data cleaning and preprocessing: Identifying and removing outliers, handling categorical variables, and scaling and transforming variables. Tools such as Pandas and scikit-learn are commonly used for data cleaning and preprocessing
- Data wrangling: Merging, joining and reshaping data, grouping and aggregating data, and creating new variables and features. Tools such as Pandas, dplyr and reshape2 are commonly used for data wrangling.
- Data summarization: Summarizing the data using statistical measures such as mean, median, and standard deviation, and creating summary tables and cross-tabulations. Tools such as Pandas and Numpy are commonly used for data summarization.
By understanding the different techniques and tools used in EDA, data scientists can effectively perform the EDA process and gain valuable insights from their data.
EDA is a powerful tool.
EDA is a powerful tool. It's a way to get to know your data and understand it better so that you can build better models. EDA gives you the opportunity to explore your data in more depth than what traditional statistical tests provide, which means that you can find unexpected patterns in it.
We hope that this article has given you a solid understanding of EDA and its benefits. The next step is to try it out for yourself--you don't have to be an expert in statistics or programming to get started! All we ask is that you keep an open mind as we explore your data together through this powerful process.