5 Best Practices for Exploratory Data Analysis

Posted by Varun Virat on May 26th, 2023

Understand the Problem

When it comes to using data to propel your business forward, exploratory data analysis is a key step in the process. Before making any major decisions or making investments, taking the time to understand and analyze the data helps you make better predictions. Here are five best practices for exploratory data analysis that should be considered for any data analytics project:

1. Define The Problem: Before getting started, it’s important to define what you are trying to achieve with your analysis. What information do you need? What metrics are you looking at? This will help determine how you structure your data collection and will set up the rest of your steps for success.

2. Research Relevant Data: To get a comprehensive view of what’s going on with your business, think about what types of information would be helpful in understanding the situation better. Is there historical data available that can provide useful insights? Consider which datasets could help inform your decision-making process and acquire them accordingly.

3. Prepare The Data: Once all relevant datasets have been acquired, organize them into formats that are easy to interpret for further analysis. Cleaning up any inconsistencies will ease the downstream processing of events and ensure the accuracy of results. It may also be useful to develop scripts to automate part of this process in order to save time that would normally go into preparing the data manually. Check Out: Software Development Jobs

Gather Data and Develop a Plan of Action

Exploratory data analysis (EDA) is a vital step in the data science process. By gathering data, exploring its variables, and testing select hypotheses, we can develop predictive models that offer useful insights and inform decisions. Here are five best practices to get your exploratory data analysis projects off the ground.

Set Data Analysis Goals

Before beginning an EDA project, it’s important to define the goals of the analysis. You should consider questions like what kind of data you need to answer these questions and how much data do you need for accuracy. What insight do you hope to gain from this analysis? And how will this shape your approach? When it comes to setting up an effective plan of action for gathering and analyzing data, having clear objectives will make a big difference.

Acquire Data

Now that you have set clear goals for your exploratory data analysis, it’s time to start gathering relevant data. Depending on your research needs and objectives, there are different sources to consider. Accessible public databases such as government documents or industry resources provide a wealth of standardized datasets. Other options include manually compiled datasets or surveys distributed through different channels such as social media or email campaigns. Make sure that all the necessary permissions have been acquired if using private sources.

Explore the Data

Exploratory Data Analysis (EDA) can be a great way to uncover insights about data sets. By exploring the various relationships between variables and transformations of data, critical patterns and trends may appear. As a data scientist, it’s important to understand the best practices that you can use to efficiently and effectively explore your data.

One of the best places to start with any EDA is descriptive statistics, which can help you summarize the main features of your dataset. Descriptive stats are a useful tool for evaluating how data is distributed across different variables in a given dataset. For example, let’s say you had a dataset of financial transactions. You could run descriptive stats on this dataset to see how many transactions were made by each customer and how much money they spent each month, which would provide helpful context about how customers interact with your business.

Once you have an understanding of basic descriptive stats in your dataset, the next step is visualizing your data. Visualization tools like charts or graphs can help you identify patterns or trends in your data that may not be immediately obvious from descriptive stats alone. For instance, if you plotted customer spending against the date of the transaction, it may reveal cyclical spending patterns that might suggest certain events influenced customer behavior.

By profiling your data set, you can also gain a deeper understanding of what variables are present in it and draw connections between them. This is especially useful if there are many features or attributes included in the dataset; by analyzing the relationships between them, it’s possible to identify groups or clusters within the population that share certain characteristics or behaviors. Check Out: Software Engineer

Incorporates Visualization Techniques

Using visualization techniques when analyzing data can be a powerful way to gain new insights, contextually represent raw data, and identify trends. Visualization is an essential part of the exploratory data analysis process, enabling us to streamline the analysis, decipher patterns, structure, and represent data in a more effective way. With the ability to leverage color and graphics, these techniques can also help enhance representation accuracy.

In this blog post, we'll cover 5 best practices for using visualization techniques during exploratory data analysis. By following these guidelines you'll have a better understanding of how this approach can make your data exploration journey much easier.

1. Start With The Basics: Before diving into complex visualizations that represent your dataset, it's important to start with the basics such as histograms, bar graphs, and line plots. This will give you an initial overview of the data at hand and provide insights into potential trends or outliers that you may not have noticed initially.

2. Focus on Context: Understanding your data in context is key when exploring it visually as well as statistically. Make sure that any visualizations you use are set up so that they accurately reflect the context of the data you’re dealing with – don't just focus on one aspect of the data without looking at it holistically first.

3. Leverage Color & Graphics: Draw attention to key points by using color and graphics when creating visualizations this can help bring datasets to life and make them easier to explore in more detail. Be sure to use color consistently throughout each visualization; for example, if blue represents positive values then ensure that all positive values are represented by blue across each chart or graph created from your dataset.

Use Relevant Statistical Techniques

Exploratory data analysis (EDA) is a key part of any data-driven process, allowing you to gain insights into the structure and content of your dataset. Following best practices for EDA can help ensure the accuracy of your analysis and prevent potential errors. Here are five essential tips for performing successful exploratory data analysis:

1. Select Relevant Techniques: Thoroughly study your data set before beginning an analysis, so you can select the appropriate techniques to apply. Techniques such as descriptive statistics, data visualization, detecting outliers, and hypothesis testing work well for exploring large datasets. You might also consider applying machine learning algorithms or model selection to detect patterns in the data or make predictions about future events.

2. Select an Appropriate Data Set: To accurately evaluate a dataset, it’s important to choose one that is relevant and legally obtained. Ensure that the dataset you use contains information that’s related to the problem at hand and contains all necessary variables needed for analysis, such as date of birth or other demographic information. Additionally, be sure not to violate any laws or regulations when collecting and utilizing the data.

3. Utilize Data Visualization: Data visualization can be an effective way to gain insights into a dataset quickly and easily. A variety of tools exist that allow you to create informative charts and tables from your dataset; these visualizations can help illustrate relationships between variables in a way that’s easy to interpret by people who lack technical expertise in analyzing datasets. Check Out: Software Developer

Draw Conclusions Based on Findings

Drawing conclusions from data can be difficult and time-consuming, but if done correctly, it can open a window of insights that can drive data-driven decision-making. To ensure you are taking advantage of the data available to you, here are 5 best practices for exploratory data analysis:

1. Multivariate Analysis: When analyzing multiple datasets, use multivariate analysis to look for correlations and uncover patterns from all your related datasets. This will help you find trends and connections that individual dataset analyses may overlook.

2. Visualizations: Use visualizations to turn your data into something tangible so it’s easier to understand what’s going on in the data. You can use charts, graphs, or map visualizations to help identify trends and patterns quickly which will help you make faster decisions.

3. Hypothesis Testing: Testing hypotheses will help determine if there are statistically significant differences between datasets or observations being studied. This is an important point in drawing meaningful conclusions about your data because lacking confidence in the results could lead to poor decisions or wasted resources.

4. Identify Patterns: It is also important to look for patterns when exploring a dataset or running tests on hypotheses testing methods. Finding patterns (whether repeating shapes, colors, or clusters of words) gives context as well as insight into similarities or differences in different sets of observations or datasets which helps draw more reliable conclusions from your findings.

5. Domain Knowledge & Evaluation of Assumptions: Having domain knowledge of the subject matter that you are analyzing can give insights that simple statistical processes cannot uncover and provide a deeper understanding of underlying trends present in the data.  Check Out: Computer Programer

Following these best practices will help ensure an effective exploratory data analysis.

Exploratory Data Analysis (EDA) can help you to identify patterns and relationships between variables in a dataset. It is an important part of the data analysis process and can help you to answer important questions and explore new insights. Following these 5 best practices will ensure that your EDA is successful and effective.

1. Define the Problem: Before you begin your exploratory analysis, it’s essential to take some time to define what question or research objective you are looking to answer. Having a clear idea of what you want to find out will help guide your EDA process.

2. Data Wrangling: Once the problem has been defined, it’s time to do some data wrangling – cleaning, transforming, manipulating, and summarizing data – so that it is ready for exploration. This includes things such as imputing missing values, dealing with outliers, creating new variables, etc.

3. Exploratory Analysis: This step involves exploring the dataset using mathematical and statistical techniques such as correlation analysis, clustering algorithms, hypothesis testing, etc., to identify patterns and relationships between variables in the dataset. It’s important to use visualizations here since they can be useful for identifying outliers or trends in the data that might have otherwise gone unnoticed with just numbers alone.