15 Questions to Ask During Exploratory Data Analysis

Exploratory Data Analysis (EDA) is a vital part of #datascience. It helps you gain insight into your data, recognize relationships, detect #anomalies, and identify important trends. But it can be hard to know what questions to ask when you are exploring your data.?

Here are 15 questions that can help guide your investigation and give you the information you need to make informed #datadrivendecisions.

1. What are the basic characteristics of my dataset? Ask yourself what type of data is being used, how many observations and variables there are, etc. This will provide an initial understanding of the composition of the dataset and help guide further exploration.
2. What is the overall structure of my dataset? Looking at things like variable types (categorical vs continuous), distributions, etc., will give you an overview of how all the components fit together into a cohesive whole and help guide further exploration into specific areas of interest.??
3. What patterns exist in the data? Are there any #trends or relationships between variables? Identifying the #patterns in the #data can help you better understand how different elements interact with each other and provide insights into what is driving them.
4. Are there any #outliers present? Outliers may indicate an anomaly or an error in the data set or could be indicative of something more significant. Analyzing outliers carefully can help determine why they exist and how they should be addressed.
5. What are the #missingvalues in the data set? Missing values could indicate a problem with the data collection process or could be caused by a user error in entering the information into the system. It's important to understand why these values are missing so that proper steps can be taken to correct them if necessary.
6. What is the correctness of data? Is it coming from an authentic source, does it have duplicate values, etc? It’s important to understand the quality of data because the quality of analysis is directly proportional to the quality of data.?
7. Is there any #correlation between variables? Examining correlations between variables can help reveal hidden relationships between them that might not have been obvious from visual inspection alone. Understanding such correlations can also lead to new insights about your data set and potential areas for further exploration.
8. How does this data compare to past performance? Comparing current #performancemetrics with those from previous periods can help identify changes in behavior over time and allow for more accurate #forecasting of future outcomes based on past performance trends.
9. Is there any #seasonality present? Seasonality refers to recurring patterns within a given period of time, such as month-to-month or year-to-year fluctuations in sales figures due to seasonal fluctuations in demand or other factors outside of our control (e.g., holiday shopping season).
10. How much #variability exists within each variable? Variability measures how spread out individual points within a given variable are relative to one another, providing insight into how much “noise” exists within a particular dataset and helping you decide which techniques should be used during analysis (e.g., clustering algorithms versus linear regression).???
11. Are there any discrepancies between observed values and expected values? If certain observations don’t match up with what was expected from prior knowledge, further investigation may be warranted to determine why this is happening and whether some sort of adjustment needs to be made either on the input side (e.g., cleaning up bad records) or on the output side (e..g, changing model parameters).?
12. A related question to the above one is what are some potential explanations for unexpected results? Unexpected results may suggest something interesting about our underlying assumptions or provide valuable information on how we might go about addressing certain issues moving forward (e..g., improving accuracy by targeting specific features).???
13. How do different subsets of my dataset behave differently? By breaking down our datasets into smaller samples based on various criteria (e..g., geographic location), we can often gain additional insight into how different subpopulations respond differently under different circumstances, allowing us to tailor strategies accordingly for maximum effectiveness/efficiency moving forward!???
14. Do I need to transform any variables before analysis? Certain types of transformations (e.g., scaling) may be necessary for certain types of analysis; it’s important to identify if this is necessary before moving forward with more detailed work on specific aspects of the dataset.
15. Are there any gaps in our understanding/knowledge base that need filling prior to proceeding with deeper analysis? If so, what resources do I need to acquire/utilize in order fill those gaps? It’s important to make sure you have all the relevant information needed prior to conducting more detailed analyses on specific aspects of your dataset; otherwise, your results may not be reliable due to lack of knowledge/contextual understanding on certain topics related to your research question(s).

Conclusion?

Exploratory Data Analysis is a crucial step towards understanding your datasets better so that you can make informed decisions regarding future strategies and approaches going forward! Asking yourself these questions while conducting EDA will help ensure that you get the most out of your analysis while uncovering meaningful insights along the way! With careful consideration of these questions during exploratory analysis, hopefully #businesses will have all they need for making successful decisions backed by reliable evidence from their own datasets!

Which of these questions or any other questions your data science or business intelligence teams look for while doing exploratory data analysis?

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