Ways of Identifying outliers and missing values in your data during exploratory data analysis?

What are outliers and missing values?

In my experience as a data analyst, spotting unusual data points, called outliers, and dealing with missing information are key in making sure our data analysis is reliable.

Outliers are like the odd ones out that can mess up our understanding, while missing values are pieces of information that are simply not there.

By using techniques to find these outliers and filling in the missing parts, I make sure my data analysis is trustworthy, giving me results that are accurate and make sense in a way that everyone can understand.

How can you identify outliers in your data?

Finding outliers is like spotting the unusual or standout values in your data set. Here's a simpler way to identify them:

Visual Check: Picture your data on a graph, like a scatter plot. Look for points that seem really different or far away from the rest—they might be outliers.

Numbers Game: Use some math tricks, like the Z-score or Interquartile Range (IQR). These methods help you figure out which data points are way different from the others.

How to identify missing values in your data?

Spotting missing values is like finding gaps in your information. Here's a straightforward approach:

• Blank Spaces: Look through your data, and if you see empty spaces or cells without any information, those are likely missing values.
• Summary Stats: Check summary statistics, like counts or averages. If some variables have fewer values than others, there might be missing data.
• Data Labels: Sometimes, missing values are labeled as "NA" or "NaN" in your dataset. Keep an eye out for these codes.
• Tech Tools: Use software or tools that highlight missing values. They can quickly show you where the gaps are in your data.

By keeping these simple steps in mind, you can easily identify and address missing values in your dataset, ensuring a more complete and reliable analysis.

How to handle outliers in your data?

Dealing with outliers in your data is part of ensuring accurate analysis.

First you need to

Understand the Context: Investigate the outliers to grasp their significance. Sometimes, they might be valid and essential data points.

Secondly you need to Visualize the Data: Replot your data without outliers to see if they significantly impact the overall pattern.?

if possible apply Statistical Methods

Data Transformation: If needed, transform the data using techniques like log transformation to mitigate the impact of extreme values.

How to handle missing values in your data?

Addressing missing values in your data is very important for a comprehensive analysis. To start, you have to

Identify Missing Values: Locate where data is missing. Ask yourself this question

Is there how missing values might affect your analysis? Consider the importance of the missing data in the context of your study.

You can Input the missing values,? using methods like mean, median, or mode imputation. Be cautious not to distort the overall patterns.

In some cases, removing rows or columns with missing values might be necessary. provided this won't compromise the integrity of your analysis.

And finally, Clearly document how you handled missing values in your analysis to maintain transparency.

For you to conduct Exploratory Data Analysis (EDA) with outliers and missing values you need to be systematic in your approach:

You have to employ visualizations and statistical methods to pinpoint outliers and detect missing values in your dataset.

You have to explore the overall patterns and relationships in your data.

You have to Input the missing values. Be mindful of not distorting the original data structure.

Decide whether to remove, transform, or cap extreme values.?

Revisualize your data after handling outliers and missing values to ensure a clear understanding of the revised patterns.

Use statistical tests to validate the impact of outliers.

And as usual? document the steps taken to handle outliers.