Exploratory Data Analysis (EDA) is a critical step in data analysis process that involves examining and understanding the characteristics of a dataset.
In this article, we will dive into:
What is Exploratory Data Analysis (EDA) ?
Why is it important?
Common techniques used in EDA
What is Exploratory Data Analysis?
The method of exploring and comprehending data through visualization and summarization of its significant features is what we call Exploratory Data Analysis(EDA).
EDA's objective is to acquire a better understanding of the data and to recognize patterns, trends and correlations that might not be evident initially.
Steps involved in Exploratory Data Analysis
EDA typically involve several steps which include :
Data Collection - This involves gathering and compiling
data to be analyzedData Cleaning - Removal or correction of any errors,
inconsistencies or missing data in the
dataset is done here.Data Visualization- This involves creating visualizations
such as histogram , scatter plots and
box plots to explore the data.Descriptive Statistics -Here , we calculate the summary
statistics such as mean , median,
standard deviation and correlation
coefficients to summarize the data
5 Hypothesis Testing - Hypothesis in other words is an
assumption. This step involves
testing hypotheses/assumptions about the
data in order to determine if the data
is statistically significant.
Importance of EDA
EDA is a crucial step in data analysis process because it help us understand the data and identify any potential issue(s) or biases that may affect our analysis and so EDA can help us :
Identify missing data/outliers that may affect our
analysisIdentify trends and patterns in our data that may not be
immediately apparentIdentify potential relationships or correlations between
variables.
Correlation refers to the extent to which two or
more variables are related to each other. If a change in one
variable is associated with a change in another variable, the two
variables are said to be correlated.Identify potential issues or biases that may affect the analysis
Formulate hypotheses/assumptions about the data that can be
tested using statistical methods
Gaining an understanding of the data's characteristics is crucial for making well-informed decisions on how to analyze and interpret the data. Doing so enables us to avoid errors or incorrect conclusions that may be drawn from incomplete or biased analysis.
Techniques used in Exploratory Data Analysis(EDA)
These techniques help us explore and understand the data. They include :
1.Histograms -This is a graph that shows frequency
distribution of a dataset. It help in visualizing the shape of
the data and identifying unusual patterns
2.Box Plots - This are graphical representations of the
distribution of a dataset. It displays the minimum,
maximum, median, and quartiles of the data,
and can help identify unusual patterns in the data.
3.Scatter Plots - A scatter plot is a graph that shows the
relationship between two variables. It is useful for
identifying any potential correlations or patterns in the data
4.Heat Maps- It involves graphical representation of the data
that uses color to represent the values in a dataset. It is
useful for identifying patterns or correlations in large
datasets.
5.Correlation Analysis -This is a statistical technique used to
measure the strength and direction of the relationship
between two variables. It is useful for identifying potential
relationships or patterns in the data.
Conclusion
Exploratory Data Analysis is a critical step in the data analysis
process that involves examining and understanding the
characteristics of a dataset. By visualizing and summarizing the
data, we can identify patterns, trends, and relationships that
may not be immediately apparent. This can help us make more
informed decisions about how to analyze and interpret the data,
and can help us avoid mistakes or incorrect conclusions that may
be drawn from an incomplete or biased analysis.
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