Understanding Your Data: The Essentials of Exploratory Data Analysis.

What is EDA(Exploratory Data Analysis)?

It refers to the critical process of performing initial investigations on data so as to discover patterns,to spot anomalies,to test hypothesis and to check assumptions with the help of summary statistics and graphical representations.

EDA makes it easier for data scientists to find patterns, identify anomalies, test hypotheses, and verify assumptions by assisting in the best way to alter data sources to achieve the answers they require.

EDA offers a better knowledge of data set variables and the interactions between them and is mainly used to examine what data might disclose beyond the formal modelling or hypothesis testing assignment. It can also assist in determining the suitability of the statistical methods you are thinking of using for data analysis.

So why is EDA Important?

EDA's primary goal is to assist in analysing data before drawing any conclusions. It can assist in locating noticeable errors, better understanding data patterns, spotting outliers or unusual occurrences, and discovering intriguing correlations between the variables.

It helps in guaranteeing that the results produced are valid and relevant to any desired company goals. Standard deviations, categorical variables, and confidence intervals are among the topics that EDA can assist with. The elements of EDA can be applied to more complex data analysis or modelling, such as machine learning, after it is finished and conclusions have been formed.

EDA TOOLS

Some of the tools we use for EDA are;

Exploration and Visualization

• Univariate Analysis: Visualize and summarize each individual variable in the dataset to understand its distribution and characteristics.

• Bivariate Analysis: Examine the relationship between each variable and the target variable to identify potential correlations or patterns.

• Multivariate Analysis: Explore interactions among multiple variables to uncover complex relationships within the data.

Clustering

• K-means Clustering: An unsupervised learning technique that groups data points into clusters based on their similarity. It's commonly used for market segmentation, pattern recognition, and image compression.

Predictive Modeling

Predictive Models: Utilize statistical methods to build models that predict future outcomes based on historical data. Linear regression is an example of a predictive model.

Types Of EDA

• Univariate EDA focuses on a single piece of data at a time. By examining its distribution and identifying unusual values (outliers), we can gain insights into its characteristics.

• Bivariate EDA explores the relationship between two pieces of data. This helps us understand how they are connected and if any patterns emerge.

• Multivariate EDA looks at multiple pieces of data simultaneously. This allows for the discovery of complex connections and unusual values that might be hidden when examining data individually or in pairs.

Within each of these types, there are two primary approaches: graphical and statistical.

1. Graphical EDA -it uses visual representations like charts and graphs to explore the data.

2. Statistical EDA - It employs mathematical calculations to analyze the data.

For instance, univariate graphical EDA involves creating charts to understand a single dataset's distribution, while univariate statistical EDA uses calculations like mean, median, and standard deviation for the same purpose. Similarly, multivariate graphical EDA uses charts to show relationships between multiple datasets, and multivariate statistical EDA uses techniques like regression or principal component analysis.

The common types of univariate graphics include:

1. Stem-and-leaf plots
   Show all data values and the shape of the distribution.

2. *Histograms *
   A bar plot in which each bar represents the frequency or proportion of cases for a range of values.

3.Box plots
Graphically depict the five-number summary of minimum, first quartile, median, third quartile, and maximum.

The common types of multivariate graphics include:

1. Scatter plot
   IT is used to plot data points on a horizontal and a vertical axis to show how much one variable is affected by another.

2. Multivariate chart
   It is a graphical representation of the relationships between factors and a response.

3. Run chart
   It is a line graph of data plotted over time.

4. Bubble chart
   It is a data visualization that displays multiple circles (bubbles) in a two-dimensional plot.

5. Heat map
   It is a graphical representation of data where values are depicted by color.

In conclusion, Exploratory Data Analysis (EDA) is the cornerstone of data-driven decision making. By uncovering patterns, anomalies, and relationships within data, EDA provides essential insights for effective data modeling and analysis.