Exploratory Data Analysis using Data Visualization Techniques

Data visualization techniques are effective tools for exploratory data analysis (EDA), which is a crucial step in the data analysis process that involves looking at and visualizing data to understand its characteristics and patterns. Find below a guide on how to execute EDA using data visualization techniques.

1. Gathering and Loading Data: Collecting and loading your dataset should come first. This could be presented in a database, CSV file, Excel file, or any other format.
2. Understanding the Data: Get a high-level grasp of your dataset to start. Analyze the variables, size, and structure of the data. Recognize the many sorts of data, such as date/time, category, and numerical.
3. Handling missing Data: Determine a strategy for addressing missing values, such as imputing missing values or eliminating rows or columns with missing data.
4. Summary statistics: Compute and display summary statistics to obtain a general understanding of the data. For numerical variables, common statistics include the mean, median, standard deviation, and quantiles. It is possible to determine the frequency of each category for categorical variables.
5. Univariate Analysis: Univariate analysis should be done for each variable in your dataset. Depending on the type of variable, apply different data visualization techniques: i) For numerical variables, make histograms, box plots, or density plots to comprehend their distribution; ii) For categorical variables, use bar charts or pie charts to depict the distribution of categories.
6. Bivariate Analysis: Common visualization methods for exploring the relationships between pairs of variables include scatter plots for two numerical variables, box plots, violin plots, or stacked bar charts for categorical variables, correlation matrices, and heat maps for understanding the relationship between numerical variables.
7. Multivariate Analysis: Parallel coordinate plots can be used to visualize relationships between numerous numerical variables when dealing with multiple variables. Pair plots (also known as scatterplot matrices), which are used to examine interactions between pairs of numerical variables. Stacked or grouped bar charts for illustrating interactions between numerous category variables
8. Time Series Analysis: In order to comprehend temporal patterns in your data, employ time series-specific visualizations including line charts, seasonal decomposition, and autocorrelation plots.
9. Outlier detection: To find and examine potential outliers in your data, use box plots, scatter plots, or other techniques.
10. Interactive Visualizations: Interactive charts can provide you deeper insights into your data by enabling you to zoom in on, filter, and examine the data in greater detail. These plots can be made with libraries like Plotly or Bokeh.
11. Interactive Exploration: EDA is a process that is iterated. You might need to conduct additional research or preprocess the data as you find insights or abnormalities.
12. Documentation: Document your discoveries, ideas, and any data manipulations you perform. When it comes time to present your findings or replicate your analysis, this documentation will be helpful.
13. Presentation and Reporting: Lastly, share your observations and conclusions by presenting your findings through reports, dashboards, or presentations.