Data exploration is the process of examining, analyzing, and visualizing data to understand its characteristics, patterns, and relationships. It’s a crucial step in the data analysis pipeline, often done before formal statistical analysis or modeling.
Step 1 : Upload your dataset and ask Julius to display a preview of the dataset.
Julius can be asked to show you the first few rows of the dataset and explain the different categories of data. It’s best to use straightforward language in your requests while speaking with Julius (see examples of requests and responses below).
Step 2 : Data Description and Summary
Data Description:
Data Summary:
Step 3 : Data Cleaning:
To enhance a dataset’s quality and dependability for analysis, data cleaning—also referred to as data cleansing or data scrubbing—is the process of finding and fixing flaws, inconsistencies, and inaccuracies in the dataset. It is an essential step in the preparation of the data before beginning any modeling or analysis that is significant.
identifying possible outliers for particular important variables in the sample. Julius went on to describe outliers and provide advice on how to identify and deal with them.
Step 4: More exploratory data analysis (EDA):
The process of evaluating data sets to highlight their key features is known as exploratory data analysis (EDA), and it frequently uses visual aids. Finding patterns, correlations, anomalies, and other insights in the data is its main objective since these findings can help with further analysis and the formulation of hypotheses.
Step 5: Advanced Analysis
A wide range of strategies and tactics are referred to as “advanced analysis,” which is utilized to derive deeper meanings from data, forecast outcomes, or identify intricate connections. These strategies frequently make use of more complex algorithms and models, moving beyond exploratory data analysis and fundamental statistical methods.
1)Recognize the Data Domain: It’s important to recognize the domain that the data comes from before delving deeper into the data. This entails learning about the terms, concepts, and variables that may affect the creation and understanding of the data.
2)Data Profiling: To comprehend the structure of the dataset, including the number of observations (rows) and variables (columns), data types, summary statistics, and variable distributions, begin by performing a basic data profiling. This gives a general summary of the properties of the data.
3)Visual Investigation: To visually explore the dataset, apply tools for data visualization. To see distributions, correlations between variables, trends, and anomalies in the data, create a variety of plots, including scatter plots, box plots, heatmaps, and bar charts.
4)Finding Missing Values: Look for missing values in the dataset and evaluate the distribution and trends of those values. Choose the best methods for dealing with missing data, such as modeling missingness processes, deleting data, or imputation.
- Handle Outliers: Locate extreme values or outliers in the dataset and evaluate how they will affect the analysis. Assess if outliers are legitimate data points or errors, and choose the best course of action for treating them, including transformation, truncation, or robust statistical techniques.
6)Examine your relationships: Use cross-tabulations, correlation calculations, scatter plots, heatmaps, and other visualization tools to examine relationships between variables. Search for dependencies, trends, patterns, and interactions among the variables.
