Understanding data before conducting an in-depth analysis is an essential practice. Exploratory Data Analysis (EDA) is a critical step for data analysis used to uncover the hidden clues in a given data set, and it is necessary because it guides one towards attaining meaningful insights appropriate for making an effective decision towards a specific problem. Consequently, this process also helps one understand the data structure and patterns, detect anomalies, test assumptions, and uncover potential associations between the variables in a study. Some key processes critical for the exploratory data analysis (EDA) process include data collection and preparation, data collection, data analysis, data visualization and reporting, and summary statistics.
Procedures of Exploratory Data Analysis (EDA)
Data Overview and Cleaning
The first step for EDA is data overview and cleansing; data overview is a crucial process in which the data analyst begins with an in-depth understanding of the data set. For example, during this process, it is essential first to understand the kind of data that one is dealing with, including integers, float, strings, dates, or others). This is critical because it informs the various tools and approaches that ought to be used in the entire process of analysis. Data cleaning is a procedure that entails detecting the missing values and outliers and also correcting the inconsistencies. Data cleaning is a foundation for reliable and effective analysis of data.
Data Descriptive
Data descriptive is an appropriate statistical technique that focuses on describing and analyzing a given data set to identify the main characteristics without making any inferences and generalizations. This process provides a critical understanding of the basic characteristics of a given data set. Data descriptive includes measures of central tendency (mean, median, mode), measures of variability (range, variance, standard deviation), and measures of shape, which include skewness, kurtosis, and others.
Data Visualization
This process is used to identify patterns, trends, correlations, and relationships in a particular data set. Some tools used to visualize data include scatter plots, histograms, bar charts, box plots, heat maps, and others. Matplotlib is a comprehensive library mainly used to conduct interactive visualization in Python.
Importance of Exploratory Data Analysis (EDA)
EDA is an important concept because it enhances data quality, provides a better understanding of the chosen data and models, and helps formulate hypotheses essential for making effective insights. Also, it helps detect various issues, such as incorrect assumptions, missing data, and outliers. Some of the EDA tools that are applied include Python libraries such as Plotly, Seaborn, Matplotlib, Pandas, and NumPy, R Libraries (tidyr, dplyr, ggplot2), visualization tools (Excel, Power BI, Tableau), and others.
In conclusion, EDA is an essential process because it provides for an extensive exploration of data analysis and reporting. The EDA process follows no specific process because it varies based on the requirements used for the analysis purposes. However, as discussed, ensuring that the above components are effectively accounted for and understood is essential.