In the vast landscape of data science, Exploratory Data Analysis (EDA) serves as the compass, guiding analysts through the intricate terrain of raw data. It is the preliminary step in the data analysis journey, where the analyst delves into the heart of the dataset to unravel its mysteries. It is said that almost 70% of a data scientist’s work is doing EDA. In this article, we'll embark on an enlightening exploration of EDA, uncovering its significance and techniques.
What is Exploratory Data Analysis (EDA)?
Before working with any data, one must understand the data’s characteristics. This involves exploring the dataset. This process will help you to understand the structure, patterns, and peculiarities of the data before diving into more advanced analyses. This is called Exploratory Data Analysis (EDA). It helps explain the features/characteristics of the data. That is, EDA is an investigative process in which you use summary statistics and graphical tools to get to know your data and understand what you can learn from it.
EDA helps you find anomalies such as outliers or unusual observations in your data, uncover patterns, understand potential relationships among variables, and generate interesting questions or hypotheses that you can test later using more formal statistical methods.
EDA also helps you search for clues and insights that can lead to identifying potential root causes of a problem you are trying to solve. Thus EDA work can also be described as detective work.
Important of EDA:
The work of data scientists, analysts and statisticians is to make meaning out of data, however, data comes with numerous irregularities and unnecessary values. There is therefore the need to remove all these irregularities and unnecessary values to make better sense of the data. Thus the primary goal of EDA is to help:
- Understand the Data Structures
- Identify Patterns and Relationships
- Detect Anomalies and Outliers
- Assess Data Quality
- Inform Feature Selection and Engineering
- Optimise Model Design
- Enhance Communication
- Hypothesis Generation
Types of EDA:
Exploratory Data Analysis (EDA) can be broadly categorised into several types based on the techniques and tools used. Below are some of them:
- Univariate Analysis
This focuses on analysing a single variable. The goal is to understand the distribution, central tendency, and variability of that variable. It helps to gain insights into individual features or variables and identify potential issues or anomalies specific to each variable. Summary statistics like mean, median, mode, standard deviation, variance, range, and percentiles and visualisation like histograms, box plots, bar charts, and density plots are used.
- Bivariate Analysis
This examines the relationship between two variables. It helps in understanding how one variable influences or is related to another. Summary statistics like correlation coefficient and covariance and visualisations like Scatter plots, line plots, box plots (for categorical vs. numerical), and heatmaps (for correlation matrices) are employed in this type of analysis.
- Multivariate Analysis
Multivariate analysis involves more than two variables and aims to understand the relationships and interactions between multiple variables. This type of analysis is crucial for understanding how different features or variables relate to one another and how they may collectively influence the target variable. Summary statistics like multivariate correlation, and variance-covariance matrix are used while visualisations like pair plots, heatmaps, 3D plots, and parallel coordinate plots are used.
- Time Series Analysis
Time series analysis focuses on data points collected or recorded at specific time intervals. This type of EDA is crucial for identifying trends, seasonal patterns, and cyclic behaviours. Moving average and autocorrelation are summary statistics used while line plots, seasonal plots and lag plots are visualisations used.
- Comparative Analysis
This involves comparing different subsets of the data to draw insights about their differences and similarities. To do this, summary statistics like grouped means and standard deviations are used while visualisations like grouped bar charts, box plots, and facet grids are used.
- Descriptive Statistics
Descriptive statistics can also be considered a type of EDA because it provides a summary of the main features of a dataset. It includes measures of central tendency, measures of variability, and the shape of the distribution.
Key Steps in EDA:
EDA is akin to an archaeological expedition, where one meticulously moves through layers of data to unearth valuable insights. However, specific steps and techniques used vary depending on the nature of the data and the objectives of the analysis. The journey begins with data collection and cleaning, where the dataset is purged of imperfections and inconsistencies. This ensures that the analysis is built on a strong foundation free from incorrect or misleading data in a dataset. Once the data is clean, summarisation starts using descriptive statistics to show key patterns and trends. Below is an outline of the typical EDA process:
Data Collection:
The first step in the EDA process is data collection. It is the process of gathering and measuring information on variables of interest. This can be done in several ways such as through surveys and questionnaires, observations, interviews and others. Every effective data collection ensures that the data gathered is accurate, reliable, and relevant to the analysis objectives.
Data Cleaning:
Data cleaning, like polishing a gemstone, is an important step before analysing the data. Through this, you fix missing values, correct errors, and remove outliers that can distort the results. Consider a dataset of housing prices filled with missing values and outliers. By carefully cleaning the data, you can turn chaos into order, making it ready for better analysis. Fill in missing values, manage outliers and remove duplicates, resulting in a dataset that is ready for exploration.
Data Summarisation:
Data Summarization refers to the process of reducing and presenting large volumes of data in a concise and meaningful format. It aims to provide an overview or summary of the main characteristics, trends, and patterns such as average values, how spread out the data is, and the overall shape of the dataset, making it easier to understand and interpret. Techniques such as data aggregation, data reduction and descriptive statistics help to achieve this.
Data Visualisation:
Data visualisation plays a crucial role in the EDA process. This is where one utilises charts and plots like bar charts, scatter plots, histograms, line charts, heatmaps etc, to understand the structure, patterns and relationships within our data. Both R and Python have extensive libraries for such as ggplot2, plotly and corrplot in R and matplotlib and seaborn in Python for powerful visualisations.
Hypothesis Testing:
As part of the EDA process, data scientists and analysts may formulate hypotheses or assumptions about the data based on the insights gained from the various analyses and visualisations. Hypothesis testing is a statistical method used to make decisions about a population based on sample data. It helps determine whether there is enough evidence to support a specific claim about the population. Some statistical techniques used are the t-test, ANOVA (Analysis of Variance), F-test, Z-test, chi-square test, Kruskal-Wallis test, etc.
Reporting and Communication:
The last step in the EDA process is to effectively communicate the findings and insights gained from the analysis. This includes generating clear and informative visualisations such as plots, charts and dashboards that effectively convey key findings and patterns discovered during the process, preparing summaries or reports that highlight the most significant insights, potential issues or anomalies and recommendations for further analysis, and presenting findings and insights to stakeholders or decision-makers.
EDA Tools and Libraries:
There are several tools and libraries available for performing Exploratory Data Analysis ranging from programming languages and libraries to dedicated visualisation tools. Below are some of the popular ones:
1. R: R is an open-source programming language and software environment for statistical computing and graphics. It is a widely used programming language among data scientists, analysts and engineers. R offers several packages dedicated to EDA such as:
- dplyr: For data manipulation and transformation.
- ggplot2: For creating detailed and customisable plots. It is a powerful data visualisation package.
- tidyr: For tidying and reshaping data.
- GGally: GGally is an extension of the ggplot2 package that provides functions for visualising multivariate data.
- plotly: For interactive visualisations.
- lattice: For multivariate data visualisation.
- corrplot: For visualising correlation matrices.
2. Python: Python is also a widely used programming language among data scientists, analysts and engineers. It is an interpreted, object-oriented programming language that offers a lot of powerful libraries like:
- Pandas: A data manipulation and analysis library that provides data structures and data analysis tools.
- NumPy: A numerical computing library that supports large, multi-dimensional arrays and matrices.
- Matplotlib: For creating static, animated, and interactive visualisations.
- Seaborn: Based on Matplotlib, it provides a high-level interface for drawing attractive and informative statistical graphics.
- Scikit-learn: A machine learning library that includes utilities for data preprocessing, dimensionality reduction and clustering.
- Scipy: For scientific and technical computing.
- Plotly: For creating interactive visualisations.
3. SPSS: SPSS is a statistical software package used for data analysis and predictive modelling. It provides a graphical user interface (GUI) for performing various statistical tests, creating charts, and exploring data.
4. SAS: SAS is a software suite for advanced analytics, business intelligence and data management. It includes tools for data analysis, predictive modelling and machine learning, with a strong focus on enterprise-level solutions.
5. Power BI: Microsoft's business analytics service for creating interactive visualisations and business intelligence dashboards. It integrates well with other Microsoft products and allows users to connect to multiple data sources.
6. Tableau: A powerful and interactive data visualisation tool that allows users to create a wide range of charts, graphs and dashboards. It supports connecting to various data sources and is widely used in businesses for data analysis and reporting. 7. QlikView/Qlik Sense: QlikView is a traditional BI tool with strong data visualisation capabilities, while Qlik Sense is a more modern and user-friendly version. Both allow users to create dynamic dashboards and perform in-depth data analysis. 8. Excel: Excel is widely used for EDA tasks such as data cleaning, filtering and visualisations using charts and pivot tables.
Conclusion:
Exploratory Data Analysis (EDA) is a critical step in data science and analysis providing data scientists and analysts with a comprehensive understanding of their dataset and valuable insights that inform subsequent analytical strategies and modelling efforts. EDA is like a guiding light that helps data scientists and analysts find insights and understand data better. By cleaning, summarising, making visualisations, and testing hypotheses, EDA helps to identify patterns, relationships, anomalies and potential issues within the data. However, the success of EDA relies on the data scientist’s or analyst’s ability to ask probing questions, formulate hypotheses and interpret the results well.
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