Exploratory Data Analysis (EDA) is an approach to analysing and summarising datasets in order to identify patterns, uncover relationships, and extract insights. EDA is typically the first step in the data analysis process, and it is often used to gain a better understanding of the data and to identify any potential issues or problems with the data.
The main goal of EDA is to explore and understand the data, rather than to confirm a preconceived hypothesis. It is an iterative process that involves a combination of descriptive statistics and graphical techniques to summarise the main characteristics of the data, and to identify any patterns, outliers, or relationships that may exist.
EDA can be applied to a wide range of data types, including numerical, categorical, and time series data. It is an essential step in the data science process, as it allows data scientists to gain a deeper understanding of the data and to identify any potential issues that need to be addressed before proceeding with more formal analysis or modelling.
The steps involved in performing EDA typically include:
1.Define the research question: Clearly define the question or problem you want to solve with your analysis.
2.Collect and clean the data: Gather the data needed to answer the research question. Then clean the data by removing missing or duplicate values, and correcting any errors or inconsistencies.
3.Summarise the data: Use descriptive statistics, such as mean, median, and standard deviation, to summarise the main characteristics of the data.
4.Visualise the data: Use graphical techniques, such as histograms, scatter plots, and box plots, to create visual representations of the data. These visualisations can help to uncover patterns and relationships in the data that may not be immediately apparent from the summary statistics.
5.Identify outliers: Check for any extreme values or unusual observations in the data that may be skewing the results or warrant further investigation.
6.Check for relationships: Look for relationships or associations between different variables in the data. This step can involve using statistical tests or machine learning models.
7.Draw conclusions and make recommendations: Use the findings from the analysis to draw conclusions and make recommendations for further action or future research.
8.Communicate the results: Communicate the results of your analysis clearly and effectively, typically with a report or presentation that includes the key findings, visualisations, and any relevant information.
Note: There might be slight variations or additional steps depending on the complexity and goal of your analysis. This is general steps that are usually common in EDA.
