(EDA)
Exploratory Data Analysis (EDA) refers to the initial phase of a Data Science project, involving the use of graphical and statistical methods to explore and analyze data sets. It is a crucial part of the journey of gathering insights and forming hypotheses from raw data, ultimately leaving Data Scientists with two choices: to confirm or rule out theories in order to find sound answers and actionable items based on the data.
What Is Involved in EDA?
Exploratory Data Analysis seeks to uncover patterns both large and small in data sets with the intent of forming further hypotheses and validation tests. Various methods are employed, from graphical methods such as plotting, to statistical techniques such as correlation coefficients, to accomplish this. It can also involve dimensionality reduction techniques, such as principal component analysis, which are used to effectively reduce the number of variables in consideration.
Data preparation is a key component of EDA, often done prior to any graphical or statistical analysis. It involves tasks like data cleaning, filtering, Imputation, and sampling to ensure that the data is in an optimal state for exploratory analysis.
Key Features and Considerations
Exploratory Data Analysis is an important step in a Data Scientist’s journey, and should be the first step of any Data Science project. It allows the creation of an initial understanding of the data before making any decisions, allowing a quicker and more informed response to potential hypotheses.
Here are some of the key features and considerations of EDA:
* Embrace uncertainty and leverage creative methods to uncover hidden insights.
* It’s important to develop deep knowledge of the data at the outset and design smart techniques to draw information from it.
* Make sure to clean the data before any analysis, it should be complete, consistent and valid.
* Check for outliers, anomalies, and patterns in the given dataset.
* Understand the relationships between the variables, or clusters of data points.
Real-World Example
Suppose a financial manager came across a dataset of financial records that contains records from their customers, including columns such as age, annual income, and date of purchase. They would likely perform exploratory data analysis on this data to look for patterns or correlations that might be informative of their customers’ behaviour. This could range from simple graphical techniques, such as plotting line graphs to show the relationship between age and annual income, to more complex methods such as a clustering algorithm to identify any co-dependent features of the dataset.
Conclusion
Exploratory Data Analysis is an essential component of Data Science, allowing Data Scientists to quickly and effectively understand a dataset and uncover new patterns and relationships. It requires skill, creativity, and knowledge of data preparation techniques, as well as graphical and statistical methods to successfully derive new insights from a dataset. This early insight is invaluable when deciding which strategy to take when formulating hypothesis and validating those hypothesis.
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