Exploratory Data Analysis
Exploratory Data Analysis (EDA) is a critical method used to analyze data sets, revealing their main characteristics through both statistical and visual techniques. The key aspects of EDA include understanding data structure, detecting patterns, and preparing for subsequent modeling tasks. Utilizing tools such as Pandas, Matplotlib, and Seaborn facilitates effective analysis and visualization, allowing practitioners to derive meaningful insights and make informed decisions based on data anomalies and trends.
Enroll to start learning
You've not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Sections
Navigate through the learning materials and practice exercises.
-
6.1Description
What we have learnt
- EDA helps uncover structure, trends, and anomalies in data.
- Use Pandas for descriptive statistics and summaries.
- Use Seaborn and Matplotlib for visual exploration.
- Interpret plots to form data-driven hypotheses.
- Tools like Pandas Profiling can speed up initial exploration.
Key Concepts
- -- Exploratory Data Analysis (EDA)
- The process of analyzing data sets to summarize their main characteristics, often with visualizations.
- -- Pandas
- A powerful data manipulation and analysis library for Python that provides data structures like DataFrames.
- -- Matplotlib
- A versatile library for creating static, interactive, and animated visualizations in Python.
- -- Seaborn
- A statistical data visualization library based on Matplotlib that provides a high-level interface for drawing attractive graphics.
- -- Correlation
- A statistical measure that describes the degree to which two variables move in relation to each other.
- -- Outliers
- Data points that differ significantly from the majority of the data, which can skew analysis and results.
Additional Learning Materials
Supplementary resources to enhance your learning experience.