Python for Data Science | Data Visualization Using Seaborn | Tips Dataset

✅Topics Covered:

1. Introduction to Seaborn
- Get introduced to Seaborn, a versatile library for creating informative and attractive statistical graphics.
- Learn how to load and inspect the Tips dataset.

2. Boxplot
- Understand the purpose of #boxplot in visualizing the distribution of data.
- Learn how to create boxplots to compare distributions across different categories.

3. Joint Plot
- Explore #jointplots to visualize the relationship between two variables.
- Learn how to display both the #scatterplot and the distribution of each variable.

4. Pair Plot
- Discover #pairplot for visualizing relationships between multiple pairs of variables.
- Learn how to create a matrix of scatter plots for a comprehensive view of data relationships.

5. Catplot
- Understand the flexibility of #catplot for visualizing categorical data.
- Learn how to create different types of categorical plots using catplot, such as bar plots and point plots.

6. Violin Plot
- Explore violin plots for visualizing the distribution and density of data.
- Learn how to create #violinplots to compare distributions across categories with additional detail.

7. Strip Plot
- Understand how strip plots can be used to display individual data points.
- Learn how to create strip plots to visualize the distribution of data points within categories.

✅Hightlights:
- Comprehensive overview of Seaborn's plotting capabilities using the Tips dataset.
- Step-by-step guide to creating various types of plots.

✅About the Instructor:

By the end of this class, you'll have a solid understanding of how to use Seaborn to create a variety of plots, enabling you to visualize and interpret data effectively. These skills are crucial for uncovering insights and communicating your findings in the field of data science.

Join us and continue your journey towards becoming a proficient data scientist with Python. Happy learning and coding!

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