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Data Analysis

Python Visualization and Dashboard Practice (Using Seaborn and Plotly Dash) Part.2

This course focuses on developing immediately applicable visualization skills by utilizing Seaborn, a core tool for data visualization, and Plotly/Dash for building interactive dashboards. Through various hands-on exercises, you can simultaneously improve your ability to create insight-driven visual materials and implement web-based dashboards.

4 learners are taking this course

Level Beginner

Course period 12 months

  • usefulit
Python
Python
Seaborn
Seaborn
Python
Python
Seaborn
Seaborn

What you will gain after the course

  • You can visualize data relationships and patterns at a glance using figure-level functions.

  • You can easily create multiple graphs using FacetGrid, PairGrid, and JointGrid.

Practical Python Visualization and Dashboards (Using Seaborn and PlotlyDash)
Part.2 Seaborn

Learn advanced visualization techniques to represent data patterns and relationships at a glance using Seaborn applications.

Building on the Seaborn basics covered in Part 1,
this course teaches advanced objects like FacetGrid, PairGrid, and JointGrid and figure-level functions
to master advanced visualization techniques such as multi-subplot composition, data relationship visualization, and pattern exploration.

Upon completing this course, you will be able to implement advanced graphs that show data analysis results at a glance,
and in the following Parts 3 and 4, this will expand to creating interactive dashboards using Plotly and Dash.


🎯 Learning Objectives

  • You can configure multiple subplots and perform comparative analysis using FacetGrid, PairGrid, and JointGrid.

  • Learn how to use figure-level functions such as relplot, displot, catplot, jointplot, and lmplot/pairplot.

  • Through advanced data visualization techniques, you can enhance static graphs with a focus on analysis.


⚙️ Tools Covered

  • Python

  • Seaborn / Matplotlib



📂 Curriculum

Section 1. Seaborn - FacetGrid

Configure subplots using the FacetGrid object and compare relationships between variables.

Section 2. Seaborn - PairGrid

Create multiple graphs with the PairGrid object and analyze the relationships between each axis.

Section 3. Seaborn - JointGrid

Visualize the relationship and distribution between two variables simultaneously using the JointGrid object.

Section 4. Seaborn - JointGrid

Intuitively represent data patterns and distributions using figure-level functions such as relplot, displot, catplot, jointplot, and lmplot/pairplot.


✅ Checklist

  • This course does not provide instructor Q&A.

  • Lecture materials and syllabi can be downloaded from the first lesson, [Lesson Materials], in the first section.

Recommended for
these people

Who is this course right for?

  • Learners who have completed Part 1 and mastered the basics of Seaborn

  • Those who want to learn advanced visualization techniques for analyzing data relationships and patterns

  • Learners who plan to expand into creating Plotly/Dash dashboards in the future

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16 lectures ∙ (8hr 45min)

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