What you'll get
- 7+ Hours
- 4 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Gain expertise in visualizing statistical relationships using Seaborn.
- Master the creation of diverse visualizations, such as scatter plots, bar charts, and categorical data plots.
- Explore color palettes and techniques for visualizing linear relationships.
- Understand bivariate and univariate distributions and how to display data distributions effectively.
- Work with advanced visualization types such as cat plots and strip plots.
- Develop proficiency in Seaborn, a high-demand tool for real-world data analysis.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Seaborn | 2h 28m | ✔ | View Curriculum |
| Seaborn Intermediate | 1h 18m | ✔ | View Curriculum |
| Seaborn Advance | 1h 56m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Seaborn Project : Data Visualization Using Seaborn on Census Dataset | 2h 9m | ✔ | View Curriculum |
Description
The SEABORN - Python Data Wrangling & Visualization with Seaborn course is designed to equip participants with in-depth knowledge and practical skills in using Seaborn, a highly sought-after tool for statistical data visualization. As data continues to grow exponentially across industries, organizations require robust solutions to interpret and present data effectively, skills that Seaborn makes accessible. This program prepares learners to pursue careers as Seaborn developers or data visualization specialists, offering strong opportunities in today's data-driven job market.
The training provides lifetime online access and includes hands-on projects that simulate real-world scenarios, enabling participants to build practical expertise and industry readiness. Upon successful completion, learners receive a recognized certificate of completion, widely acknowledged across the industry.
Participants will develop a wide range of skills, including visualizing statistical relationships, creating various types of plots such as scatter plots, bar plots, and categorical data plots, working with color palettes, and analyzing linear, bivariate, and univariate relationships. Advanced visualizations such as cat plots, strip plots, and heatmaps are also covered, ensuring a comprehensive understanding of Seaborn's capabilities.
After completing the course, learners are prepared for roles such as data analyst, business analyst, statistician, quality engineer, QA engineer, analytics manager, team leader, consultant, and more. The program opens diverse career paths for individuals to showcase their expertise in data visualization and analysis.
The curriculum is carefully structured to balance industry requirements with learners' personal goals, providing a meaningful, engaging learning experience. Core topics covered include an introduction to Seaborn, an overview of key concepts, distribution plots, scatterplots, heatmaps, boxplots, swarm plots, and practical case studies to reinforce applied learning.
Requirements
- Basic understanding of machine learning tools and techniques.
- Familiarity with fundamental data science concepts.
- Knowledge of core analytical principles.
- Mathematical background to grasp algorithms and key concepts.
Target Audience
- Data professionals, software developers, and testers.
- Entrepreneurs and students seeking advanced data visualization skills.
- Anyone interested in learning Python Seaborn with the required prerequisites.
