
Section 1: Seaborn Python – Beginners
In this introductory section, students will familiarize themselves with Seaborn, a Python library built on top of Matplotlib that facilitates the creation of informative and visually appealing statistical graphics. They will start by understanding the fundamental concepts of Seaborn and its advantages over other visualization libraries. The lectures will cover essential plot types such as scatter plots, line plots, and categorical scatterplots. Students will learn how to create these plots using Seaborn and gain insights into their interpretation and usage in data analysis tasks. Additionally, they will explore more advanced visualization techniques like box plots, violin plots, and bar plots, enabling them to effectively represent data distributions and relationships.
Section 2: Seaborn Python – Intermediate
Building upon the foundational knowledge from the beginner section, students will delve deeper into Seaborn’s capabilities in the intermediate section. They will learn how to visualize univariate and bivariate distributions using functions like DISTPLOT and JOINTPLOT. Additionally, students will explore the use of regression plots to understand the relationships between variables and how to customize them using different parameters. The lectures will also cover advanced topics such as conditional small multiples, where students will learn to create multiple plots based on different conditions, providing deeper insights into the data.
Section 3: Seaborn Python – Advanced
In the advanced section, students will further enhance their proficiency in Seaborn by mastering more complex visualization techniques. They will learn how to use custom functions to create specialized plots and effectively visualize pairwise relationships between variables. The lectures will also cover advanced styling options such as setting different color palettes and themes to enhance the aesthetic appeal of the visualizations. Additionally, students will explore the use of PairGrid to create a grid of subplots for visualizing multiple pairwise relationships simultaneously.
Section 4: Seaborn Python Case Study – Data Visualization using Seaborn on Census Dataset
In this practical section, students will apply their knowledge of Seaborn to a real-world case study involving the visualization of census data. They will gain hands-on experience in performing exploratory data analysis (EDA) to gain insights into the dataset’s structure and characteristics. Students will learn how to preprocess the data, add new columns, and perform various visualizations using Seaborn. By the end of this section, students will have the skills to effectively visualize complex datasets and communicate their findings through compelling visualizations.
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Who this course is for:
- Python enthusiasts looking to enhance their data visualization skills using Seaborn.
- Data analysts and scientists seeking to leverage Seaborn for exploring and presenting data insights effectively.
- Students and professionals in fields like data science, statistics, and research who want to add Seaborn proficiency to their toolkit.
- Anyone interested in learning advanced data visualization techniques in Python for insightful analysis and presentation of data.
Can I download Seaborn Mastery: Comprehensive Data Visualization in Python course?
You can download videos for offline viewing in the Android/iOS app. When course instructors enable the downloading feature for lectures of the course, then it can be downloaded for offline viewing on a desktop.Can I get a certificate after completing the course?
Yes, upon successful completion of the course, learners will get the course e-Certification from the course provider. The Seaborn Mastery: Comprehensive Data Visualization in Python course certification is a proof that you completed and passed the course. You can download it, attach it to your resume, share it through social media.Are there any other coupons available for this course?
You can check out for more Udemy coupons @ www.coursecouponclub.comDisclosure: This post may contain affiliate links and we may get small commission if you make a purchase. Read more about Affiliate disclosure here.