course
Introduction to Data Visualization with Seaborn
बुनियादीकौशल स्तर
अद्यतन 01/2026कोर्स मुफ्त में शुरू करें
इसमें शामिल हैअधिमूल्य or टीमें
PythonData Visualization4 घंटा14 videos44 exercises3,700 एक्सपी170K+उपलब्धि का कथन
अपना निःशुल्क खाता बनाएँ
या
जारी रखने पर, आप हमारी उपयोग की शर्तें, हमारी गोपनीयता नीति को स्वीकार करते हैं और यह भी कि आपका डेटा संयुक्त राज्य अमेरिका में संग्रहीत किया जाता है।हजारों कंपनियों में कार्यरत शिक्षार्थियों द्वारा पसंद किया जाता है
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Create Your Own Seaborn Plots
Seaborn is a powerful Python library that makes it easy to create informative and attractive data visualizations. This 4-hour course provides an introduction to how you can use Seaborn to create a variety of plots, including scatter plots, count plots, bar plots, and box plots, and how you can customize your visualizations.Turn Real Datasets into Custom Seaborn Visualizations
You’ll explore this library and create your Seaborn plots based on a variety of real-world data sets, including exploring how air pollution in a city changes through the day and looking at what young people like to do in their free time. This data will give you the opportunity to find out about Seaborn’s advantages first hand, including how you can easily create subplots in a single figure and how to automatically calculate confidence intervals.Improve Your Data Communication Skills
By the end of this course, you’ll be able to use Seaborn in various situations to explore your data and effectively communicate the results of your data analysis to others. These skills are highly sought-after for data analysts, data scientists, and any other job that may involve creating data visualizations. If you’d like to continue your learning, this course is part of several tracks, including the Data Visualization track, where you can add more libraries and techniques to your skillset.आवश्यक शर्तें
Introduction to Python1
Introduction to Seaborn
What is Seaborn, and when should you use it? In this chapter, you will find out! Plus, you will learn how to create scatter plots and count plots with both lists of data and pandas DataFrames. You will also be introduced to one of the big advantages of using Seaborn - the ability to easily add a third variable to your plots by using color to represent different subgroups.
2
Visualizing Two Quantitative Variables
In this chapter, you will create and customize plots that visualize the relationship between two quantitative variables. To do this, you will use scatter plots and line plots to explore how the level of air pollution in a city changes over the course of a day and how horsepower relates to fuel efficiency in cars. You will also see another big advantage of using Seaborn - the ability to easily create subplots in a single figure!
3
Visualizing a Categorical and a Quantitative Variable
Categorical variables are present in nearly every dataset, but they are especially prominent in survey data. In this chapter, you will learn how to create and customize categorical plots such as box plots, bar plots, count plots, and point plots. Along the way, you will explore survey data from young people about their interests, students about their study habits, and adult men about their feelings about masculinity.
4
Customizing Seaborn Plots
In this final chapter, you will learn how to add informative plot titles and axis labels, which are one of the most important parts of any data visualization! You will also learn how to customize the style of your visualizations in order to more quickly orient your audience to the key takeaways. Then, you will put everything you have learned together for the final exercises of the course!
Introduction to Data Visualization with Seaborn
कोर्स पूरा
उपलब्धि प्रमाण पत्र अर्जित करें
इस क्रेडेंशियल को अपने लिंक्डइन प्रोफाइल, रिज्यूमे या सीवी में जोड़ें।इसे सोशल मीडिया पर और अपनी परफॉर्मेंस रिव्यू में साझा करें।
इसमें शामिल हैअधिमूल्य or टीमें
अभी दाखिला लेंजुड़ें 19 मिलियन शिक्षार्थी और आज ही Introduction to Data Visualization with Seaborn शुरू करें!
अपना निःशुल्क खाता बनाएँ
या
जारी रखने पर, आप हमारी उपयोग की शर्तें, हमारी गोपनीयता नीति को स्वीकार करते हैं और यह भी कि आपका डेटा संयुक्त राज्य अमेरिका में संग्रहीत किया जाता है।