Python Data Science: pandas, Matplotlib, and Seaborn

Python Data Science: pandas, Matplotlib, and Seaborn

This course is part of Using Data Science Tools in Python Specialization

Instructor: Bill Rosenthal

Access provided by ASHOKA UNIVERSITY

4 modules

Gain insight into a topic and learn the fundamentals.

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Clean and reshape tabular business data using Pandas DataFrames, handling missing values, and combining files using joins and groups.
Isolate exactly the data you need using index tools, comparison operators, and custom logical masks.
Build stunning visual reports ranging from standard bar charts and histograms to complex subplots, heatmaps, and linear regression charts.
Data files for this course are provided in the first course of this specialization, "Python Data Science: Environment Setup".

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

1 assignment

Taught in English

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Build your subject-matter expertise

This course is part of the Using Data Science Tools in Python Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

To round out your exploration of data science in Python, in this course, you'll work with the pandas DataFrame—one of the most prominent data structures in data science. You'll create DataFrames, load and save data, analyze data, and slice and filter data in DataFrames. Then, you'll manipulate, modify, and plot DataFrame data. Lastly, you'll work with specialized plotting libraries Matplotlib and Seaborn to create common types of plots and format those plots so they are visually appealing and optimal for analysis.

While NumPy serves as the foundation of your data science tasks, you may instead work directly with the more user-friendly library called pandas, which builds on NumPy. Or, you may find it beneficial to work with both. In either case, as with NumPy, you'll want to begin by managing your data within pandas structures and then analyze that data for useful insights.

What's included

1 reading6 plugins

The pandas libraries provides many tools for changing data to meet your needs. It also provides basic plotting functionality for the analysis and/or presentation of data. In this lesson, you'll transform and visualize your data in multiple ways.

What's included

5 plugins

Although you did some simple plotting with pandas directly, you'll likely need to get more detailed with your visualizations. Matplotlib is the most common plotting library in Python®, and you'll use it to generate visualizations that help you tell a story with your data. Likewise, you'll use the Seaborn library, which is built on Matplotlib, to help you streamline your plotting efforts.