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Rice-Python-Data-Visualization

Rice University - Python Data Visualization

Solutions for Rice's Python Data Visualization

Rice University

Rice University INSTRUCTORS

Instructors: Joe Warren, Scott Rixner

Course Description

This if the final course in the specialization which builds upon the knowledge learned in Python Programming Essentials, Python Data Representations, and Python Data Analysis. We will learn how to install external packages for use within Python, acquire data from sources on the Web, and then we will clean, process, analyze, and visualize that data. This course will combine the skills learned throughout the specialization to enable you to write interesting, practical, and useful programs.

By the end of the course, you will be comfortable installing Python packages, analyzing existing data, and generating visualizations of that data. This course will complete your education as a scripter, enabling you to locate, install, and use Python packages written by others. You will be able to effectively utilize tools and packages that are widely available to amplify your effectiveness and write useful programs.

Syllabus

Week 1

This module will discuss the importance of using and writing documentation. The Python documentation is a valuable resource for learning about language features you haven't seen yet.

project: drawing USA Map with matplot

Week 2

This module will teach you about packages and modules in Python, including how to install packages and how to create your own modules. You will also learn to use the Pygal plotting library.

project: Creating Line Plots of Countries' GDP Data

Week 3

This module will teach you about Python sets. Sets are used to hold unordered collections of data without duplicates. We will also discuss efficiency.

project: Plotting GDP Data on a World Map - Part I

Week 4

The final project of the specialization will enable you to demonstrate mastery of the concepts you have learned up to this point. You will also be able to understand and compare different approaches to reconciling two data sets.