Visualizing & Communicating Results in Python with Jupyter (Coursera)

The modules in this course will cover a wide range of visualizations which allow you to illustrate and compare the composition of the dataset, determine the distribution of the dataset, and visualize complex data such as geographically-based data. Completion of Data Analysis in Python with pandas & matplotlib in Spyder before taking this course is recommended.

To allow for a truly hands-on, self-paced learning experience, this course is video-free.

Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours. Finally, an accumulative lab at the end of the course will provide you an opportunity to apply all learned concepts within a real-world context.

Course 2 of 4 in the Data Science and Analysis Tools - from Jupyter to R Markdown Specialization.

What You Will Learn

- Create charts to describe and compare the composition of data sets

- Illustrate the distribution of data through visualizations

- Generate visualizations for specialized data (e.g. geographical, three dimensional, etc)

Syllabus

WEEK 1: Creating Comparison and Composition Charts

WEEK 2: Creating Distribution Charts

WEEK 3: Creating Specialized Visualizations

WEEK 4: Communicating Data Using Jupyter notebook

WEEK 5: Visualizing Data and Communicating Results with Jupyter