CS250: Python for Data Science

seaborn is an advanced visualization module designed to work with pandas dataframes. Follow along with the programming examples for an introduction to seaborn's capabilities. Pay close attention to how it is applied in tandem with pandas. For instance, notice how the fillna method is used for data cleaning. Additionally, observe how powerful seaborn can be, for example, as scatter plots are created for all numeric variables within a dataframe using a single command.

In many Python visualization presentations, you will see an almost "stream of consciousness" movement between matplotlib, pandas, and seaborn. When presented in this way, it can get a little confusing when tutorials jump around from one module to another (as you gain expertise, you most likely will end up doing the same). In this course, we have made extra effort to decouple these modules for you to understand how they work individually. At this advanced stage, however, be prepared for some overlap between various modules when it comes to visualization techniques. Watch this tutorial to practice examples of how matplotlib and seaborn are applied for visualization.

Watch this tutorial to practice more examples of how pandas and seaborn are applied for visualization.

A tool very often used for plotting the results of statistical experiments is the box plot. It provides a quick visual summary of the maximum, minimum, median, and percent quartiles. Practice these programming examples to apply various quantities previously introduced in the statistics unit.

There is no substitute for much programming practice when connecting statistics and visualization. Follow along with this tutorial to refine your programming skills and review scatter plots, bar plots, pairwise plots, histograms, and box plots.

With your knowledge of Python visualization, this video offers some food for thought. You should gauge your confidence level for developing and implementing code to analyze data science problems by watching the examples.

Here is more Python practice with a specific application that applies a suite of programming techniques and commands. At this point in the course, your goal is to assimilate the knowledge presented to begin making higher-level connections between the materials presented in the course units.