Pandas, Matplotlib, Seaborn, & More! Analyze Dozens of Datasets & Create Stunning Visualizations
InstructorColt Steele
Instruction covers a wide range of essential operations including handling missing values, type casting, date/time conversions, and hierarchical indexing.
The teaching style is described as clear and digestible, utilizing a lively delivery that maintains focus through frequent visualization examples.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This Jupyter Notebook screenshot demonstrates loading car stock data with pandas and visualizing the Rivian IPO closing prices using a seaborn line plot.
This screenshot shows a Jupyter Notebook cell executing Python code to generate a horizontal bar chart visualizing UFO sighting counts by hour.
This screenshot shows a Jupyter Notebook running a Seaborn pairplot that visualizes relationships between bill dimensions and flipper length across different penguin species.
This slide displays a Python Pandas command to calculate the mean age grouped by sex, accompanied by a sample dataset containing survival status, gender, and age values.
Early sections include frequent exercises, but several signals from before the last update indicate that the later modules covering Matplotlib and Seaborn lack guided practice. While the recent update label suggests potential changes, it does not prove these gaps have been addressed.
Some signals suggest the coding material may be outdated, and there are reports of unanswered student queries. Although a recent update is noted, these pre-update reviews indicate that the current state of the content remains uncertain.
The curriculum aligns well with the target of beginner Python developers interested in data analysis.