Master applied Statistics with Python by solving real-world problems with state-of-the-art software and libraries
InstructorSamuel HintonThe curriculum includes diverse practical examples covering topics such as car emissions, diabetes diagnosis, and sales uncertainty.
Instruction covers a wide range of data characterization techniques, including 1D histograms, box plots, and ND scatter matrices.
Editorial course preview
These 2 complementary views highlight concrete, legible examples from the course presentation.
A close-up view of a laptop screen displaying Python code in a terminal window, demonstrating variable assignment and type checking.
The instructor stands between visual markers for practical examples and concept lectures, illustrating the course's balanced approach to combining theoretical foundations with hands-on application.
Instructional signals suggest the course can be overly technical, frequently dropping large code blocks without sufficient line-by-line explanation or underlying theory.
One signal suggests a lack of detailed commentary within the provided Jupyter notebooks to aid independent review. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
There is a mismatch between the beginner designation and the technical depth required to follow the lectures effectively. This signal predates the displayed course update; while an update occurred later, the update label does not prove that it was corrected.
