Hello Data Enthusiasts,
One of the most important competencies when working with python is the ability to manipulate and preprocess large datasets into forms that are vetted, logical, and easily fed into automation / analytical tools. One of the amazing features of python when compared to other graphic based tools like excel or numbers is the speed in which it operates. How fast you may ask? When using python in the right way (that is using vector based computation vs. iterative) it is possible to perform calculations in several minutes over millions of individual records (depending on your computing power of course). With graphic based tools you're lucky to load the data, let alone do any meaningful work.
To help you gauge the level of effort and the scaled importance (as dictated by me :D) I have labeled these tutorials as Basic, Moderate, and Advanced. If you're just starting in python take a look at my basic posts. These should give some insight on how you can put python to use in short order to make a difference in your data analysis workflows.
In addition to providing some simple categories on the perceived level of difficulty for python tasks, I also am going to tailor my posts to address specific business problems I often encounter. This should help provide context as to how you can employ the tool to make a difference in your own work and, as I like to say, have some fun along the way.
Thanks for visiting and happy browsing!