I recently finished this course.
Like the above testimonials mentioned,
@APalley and
@Andy Nguyen definitely did an excellent job with putting this course together and it covers a lot of useful material that is used a lot in day-to-day work--I think anyone who has worked even a bit in industry would agree with this.
While I didn't take the first half of this course, I can say that from both my past and current jobs, many of the Python concepts taught and practiced from levels 1 to 7, especially object-oriented programming, are used heavily in the financial/commodity industry, but unfortunately not covered in much detail in college/grad school. Oh and that Asset-Backed Security pricing project in level 7 is definitely something that will impress!
For the second half of the course, I will break it down into levels.
Level 8 gave an extensive overview on Pandas data structures, data manipulation and sources for data. This level greatly refreshed my knowledge of the Pandas library. In addition, it gave me a brief overview of some great sources for collecting data--as opposed to using a combination of the requests and BeautifulSoup modules (which works but definitely requires a good understanding of HTML) for data scraping. The one thing that really stood out for me is that this level covered the "melt" function--I've used this function so many times at work to get data (from, e.g., .json, .csv, files) into the right format to upsert into a database, as files often have multi indexing. Knowing this function beforehand would've saved a lot of googling and this course definitely gives you extensive opportunities to practice it to the point that it becomes second nature.
Level 9 covered data viz--an area that I will be focusing a lot on in work from Jan 2021 to Mar 2021. During grad school, I've used matplotlib extensively but I have leaned towards Excel plotting on the job as sometimes it gets hard to make things look nice effortlessly. After taking this course, I'm officially reverting back to Python because Plotly--this course is my first time experiencing with it--has solved many of these issues.
Level 10 material was definitely one of the key reasons I took this course. After taking the course, I'm actually happy that it focused more on data cleaning and bootstrapping, as these are areas not really focused on much (from what I've seen in other machine learning type courses) but equally important to effective machine learning model development. I do hope that a more machine learning focused Python course comes out in the future, as QuantNet always seems to deliver some of the very best online programming courses!
All in all, I really enjoyed the Python course!