• C++ Programming for Financial Engineering
    Highly recommended by thousands of MFE students. Covers essential C++ topics with applications to financial engineering. Learn more Join!
    Python for Finance with Intro to Data Science
    Gain practical understanding of Python to read, understand, and write professional Python code for your first day on the job. Learn more Join!
    An Intuition-Based Options Primer for FE
    Ideal for entry level positions interviews and graduate studies, specializing in options trading arbitrage and options valuation models. Learn more Join!

Python course testimonial

Here's my experience:

"It has been an absolute pleasure struggling through this course.

I am a beginner in terms of programming and after finishing this course, I feel very confidence in terms of my programming ability: I can read through a complex problem and design a solution, write maintainable codes, run Monte Carlo simulations on practical financial problems, slicing and dicing massive datasets and build data visualization tools like a pro.

The coursework is rigorous. There is a lot of exercises that students have to go through each level. I initially planned to dedicate 1 hour a day on the course and I must have spent at least 3-4 every single day to work through the course load. Writing codes is one thing but debugging and optimizing them are others time-consuming issues that I have learned a lot going through this experience. Get ready to work!

The course material is very hands on. It walks students through steps by steps building functions each level from very simple to complex, culminating in a very extensive and complex case study in level 7 of modeling a simplified Asset Backed Security. Even though the steps are well though out and implemented in the exercises, they left room to challenge students to think about ways to design, implement and optimize their approach. This is also part of the rigor of this course. The exercises are not easy! A lot of "Python for Finance" courses out there just feed you codes to run. This will not be one of them.

Many sophisticated topics were cover: decorator, multi-processing, data science packages etc. Best practices are also drilled in the lecture and carried out through the exercises. The video lectures are designed in bite-size and to-the-point, long enough to cover the topics but not too long that students are left drooling.

TA (APalley in my case) was excellent in terms of guiding me through this course. His hints given during the homework assignments left enough room for critical thinking on the student part while not giving the answer away. This is important as it helps you learn how to approach and breakdown a complex problems and identify possible pitfalls later on. The homework's comments are very extensive. I would recommend students going through problematic areas to re-optimize their codes as the technical debt only builds as you progress further through the course.

In the end, I experienced tremendous growth going through this course and would definitely recommend it for anyone with an interest in Python and Data Science. Non-STEM majors (as I am) might have a steeper learning curve compare to a Comp Sci major but you likely get a lot more return on investment out of this course.

Looking forward to more advanced Python courses to be built on top of this."
Let me start by stating the fact that I have actually gone from ZERO to hero. This course is intense, at least for me it was. I took a college course in Python few months before I started this course; however, everything I studied in that college course, Quantnet's course covers it in level 1. Yes level one is long, and intense, and no it doesn't get easier onward, it actually gets tougher by the day, but some of the chapter are not as long as level 1. After level 7, the course switches to more data science concentration.

All in all, I have learned tons, I learned how to think strategically and design my code in my own way (it takes a lot of time and the struggle is real!) but, once you go through that, things become easier, you start coding faster and faster; I don't know about you, but to me, that feels amazing. I am pretty happy with my results on this course; and to say the least the TA @APalley is truly AMAZING. He is polite, smart, patient; no matter how many questions we ask him about a particular question, he would always find a way to explain it to you in a way that you would understand. I wanted to thank @Andy Nguyen for putting this course together, this has been a pleasant and fruitful journey to say the least.

By the way, I am not a CS major, nor I am an expert in Python; so if you are in the same boat; I would suggest getting ready for putting serious hours. The HW assignments will take time, and once you start working on the assignment it will be an obsession, time will fly without you even feeling (it's kind of fun I am not going to lie).

Now that I have gone through this very steep learning curve; I am looking forward for the next endeavor!
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!
Last edited:
@Andy Zhang Would you say the Python and intro C++ courses require about the same amount of time per level, or did you find one to be more intensive than the other? Thanks in advance!