Andy Nguyen
Member
The following are testimonials by students who recently completed the "Python for finance with intro to data science" online course. If you have questions, ask here and they are more than willing to answer.
-
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
- Thread starter Andy Nguyen
- Start date
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."
"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."
Similar threads
