A couple of python pedagogical paths I’m sure you might have found: Allen Downey at Olin college: Think series of python books are basically written for each course he needs to teach; and Peter Kazarinoff at Portland Community College, who also wrote a book and blog: problem solving with python. Kazarinoff and Downey are very much in the trenches an tuning their content dynamically. Not the equal of your Langtangen, but they are still with us.
Hi Chris. Regrettably, I have not heard of the educators you mentioned. This may because I have (so far) been looking into physics courses with integrated computations, whilst Downey and Kazarinoff seem to be teaching CS and Engineering respectively. That said, I am just getting started on a study of how computations are benefitting a wider range of fields, so your comment will without doubt be very useful to me. Thanks!
Thank you for your very informative videos! I am currently studying engineering in EPFL, Switzerland and will start a master in computational science next year hoping to be able to pick up the more advanced QM and stat mech knowledge lacking from my curriculum in order to specialise in computational physics. However I think working in industry later is probably a good choice in term of stability and earnings, how do you think the work available in industry for a computational scientist (coming from a basic science background with heavy mathematics) compare to what you can do in academia ? Do you think industry jobs are as fulfilling as an academia job for people enjoying the mathematical/more theoretical part. I imagine you've already asked yourself those question and hope to hear your opinion ! Keep up the great videos!
Hello! Cool to hear that you are thinking about transitioning to physics. Take your time with QM. My experience is that it needs to mature in your mind for a while. It is likely when you have been doing physics or engineering in uni that you will end up in some sort of data science or software development job. I think that the main difference is the depth and/or the length of the projects. I think you would work longer on the same thing and more thoroughly while employed at a university than if you are doing an analysis or building an app for a client (blunt example). If you like doing mathematics and pure research you will probably have more trouble finding work where you're able to do this outside of academia, but it is certainly not impossible. I am very prone to digging myself down into something very theoretical, but I have found all the work I have done for a profit-seeking entity both interesting and fulfilling. Having done physics emboldens you and makes you less scared of trying to understand something. Example: when I was working with the railway data that I mentioned in this video I was able to teach myself the backpropagation algorithm used in neural networks. Completely unnecessary, since the guys and gals that have built tensorflow already did it at least ten times better than me, but it really gave me something extra. Hope this answer was alright, sorry it was so late.
@@GregWintherArtist Great to hear you opinion! Thank you! I guess no industry job will ever forbid us to look for a deeper understanding of the problems and their solution on the side.
You are doing a unique video content as I see, so thank you and keep it up!
A couple of python pedagogical paths I’m sure you might have found: Allen Downey at Olin college: Think series of python books are basically written for each course he needs to teach; and Peter Kazarinoff at Portland Community College, who also wrote a book and blog: problem solving with python. Kazarinoff and Downey are very much in the trenches an tuning their content dynamically. Not the equal of your Langtangen, but they are still with us.
Hi Chris. Regrettably, I have not heard of the educators you mentioned. This may because I have (so far) been looking into physics courses with integrated computations, whilst Downey and Kazarinoff seem to be teaching CS and Engineering respectively. That said, I am just getting started on a study of how computations are benefitting a wider range of fields, so your comment will without doubt be very useful to me. Thanks!
Aged like a fine wine
Thank you, Mr. Winther, for helping us all understand what we study better. Good Luck
Thank you for your very informative videos!
I am currently studying engineering in EPFL, Switzerland and will start a master in computational science next year hoping to be able to pick up the more advanced QM and stat mech knowledge lacking from my curriculum in order to specialise in computational physics.
However I think working in industry later is probably a good choice in term of stability and earnings, how do you think the work available in industry for a computational scientist (coming from a basic science background with heavy mathematics) compare to what you can do in academia ? Do you think industry jobs are as fulfilling as an academia job for people enjoying the mathematical/more theoretical part. I imagine you've already asked yourself those question and hope to hear your opinion !
Keep up the great videos!
Hello! Cool to hear that you are thinking about transitioning to physics. Take your time with QM. My experience is that it needs to mature in your mind for a while. It is likely when you have been doing physics or engineering in uni that you will end up in some sort of data science or software development job. I think that the main difference is the depth and/or the length of the projects. I think you would work longer on the same thing and more thoroughly while employed at a university than if you are doing an analysis or building an app for a client (blunt example). If you like doing mathematics and pure research you will probably have more trouble finding work where you're able to do this outside of academia, but it is certainly not impossible. I am very prone to digging myself down into something very theoretical, but I have found all the work I have done for a profit-seeking entity both interesting and fulfilling. Having done physics emboldens you and makes you less scared of trying to understand something. Example: when I was working with the railway data that I mentioned in this video I was able to teach myself the backpropagation algorithm used in neural networks. Completely unnecessary, since the guys and gals that have built tensorflow already did it at least ten times better than me, but it really gave me something extra. Hope this answer was alright, sorry it was so late.
@@GregWintherArtist Great to hear you opinion! Thank you! I guess no industry job will ever forbid us to look for a deeper understanding of the problems and their solution on the side.