How to Self Study Coding for Computational Neuroscience

That 6/e Kandel's book in the background of the vid has sections all throughout about computational neuroscience. Older editions of this book didn't have them so much. It's a great time to be a brain nerd! Just to back up what Charlotte said, Python is not too hard to learn if you have already gone through a course in Java or C/C++. My undergrad was also not in CS, but I am getting an MS in data science now. It is doable, guys. I say that to my fellow cell/molecular biology undergrads especially. My recommendation is to beef up your math courses (at least up to vector calculus, linear algebra, and statistics) and squeeze in a year of basic programming on the way to your bachelor's. You need to be mathematically literate to understand the models she mentions.

Some tips from someone who started out very similar to Charlotte and is now a full-time programmer: 1. don't be afraid to move away from tutorials and leaning resources and pursue your own projects - the motivation to build something you want and are interested in will increase your learning speed immensely. Return to your course when you need structure and find that you can no longer get a learning experience from your personal project. 2. stick to typing your variables whenever you can - Python is dynamically typed, which can be good for productivity but will bite you as your project grows - types are a another thing on all the things to learn in programming, and one that is easily dismissed at the beginning, but certainly one of the skills that pays off. The sooner you learn it, the better your other programming skills will scale. 3, Google the heck out of it. This is one reason why personal projects are so good for learning, because there is no externally enforced structure, no one place to go to get all the information - as with any meaningful project you want to build in the future. The information is all over the Internet, and Google is the place to find it. The ability to Google can be applied to many fields, but in my opinion is nowhere as useful as in programming - learn to phrase your questions so that Google understands them - learn how to quickly identify a useful source and eliminate the bad ones. 4 Once you've mastered the first step, use git! (Or any other version control, but really it's always git) RReiterating over your code will become familiar as you revisit your projects or rebuild from scratch as you've progressed in your skills and want to improve. properly implement or just play around- git not only gives you the tools to undo a mistake, but also to create different versions of your code, simplifies reusability and makes it easy to share your code between different machines - Git is also a great tool to see your accomplishments - almost all remot repos have a timeline feature and show what and when you committed a change, update or build a new project. Also, when you're ready to apply for jobs, this is a way to show your skills - especially if you're from a different field like physics, mathematics, biology, or another - you won't have a computer science degree, but your public repos can be just as valuable or even more! 4.2 Bonus tip: If you apply somewhere, and have put some projects on your resume via public repos, and the interviewer doesn't understand what it is, don't take the job - at least if you're serious about programming. An experienced programmer will learn more about your skills from a few lines of code than from 20 degrees you've earned. For this reason, he will immediately look at your github/gitlab presence when reviewing your resume. If there is no one in the company you are applying to who thinks this way, then they don't understand what you do and will never appreciate your work.

It's like I am jumping into the future. Joking aside I would have nominated you for Nobel prize for massive postitve influence for science dissemination, self development, mind improvement, self wellbeing improvement.

R is actually the first ever programming language I tried & learned. It is the easiest if you follow a defined path & that is to use the libraries of Tidyverse & Ggplot2. The magic ingredient here is the book R & Data science, let me be honest it is the best & most intuitive programming book ever

Thank you so much for making this beautiful in-depth tutorial. Seeing someone so passionate about studies is inspiring. <3

I did 100 days of code because of your recommendation, and it was great! Thanks so much!

Hey Charlotte, I have been following you for a few weeks. I usually don't comment on youtube videos. But I should say, madam, what you do is really useful and I am really thankful for that.  Triple Gem Bless you.

Perfect timing with this video! How do you always do that?!?! :)  Second the recommendation for 100 Days of Code, I'm on "Day" 35 currently (57 days later lol) , and it's been so rewarding to do a project, get immediate feedback, and then compare what I wrote to the teacher's solution. After watching this video I've decided to skip most of the web development section of the class for now and go straight to the more advanced data analysis material that is so much more interesting to me. If I miss anything important I can always go back and learn it ad hoc.  Thanks for confirming my intuition about that, it's ridiculously easy to get lost in the sheer number of topics to learn!

One of the most informative videos that I have ever seen in computational neuroscience

thanks for the video! pretty insightful

Super helpful, thank you!

This content is so far beyond my comprehension that I just have to sub to this channel and learn more! 🧠

Thank you so much for the video I’m interested in neuropsychology and try to learn programming

I already happen to follow my favorite researcher/youtuber on twitter ;) excellent video as usual!

You are amazing, Charlotte! All your videos are extremely helpful. I can't thank you enough for what you do but please keep going!❤

To be fair, Compsci is taught with an emphasis on algorithms. Companies for the last few decades also have tested a lot for that. Consequently there are a lot of resources for that kind of learning. Good video so far, thanks 🙏

Beautiful video! I am a PhD student, and I can tell that you have a fantastic analytical way of breaking down the learning process into tractable blocks! Keep it up!

I'm in love with 16:14 rain effect :D dramatic as I am

Hello, I loved this video. I’m a postdoc in neurobiology and wondered how accessible you think computational neuroscience is to someone who has no math nor physics but only biology background? Again, really helpful and well presented video!

Thank you! Fraza.