UPDATE EXTRA/COURSES : ADDING SOME COOL EXTRA COURSES

[aayushsinha0706]

Calculus Applied! : Apply tools of single-variable calculus to create and analyze mathematical models used by real practitioners in social, life, and physical sciences.

A cool course by Harvard that students can take after taking Calculus 1abc, The course talks about application of calculus in different areas. For example : How economists model interaction of price and demand using rates of change, in a historical case of subway ridership;

Matrix Methods in ML : Linear algebra concepts are key for understanding and creating machine learning algorithms, especially as applied to deep learning and neural networks. This course reviews linear algebra with applications to probability and statistics and optimization–and above all a full explanation of deep learning.

This is an applied linear algebra course offered by none other than linear algebra start Professor Gilbert Strang, students can take it after taking 18.06 Linear algebra and gain new perspective of linear algebra or just prepare for Ng's ML class on coursera.

Design and Analysis of Algorithms - A very difficult class as to say by reviews. This is a math intensive algorithms class that students take after 6.006 or equivalent algorithm class (here core-theory) but a good course none the less, can be even done by students of ossu-math or students who have good math maturity.

Distributed Systems by MIT - Not a MOOC but a freely available online course by MIT. There are multiple software engineers who talk about why system design is a must learn for every software engineers. There are courses coming up on system designs and distributed systems by interview prep sites like algoexpert.io, so why not provide aspiring software engineers a good academic resource. The video lectures are freely available on YouTube and some devs also created a discord study group for further help in MIT 6.824. You can also read some reviews on course here.