Top 5 Free Online Courses for Computer Science Professionals on SWAYAM

July 4, 2024 SAURAV PANDEY

1. C Programming and Assembly Language by Professor Janakiraman from IIT Madras

[{"selector":"#anim-f0965a1b-be6b-4242-809e-1aeb51ed981f","keyframes":{"opacity":[0,1]},"delay":0,"duration":3000,"easing":"cubic-bezier(0.4, 0.4, 0.0, 1)","fill":"both"}] This course for computer and electrical engineering students covers microprocessors, C programming, inline assembly, compiling C to assembly, and C++ with special functions. Source: Canva

2. Demystifying Networking by Professor Sridhar Iyer, IIT Bombay

[{"selector":"#anim-aae00c0c-f52d-4796-8471-1270b270b357","keyframes":{"opacity":[0,1]},"delay":0,"duration":3000,"easing":"cubic-bezier(0.4, 0.4, 0.0, 1)","fill":"both"}] This course introduces networking concepts and technologies for beginners, covering security, troubleshooting, the application layer, routing, and more. Source: Canva

3. Big Data Computing by Prof. Rajiv Misra from IIT Patna

[{"selector":"#anim-e1cda6c6-9138-43c8-a30f-64b1c684034c","keyframes":{"opacity":[0,1]},"delay":0,"duration":3000,"easing":"cubic-bezier(0.4, 0.4, 0.0, 1)","fill":"both"}] The course covers Computer Architecture, Data Structures, Database Management, Operating Systems, and Big Data technologies and applications. Source: Canva

4. Python for Data Science by Professor Ragunathan Rengasamy from IIT Madras

[{"selector":"#anim-4b0e5ffa-f5ea-4ba4-a15c-58a649d5264d","keyframes":{"opacity":[0,1]},"delay":0,"duration":3000,"easing":"cubic-bezier(0.4, 0.4, 0.0, 1)","fill":"both"}] The course covers basic data science algorithms, including Spyder, sequence data types, and data frames, culminating in a case study on income classification and car price prediction. Source: Canva

5. Introduction to Machine Learning by Prof. Sudeshna Sarkar from IIT Kharagpur

[{"selector":"#anim-e58abe05-8550-4878-9e1d-69b6cdedd3e6","keyframes":{"opacity":[0,1]},"delay":0,"duration":3000,"easing":"cubic-bezier(0.4, 0.4, 0.0, 1)","fill":"both"}] The course covers Deep Learning basics including clustering algorithms, Machine Learning concepts like Linear Regression, overfitting, Decision Trees, Logistic Regression, and Neural Networks. Source: Canva