Computational Complexity Theoretical Foundation on How Long Will Program Run

Algorithmic Complexity-Teaching Forward What I Learned

Kautilya Katariya

Algorithms Data-Structures Education Programming Python 3

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Online teaching materials enable learning programming at any age. Kautilya, a 7-year-old programmer who has learned his way through multiple courses in Python and Artificial Intelligence, shares his takeaways and examples so that we can learn what he has learned about Computational Complexity.
In this talk, he focuses on the time complexity in computational/algorithmic complexity. As he lays out the theoretical foundations of how we formally measure how fast a program or algorithm runs, he teaches us to analyse the amount of time our program creation choices have on how much time program will take to finish.

In computer science, the time complexity is the computational complexity that describes the time it takes to run an algorithm. With strong theoretical computer science foundation, he walks us through Big-O, Big-θ, Big-Ω notations for complexity, helps us make sense of it and shows with examples of searching (linear, binary, exponential) and sorting (merge, insertion, selection) how our choices of design of algorithm impact what we experience as time to execute the program.

Learn foundations and draw inspiration for learning from the Guinness World record holder on ‘Youngest Computer Programmer’ at the age of 6.

Type: Talk (30 mins); Python level: Beginner; Domain level: Beginner

Kautilya Katariya


I am 7 yr old.Self-taught,Computing Explorer.Guinness World Record Holder for Youngest Computer programmer at the age of 6.I am Microsoft MTA & IBM Professional AI certified ,Watson,Artificial Intelligence and Algorithm learner.
In April 2021, I was invited as a Speaker for Geekly global summit on Python for AI and ML, presented EDA and participated in QandA session to answer some of audience questions.
I am learning from YouTube, Free education material on Computing, Artificial Intelligence, coding from MIT, Stanford University, IBM, edX resources.

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