Description
Competitive programming has grown exponentially in the last decade. Millions of students, teachers, professionals solve problems including complex optimisations every minute. With the influx of programming languages, developers have a wide range of tools to choose from and use them to solve competitive challenges. Some of the popular platforms include Codeforces, Codechef, Hackerrank, Hackerearth, Topcoder etc.
In this talk we are going to use the dataset of codes scraped from Codeforces from a variety of challenges. These include programs written by top rated coders across the world to the newbies. The platform allows you to code in 26 different languages which obviously include popular programming languages like C, C++, Java, Javascript, PHP, Python etc. There are a very wide range of challenges in competitive programming like Sorting, Binary Search, Trees, Graphs, Dynamic Programming to name a few. The talk will cover the visualization of the dataset among broad classifications of how each programming language performs in these classifications. How efficient are programming languages across classifications in terms of time and memory and several others? The talk would also specifically cover the ease of using Python to solve different classes of challenges in competitive programming and the usage of Python over time.
Major takeaways :
- ABC of web scraping and best practices.
- Optimizing web scraping to scale.
- No-SQL databases for storing unstructured data
- How does Python as a language fare in competitive programming in terms of
- efficiency and popularity?
- Can I pursue competitive programming using Python ONLY?
- An analysis of popular programming languages used for solving challenges.