Pain is necessary for growth, nothing worth doing is easy.
Big o notation allows devs to compare algorithms based on complexity in regard to time and space?
Immutable/mutable datatypes are important for semantics, readability, and debugging.
The main challenges faced by beginners when learning Python are “having to forge their own path towards a solution”, “being pushed emotionally”, “being constantly flushed outside their comfort zone”. Two strategies I suggest are to take a step back when the obstacle seems unmoveable. I’ve noticed things become a tad bit easier when I take a break and let my mind catch its breath for a second. Another thing that I would suggest is to collaborate with teammates. Your classmates are different puzzle pieces to the same puzzle, what you don’t know, someone else will (and reciprical).
Time and Space complexity correlates to the complexity of the problem or algortihm being run. “O(1) algorithms will execute in the same time regardless of the size of the input data set”, “O(N) algorithms grow linearly and are worst-case scenario”, “0(N^2) algorithms are directly proportional to the square of the size of the input data set.” and “O(2^N) algorithms double in growth with each addition to the input data set”
Immutable data types cannot change. If a variable is a string it cannot change to an integer whereas if it’s mutable it might change to an integer, boolean etc.
What are some other strategies I can work on in order to maintain a strong, persistent, motivated mindset during 401?
Need big o notation explained to me in person…
How does a dev identify if a data type is mutable/immutable?