learn Python Programming Masterclass

Hello, is learn Python Programming Masterclass on udemy worth it and what will i be able to do after the completion of the course?

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Start with Profiling: Before optimizing, identify the bottlenecks. "Profiling. Don't even consider trying to optimise the performance of your code until you've got data on which stuff is slow."
Use Profiling Tools: Tools like Py-Spy, cProfile, and vmprof can help. "We made a tool that uses a genetic algorithm to do automated code refactoring... we used PyPy as pretty much a drop-in solution and did a ton of profiling with cProfile and vmprof."

Vectorization: Use libraries like NumPy, Pandas, or Polars to perform operations on entire arrays or dataframes at once, avoiding slow Python loops. "Are you vectorizing as much as possible (using pandas - or polars which is even faster)?"
Avoid Loops: "Avoid loops whenever possible, especially for loops. It's way faster to calculate the whole matrix at once than to loop through each row and column."
Efficient Data Structures: Use appropriate data structures like hash tables (dictionaries) for O(1) lookups instead of searching through lists. "Or do you often need to find other data to combine it? How's that lookup done—searching through lists one by one, or could you use hash tables (maps, dictionaries) for O(1) lookup?"
Pre-allocate Memory: For large datasets, pre-allocating memory can prevent slow append operations. "For large datasets, pre-allocating memory is key. Don't create variables inside loops, and avoid appending data blocks – both are slow."

Fast Data Handling: Consider Polars for significantly faster data frame operations compared to Pandas. "When I switched from Pandas to Polars, my CPU usage went from around 20% to 80%, with a big speed increase."
JIT Compilers: Use Numba to compile Python code to machine code for speedups. "Try out numba: You basically slap JIT decorators on your hot functions saying 'pretty please make it fast'."
Alternative Python Interpreters: PyPy can offer significant speedups for pure Python code. "PyPy gave us roughly an order of magnitude speedup over CPython."
Serialization: Optimize data serialization, especially in web services. "Switching serialization in context of a web service could be a cool topic."

Async/Await: For I/O-bound tasks like network calls, asyncio can be a game-changer. "For API development utilizing async has to be at the top for me. Switching from synchronous to asynchronous has been a game changer for me..."
Multiprocessing: For CPU-bound tasks, use the multiprocessing module to bypass the Global Interpreter Lock (GIL). "I was running simulations in Python and used the new multiprocessing abilities to bring the time down."

Memoization: Use functools.lru_cache to cache results of expensive function calls. "Try memoization, e.g. slapping lru_cache on functions that dont have side effects."
Efficient Imports and Lookups: "Using from module import member to bring an external module’s member into your module’s globals is slightly more efficient than doing module.member lookups via sys.modules, especially in tight loops."
Generator Expressions: Use generators to process data iteratively without loading everything into memory. "turning some of your calculations into a generator instead of a list if they're using a lot of memory"

C/C++/Rust Integration: For critical performance-sensitive parts, consider rewriting them in a faster language and integrating with Python using tools like Cython or ctypes. "You can write time heavy functions in C or another compiled language."

Readability First: Optimize for readability before performance, unless performance is a proven bottleneck. "To me the number one Python code optimization is to make sure it's readable."
Structured Data: Utilize structured data types and libraries that are designed for performance. ["Numpy arrays, Pandas DataFrames,