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This Module, you鈥檒l learn about Monte Carlo methods, which are a common technique we use to simulate a lot of possible outcomes. We鈥檒l also introduce you to the Poker Project that you鈥檒l be working on for the rest of the course. In this module we鈥檒l focus on how we can write code to simulate different possible outcomes for a hand of poker, and the individual programming problems we鈥檒l need to solve to make a complete poker simulation. You鈥檒l create some of these smaller solutions in this module, and receive feedback on these individual pieces before we move onto synthesizing some of these parts together in the next module.

This module, you will learn about writing test cases and debugging in a Python program, and apply it to your poker project! Additionally we鈥檒l move forward to the logical evaluation part of the poker project, where you鈥檒l write the code that will allow your program to decide what a winning hand would be, and use some data science techniques to help clean up the data generated by Monte Carlo methods. Similarly to the last unit, you鈥檒l write these individual parts of the program and get feedback on those, before we move on to the next unit, where we鈥檒l synthesize all of these pieces into a complete poker hand simulation.

This module, we鈥檒l integrate all of the individual sections of Python code that we鈥檝e written throughout the course into one larger program. This will likely require a bit of troubleshooting and forethought to get all of your previous bits of code working, but you will leverage the test cases and skills you learned in the previous module to accomplish this. We鈥檒l also go over object references, a way that we can directly reference a piece of memory, to efficiently update the information that the various parts of your program will be using. After all of this, we鈥檒l give feedback on your final poker project, and then we鈥檒l ask you to do a short reflection on your poker project and the experience you had creating a larger program from its discrete components.