Designing Larger Python Programs for Data Science

Genevieve M. Lipp, Nick Eubank, Kyle Bradbury, and Andrew D. Hilton

Modern programs are complicated structures, with hundreds to thousands of lines of code, but how do you efficiently move from smaller programs to more robust, complicated programs? How do data scientists simulate the randomness of real world problems in their programs? What techniques and best practices can you leverage to design pieces of software that can efficiently handle large amounts of data? In this course from Duke University, Python users will learn about how to create larger, multi-functional programs that can handle more complex tasks.

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Syllabus

Introduction to Larger Programs

This module, you’ll learn how to apply the concepts you’ve learned previously to analyze larger programs. Additionally, we’ll go through the process of program decomposition, to break up a complicated program into smaller steps that we can solve easier. After all of those pieces, we’ll put our pieces together in a programming assignment that combines a lot of the smaller programs we’ve created throughout the module.

Monte Carlo Methods and Introduction to the Poker Project

This Module, you’ll learn about Monte Carlo methods, which are a common technique we use to simulate a lot of possible outcomes. We’ll also introduce you to the Poker Project that you’ll be working on for the rest of the course. In this module we’ll focus on how we can write code to simulate different possible outcomes for a hand of poker, and the individual programming problems we’ll need to solve to make a complete poker simulation. You’ll 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.

Writing Test Cases and Identifying Sources of Error

This module, you will learn about writing test cases and debugging in a Python program, and apply it to your poker project! Additionally we’ll move forward to the logical evaluation part of the poker project, where you’ll 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’ll write these individual parts of the program and get feedback on those, before we move on to the next unit, where we’ll synthesize all of these pieces into a complete poker hand simulation.

Integrating Larger Programs

This module, we’ll integrate all of the individual sections of Python code that we’ve 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’ll 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’ll give feedback on your final poker project, and then we’ll ask you to do a short reflection on your poker project and the experience you had creating a larger program from its discrete components.

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Designing Larger Python Programs for Data Science with these activities:

Review: Practical Programming

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Review this book to strengthen your fundamental understanding of the Python programming language.

View Practical Programming: An Introduction to... on Amazon

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Read the first three chapters of the book.
Complete the practice exercises at the end of each chapter.
Summarize the key concepts covered in each chapter.

Python Monte Carlo Simulation Tutorials

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Follow online tutorials to learn how to implement Monte Carlo simulations in Python.

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Search for online tutorials on Monte Carlo simulations in Python.
Select a tutorial that is appropriate for your skill level.
Follow the steps in the tutorial to create your own Monte Carlo simulation.

Pandas Practice Drills

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Complete these practice exercises to improve your proficiency with data manipulation using Pandas.

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Import the Pandas library into a Python script.
Create a DataFrame from a CSV file.
Perform basic data manipulation tasks, such as filtering, sorting, and aggregating data.
Plot data using Pandas.

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Study Group: Monte Carlo Methods

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Join a study group to discuss Monte Carlo methods and their applications in data science.

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Find a group of peers who are interested in learning about Monte Carlo methods.
Schedule regular meetings to discuss the topic.
Prepare presentations or lead discussions on different aspects of Monte Carlo methods.

Contribute to a Python Monte Carlo Simulation Library

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Contribute to an open-source Python library for Monte Carlo simulations to gain practical experience and improve your understanding of the topic.

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Find an open-source Python library for Monte Carlo simulations.
Identify an area where you can contribute to the library.
Submit a pull request with your contributions.

Probability Calculator Project

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Develop a Python program to calculate probabilities for different scenarios, such as rolling dice or flipping coins, to reinforce your understanding of probability concepts.

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Design the overall structure of the program.
Write functions to calculate probabilities for different scenarios.
Integrate the functions into a complete program.
Test the program and refine the code.

Poker Simulation Project

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Develop a Python program that simulates a poker game and calculate the probability of winning hands.

Browse courses on Monte Carlo Simulation

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Design the overall structure of the program.
Write functions to simulate dealing cards, evaluating hands, and calculating probabilities.
Integrate the functions into a complete program.
Test the program and refine the code.