Designing Larger Python Programs for Data Science

О курсе

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. We don't recommend that this be the first Python course you take, as we'll be covering a decent amount of specific programming syntax. However, if you hold a prerequisite knowledge of basic algebra, Python programming, and the Pandas library, you should be able to complete the material in this course. In the first module, we’ll discuss top-down design for larger programs, including the programming syntax and techniques that are useful to stitch together larger programs. Then in the following modules, we’ll transition into discussing Monte Carlo simulations and introduce you to the Poker project, the larger program you’ll create by the end of the course. By the end of this course, you should be able to decompose a programming problem into manageable pieces, explain the basics of Monte Carlo Methods, and efficiently integrate smaller pieces of code into a larger complete program. This will prepare you to take the next step in your data scientist journey, creating complex programs that can more creatively simulate real-world problems.

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Genevieve M. Lipp

Assistant Professor of the Practice

Nick Eubank

Assistant Research Professor

Kyle Bradbury

Assistant Research Professor

Andrew D. Hilton

Associate Professor of the Practice