Designing Larger Python Programs for Data Science
This course is part of Programming for Python Data Science: Principles to Practice.
This course from Duke University teaches Python users how to create larger, multi-functional programs for complex data science tasks. You'll learn top-down design for program decomposition, Monte Carlo simulation techniques, and best practices for handling large datasets. The course covers planning and integrating discrete pieces of Python code into more functional and complex programs. By the end, you'll be able to decompose programming problems, explain Monte Carlo methods, and efficiently build larger programs from smaller components.
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What you'll learn
Learn how to plan program decomposition using top down design
Understand how to integrate discrete pieces of Python code into larger, more complex programs
Explain the basics of Monte Carlo Methods and their applications in data science
Develop skills in writing test cases and identifying sources of error in larger programs
Gain practical experience in building a poker simulation program from discrete components
Learn to efficiently handle and analyze large amounts of data in Python programs
Skills you'll gain
This course includes:
29 Minutes PreRecorded video
1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course teaches Python users how to create larger, multi-functional programs for complex data science tasks. It covers top-down design for program decomposition, Monte Carlo simulation techniques, and best practices for handling large datasets. Students learn to plan and integrate discrete pieces of Python code into more functional and complex programs. The curriculum includes program decomposition, Monte Carlo methods, test case writing, and debugging techniques. A poker simulation project serves as a practical application of these concepts throughout the course.
Introduction to Larger Programs
Module 1 · 13 Hours to complete
Monte Carlo Methods and Introduction to the Poker Project
Module 2 · 9 Hours to complete
Writing Test Cases and Identifying Sources of Error
Module 3 · 13 Hours to complete
Integrating Larger Programs
Module 4 · 6 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Programming for Python Data Science: Principles to Practice
Payment options
Financial Aid
Instructors
Associate Professor of the Practice
Andrew Hilton is an Associate Professor of the Practice in the Department of Electrical and Computer Engineering at Duke University's Pratt School of Engineering, where he has been teaching since 2012. Before joining Duke, he worked as an advisory engineer at IBM. One of the key courses he teaches is ECE 551, an intensive introduction to programming designed to equip graduate students with no prior experience to master programming and tackle advanced courses. In 2015, Professor Hilton received the Klein Family Distinguished Teaching Award for his excellence in teaching. He holds a Ph.D. in Computer Science from the University of Pennsylvania.
Assistant Professor of the Practice at Duke University
Dr. Genevieve M. Lipp is an Assistant Professor of the Practice in the Electrical and Computer Engineering and Mechanical Engineering and Materials Science departments at Duke University. She teaches a variety of courses, including programming in C++, dynamics, control systems, and robotics. Dr. Lipp is passionate about integrating technology into education to enhance learning outcomes and has previously worked in the Center for Instructional Technology at Duke. She holds a Ph.D. in mechanical engineering, focusing on nonlinear dynamics, as well as a B.S.E. in mechanical engineering and a B.A. in German, both from Duke University. In addition to her teaching responsibilities, she serves as the Director of the Duke Engineering First Year Computing program, where she focuses on improving computing education within the engineering curriculum and fostering students' self-efficacy in their studies.
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