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Designing Larger Python Programs for Data Science
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Designing Larger Python Programs for Data Science

This course is part of Programming for Python Data Science: Principles to Practice.

Course Cost

Free course

Beginner

Skill Level

41 Hours

Self-paced lessons

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.

Genevieve M. Lipp

Genevieve M. Lipp

Nick Eubank

Nick Eubank

English

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English

Genevieve M. Lipp

Genevieve M. Lipp

Nick Eubank

Nick Eubank

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

Program Decomposition
Monte Carlo Methods
Python Programming
Software Development
Data Science
Pandas
Poker Simulation
Test Case Writing

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

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Genevieve M. Lipp
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.
Nick Eubank
Dr. Nick Eubank is an Assistant Research Professor at the Duke Social Science Research Institute (SSRI), where he focuses on political accountability. His research examines two key aspects: the factors that influence citizens' ability to hold politicians accountable and the strategies employed by political elites to undermine accountability mechanisms. Dr. Eubank's work encompasses various topics, including gerrymandering, social networks, election administration, and issues related to race and incarceration. He holds a Ph.D. from Stanford University and is also the Associate Director of the Rhodes Information Initiative at Duke, where he contributes to advancing research and education in data science and its applications in social sciences. His interdisciplinary approach aims to enhance understanding of democratic processes and improve governance through informed citizen engagement.
Kyle Bradbury
Dr. Kyle Bradbury is an Assistant Research Professor in the Department of Electrical and Computer Engineering at Duke University, where he also serves as the Managing Director of the Energy Data Analytics Lab. His research focuses on applying machine learning and statistical modeling techniques to address energy-related challenges, particularly in modeling the reliability and cost trade-offs of energy storage systems for integrating renewable sources like wind and solar power into the grid. Dr. Bradbury completed his Ph.D. at Duke University, specializing in energy systems modeling, and holds a Master’s degree in Electrical Engineering from Duke, where he concentrated on statistical signal processing and machine learning, as well as a Bachelor’s degree in Electrical Engineering from Tufts University. His professional experience includes roles at ISO New England, MIT Lincoln Laboratories, and Dominion Energy. Dr. Bradbury is also involved in teaching various courses related to data science and machine learning at Duke, contributing to the education of future engineers and researchers in the field.
Andrew D. Hilton
Andrew Hilton is an Associate Professor of the Practice in the Department of Electrical and Computer Engineering in the Pratt School of Engineering at Duke University. He has taught at Duke since 2012, and prior to that he was an advisory engineer at IBM. Among the courses Professor Hilton teaches at Duke is ECE 551, an intensive introduction to programming course that successfully prepares graduate students with no programming experience to learn programming and go on to complete more advanced programming courses. In recognition for excellence in teaching in the Pratt School of Engineering at Duke, Professor Hilton received the Klein Family Distinguished Teaching Award in 2015. Professor Hilton holds a PhD in Computer Science from the University of Pennsylvania.
Designing Larger Python Programs for Data Science

Beginner

Skill Level

41 Hours

Self-paced lessons

Course Cost

Free course

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.