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Designing Larger Python Programs for Data Science

Learn to create complex Python programs for data science. Master program decomposition, Monte Carlo methods, and large-scale data handling.

Learn to create complex Python programs for data science. Master program decomposition, Monte Carlo methods, and large-scale data handling.

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.

Instructors:

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Designing Larger Python Programs for Data Science

This course includes

41 Hours

Of Self-paced video lessons

Beginner Level

Completion Certificate

awarded on course completion

2,436

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

Payment options

Financial Aid

Instructors

Genevieve M. Lipp
Genevieve M. Lipp

4.7 rating

1,911 Reviews

2,65,562 Students

11 Courses

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.

Nick Eubank
Nick Eubank

3.7 rating

12 Reviews

19,786 Students

5 Courses

Assistant Research Professor at Duke University

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.

Designing Larger Python Programs for Data Science

This course includes

41 Hours

Of Self-paced video lessons

Beginner Level

Completion Certificate

awarded on course completion

2,436

Testimonials

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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.