Designing Larger Python Programs for Data Science from Coursera

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.

What's inside

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.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Develops basic programming syntax and techniques, which are core skills for data science

Explores Monte Carlo methods, which are standard in industry

Builds a strong foundation for beginners in data science

Taught by Genevieve M. Lipp, Nick Eubank, Kyle Bradbury, Andrew D. Hilton, who are recognized for their work in data science

Examines object references, which is highly relevant to data science

Explicitly requires learners to have a prerequisite knowledge of basic algebra, Python programming, and the Pandas library

Reviews summary

Practical python program design

According to students, this course is a highly practical and transformative experience for those looking to build larger, more robust Python programs for data science. Learners particularly praise the hands-on Poker project, which many found to be a rewarding and effective way to solidify concepts. The course excels in teaching program decomposition, testing and debugging strategies, and the application of Monte Carlo methods. While generally well-received, a few students noted that the section on object references could be challenging and that a solid grasp of intermediate Python and Pandas is essential, though the course's content is widely considered invaluable for scaling programming skills.

The Poker project is practical but might feel niche for some data scientists.

"I was hoping for more advanced data science techniques. This felt more like a software engineering course disguised with a data science label."

"The focus on poker simulation was a bit niche, and I struggled to see its direct applicability to my general data analysis tasks."

"I sometimes wished for more theoretical depth on Monte Carlo simulations, but it provided enough for practical application."

Best suited for learners with a solid foundation in Python and Pandas.

"I don't recommend this as a first Python course. You need prerequisite knowledge of basic algebra, Python programming, and the Pandas library."

"Highly recommend if you have a solid grasp of Python and Pandas, otherwise, you might find it too challenging."

"The course assumes a higher level of prior programming experience than stated. This is definitely not for beginners."

Strong emphasis on writing test cases and effective debugging.

"The testing module was very useful. The debugging and testing modules were eye-opening for me."

"The emphasis on decomposition and testing is invaluable. Test-driven development was a game-changer for my workflow."

"I now know how to write test cases and identify sources of error in my Python programs efficiently."

Teaches essential skills for structuring larger Python applications.

"This course completely changed how I approach larger Python projects. The emphasis on decomposition and testing is invaluable."

"I learned so much about structuring larger applications. The decomposition module was excellent and practical for breaking down complicated programs."

"It helped me efficiently move from smaller programs to more robust, complicated programs, preparing me to create complex programs."

The Poker project is a highlight for practical skill development.

"The Monte Carlo methods and the Poker project were incredibly practical. I particularly enjoyed how the course broke down the process of program decomposition."

"The hands-on Poker project really solidified the concepts. It was a lot of work, but highly rewarding and helped me integrate pieces into a larger program."

"The Poker project provided a realistic scenario for applying all the concepts. It pushed me to think like a professional developer."

Some advanced topics like object references can be difficult to grasp.

"Some parts felt a bit rushed, especially the object references. I had to consult external resources for a deeper understanding."

"The material on object references was difficult to grasp without more foundation, and the jump in complexity was significant."

"The object references and some of the more advanced Python concepts were a hurdle; the course assumed a higher level of prior programming experience."

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

Show steps

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

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

Show steps

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

Show steps

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

Browse courses on Monte Carlo Simulation

Show steps

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

Show steps

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

Browse courses on Pandas

Show steps

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.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Study Group: Monte Carlo Methods

Show steps

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

Browse courses on Monte Carlo Simulation

Show steps

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

Show steps

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

Browse courses on Monte Carlo Simulation

Show steps

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

Show steps

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

Browse courses on Probability

Show steps

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

Show steps

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

Browse courses on Monte Carlo Simulation

Show steps

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.

Career center

Learners who complete Designing Larger Python Programs for Data Science will develop knowledge and skills that may be useful to these careers:

Reading list

We haven't picked any books for this reading list yet.

Designing Larger Python Programs for Data Science

Here's a deal for you

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Practical python program design

Activities

Career center

Reading list

Share

Similar courses