Python Programming Fundamentals

Python Programming Fundamentals

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

Instructors: Andrew D. Hilton

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4 modules

Gain insight into a topic and learn the fundamentals.

70 reviews

Beginner level

No prior experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

88%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

70 reviews

Beginner level

No prior experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

88%

Most learners liked this course

What you'll learn

Create algorithms and programs using a logical Seven Step framework.
Create useful test cases and efficiently debug Python code.
Apply Python basics (conditionals, loops, mathematical operators, data types) to build a Python program from scratch to solve a data science problem.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

12 assignments

Taught in English

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

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
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Earn a shareable career certificate

There are 4 modules in this course

This introductory course is designed for beginners and individuals with limited programming experience who want to embark on their software development or data science journey using Python. Throughout the course, learners will gain a solid understanding of algorithmic thinking, Python syntax, code testing, debugging techniques, and modular code development--essential skills for a successful career in software engineering, development, or data science.

By the end of this course, you will learn to: - Gain a stepwise approach to problem-solving using algorithms and programming logic. - Apply common functions, conditional statements, and loops to build Python scripts and programs. - Work with the VS Code programming environment to enhance coding proficiency. - Use testing and debugging strategies to ensure code reliability. - Perform logical and mathematical operations on datasets. In the final week of the course you will apply your new algorithm design and programming skills to a data analysis problem: analyzing heart rate data.

This module, you will learn best practices for developing code in any language by beginning with an algorithm: a stepwise approach to solving a problem. You’ll then apply the concepts by developing your own algorithms and properly identifying when to use specific data types in Python.

What's included

9 videos10 readings3 assignments

9 videosTotal 37 minutes

Introduction to Python Programming Fundamentals 4 minutes
Stepping Through an Algorithm4 minutes
Testing an Algorithm for a Numerical Sequence3 minutes
A Pattern of Squares5 minutes
Testing a Pattern of Squares2 minutes
Drawing a Rectangle4 minutes
Closest Point5 minutes
Generalizing Closest Point5 minutes
Everything is a Number6 minutes

10 readingsTotal 100 minutes

Plan First, Then Code10 minutes
Overview of the 7 Steps10 minutes
Algorithms10 minutes
Report a problem with the course10 minutes
Step 1: Work an Example Yourself10 minutes
Step 2: Write Down What You Just Did10 minutes
Step 3: Generalize Your Steps10 minutes
Step 4: Test Your Algorithm10 minutes
Intro to a Pattern of Squares10 minutes
Non-Numbers10 minutes

3 assignmentsTotal 75 minutes

Algorithms Quiz30 minutes
Algorithm Design30 minutes
Numbers and Types Quiz15 minutes

This Module, you will learn how to take the logical process of algorithm design and translate an algorithm into functional Python code. You will apply this by correctly identifying proper Python syntax for a given algorithm, and then subsequently by creating your own Python program for a given algorithm.

What's included

13 videos8 readings5 assignments4 programming assignments

13 videosTotal 58 minutes

Semantics: What Does Code Mean?2 minutes
Variables and Expressions4 minutes
Functions5 minutes
Printing4 minutes
Conditional Statements4 minutes
Loops5 minutes
Revisiting Intersection of Two Rectangles5 minutes
Planning isPrime4 minutes
Generalizing isPrime5 minutes
Translating isPrime to Code4 minutes
Tuples7 minutes
Why VS Code4 minutes
Intro Lab6 minutes

8 readingsTotal 95 minutes

First Four Steps Revisited20 minutes
Translating Algorithms to Code10 minutes
Ending Blocks with Pass10 minutes
Top Down Design and Composability10 minutes
Stars Example10 minutes
Introduction to VS Code15 minutes
How to Reset Lab Files10 minutes
Retirement Calculations10 minutes

5 assignmentsTotal 150 minutes

Reading Code Assignments30 minutes
Read Function 130 minutes
Reviewing the First four Steps of Algorithm Design30 minutes
Translating an Algorithm to Code30 minutes
Understanding Retirement30 minutes

4 programming assignmentsTotal 180 minutes

Intro To Coursera Lab Programming Assignments20 minutes
Max25 minutes
Triangle35 minutes
Retirement100 minutes

This module, you will learn different approaches to testing Python code, and methods for debugging code. You will apply this by conducting a code review, identifying opportunities to use asserts to debug code, and generating your own test cases.

What's included

6 videos8 readings3 assignments

6 videosTotal 27 minutes

Testing Means Finding Bugs2 minutes
Test-Driven Approaches7 minutes
Test-Driven Development2 minutes
Code Review3 minutes
Debugging: The Scientific Method6 minutes
Debugging: Hypotheses7 minutes

8 readingsTotal 85 minutes

Black Box Testing10 minutes
White Box Testing10 minutes
Creating Test Cases10 minutes
Asserts10 minutes
Code Review10 minutes
Introduction to Debugging Tools10 minutes
Principles and Tools for Debugging15 minutes
Debugging Python in VS Code10 minutes

3 assignmentsTotal 90 minutes

Tests Prime30 minutes
Tests countMostCommon30 minutes
Testing Code30 minutes

This module, you will learn how to filter and perform operations (logical and mathematical) across a given dataset, and how to create modular code that can be used in discrete pieces. You will apply this by writing your own functions to identify a list item with a given property, and writing a program to calculate a set of conclusions from a given dataset.

What's included

7 videos5 readings1 assignment4 programming assignments2 discussion prompts

7 videosTotal 29 minutes

Lists: References to Mutable Objects7 minutes
Lists: Iteration4 minutes
Lists: Indexing and Slicing4 minutes
Heart Rate Introduction3 minutes
Heart Rate Peaks3 minutes
Heart Rate Code4 minutes
Heart Rate isPeakAt4 minutes

5 readingsTotal 50 minutes

Default Arguments Revisited10 minutes
Heart Rate Example Introduction10 minutes
Moving Averages10 minutes
Conclusion Reading : Python Programming Fundamentals10 minutes
Share your learning experience10 minutes

1 assignmentTotal 30 minutes

Subsequence Test Cases30 minutes

4 programming assignmentsTotal 435 minutes

List Max45 minutes
Subsequence120 minutes
Heart Rate Peaks150 minutes
Heart Rate Moving Averages120 minutes

2 discussion promptsTotal 20 minutes

Heart Rate Peak Algorithm Reflection10 minutes
Code Similarities Reflection10 minutes

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Instructors

Instructor ratings

(27 ratings)

Andrew D. Hilton

Duke University

19 Courses1,148,357 learners

Offered by

Duke University

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Reviewed on Dec 9, 2025

Exactly what I was looking for, with good exercises, mostly useful feedback from AI evaluations.

Reviewed on Jul 24, 2026

Good class. Made me stretch to figure things out. I with the projects had a way of giving more help during the process but I figured it out. Thanks Duke!

Reviewed on Jan 25, 2025

I really liked the step by step approach described in this course to solve a problem.

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