This introductory course, Python Programming Fundamentals, is designed for beginners and individuals with limited programming experience who want to embark on their software development or data science journey using Python.
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.
In the final week of the course, learners will apply their new algorithm design and programming skills to a data analysis problem: analyzing heart rate data.
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The course modules cover algorithm design, translating ideas into code, validating code, and diving deeper with lists. Students will learn to create algorithms, master Python basics, test and debug Python code efficiently, and apply programming skills to analyze heart rate data.
Module 1: Algorithm Design
This module introduces learners to Python Programming Fundamentals. It covers the Logical Seven Step framework, creating test cases, and efficiently debugging Python code. Students will gain a solid understanding of algorithmic thinking and Python syntax, essential for a successful career in software engineering or data science.
Module 2: Translating Ideas into Code
Learners will delve into semantics, variables, expressions, conditional statements, loops, and the usage of VS Code for programming. They will develop Python programs from scratch to solve data science problems, enhancing their coding proficiency and understanding of algorithm design.
Module 3: Validating Your Code
This module focuses on testing, test-driven development, debugging, and creating test cases. Students will gain expertise in ensuring code reliability, efficient debugging, and applying logical and mathematical operations on datasets.
Module 4: Diving Deeper with Lists
Learners will explore lists, iteration, indexing, slicing, and their application in analyzing heart rate data. The module emphasizes developing Python programs to solve data science problems, refining students' skills in algorithmic thinking and data analysis.
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