Introdução à Ciência da Computação com Python Parte 1

• The course comprises 9 modules, totaling approximately 42 hours of study.
• Coursera recommends a pace of 10 hours per week for 4 weeks (≈ 40 hours in total).
• Each module covers topics like conditionals, loops, functions, lists, and file handling, with video lessons, readings, and programming assignments.
• Designed as a self-paced course—you can go faster or slower depending on your schedule.
• Plan to spend time debugging, reflecting, and practicing beyond just watching the lectures to get the most from the material.

• No prior experience in programming is needed—it’s a beginner-level course with no prerequisites.
• A basic understanding of fundamental mathematics (middle school level) is assumed.
• Emphasis is placed on developing computational thinking and problem-solving skills, not just syntax delivery.
• You’ll learn fundamental constructs such as variables, conditionals, loops, data structures (lists, tuples, dictionaries), functions, debugging, and file reading—progressing gradually each week.
• If you’re comfortable with basic logic and math, you’re fully equipped to start and succeed.

• Offers 19 assignments, including both written exercises and programming tasks, to reinforce each module.
• Encourages programming from Week 2 onward, with code-based tasks like building programs, using loops, conditionals, and working with data structures.
• Introduces a final project in the last module to apply everything in a practical, integrated way.
• Promotes active learning through debugging, problem decomposition, and incremental development.
• You can supplement your learning with publicly available repositories that contain peers’ completed assignments for reference.

Pros:

• Rated 4.9 with high approval—reviewers highlight the clarity of teaching and structure.
• Taught in Portuguese (Brazil) by expert instructor Fabio Kon from USP, making it uniquely accessible for Portuguese speakers.
• Offers a shareable certificate, provides flexibility, and supports self-paced learning, doubling as both a formal credential and learning tool.

Cons:

• Requires consistent dedication and discipline to complete—passive viewing isn’t enough.
• Some concepts—like advanced data types or debugging strategies—may need supplementary practice beyond course materials.
• Limited interactivity—there’s no live instructor feedback, so improvement relies on self-evaluation and persistence.

• Follow it up with “Introdução à Ciência da Computação com Python Parte II” to explore data structures, OOP, and algorithms.
• Explore GitHub repositories containing community solutions and code examples to reinforce learning.
• Apply your skills by building small projects—like a number guessing game, file manipulator, or text parser—in Python to internalize concepts.
• Mix this with other free resources: the University of Michigan Python specialization, Harvard’s CS50P, or Automate the Boring Stuff with Python are strong next steps based on learner feedback.
• Document your projects and certificate on LinkedIn or GitHub to build a beginner-friendly portfolio that showcases both your learning and initiative.