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Published 5/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.86 GB | Duration: 4h 41m
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.86 GB | Duration: 4h 41m
Master Python Fundamentals, Object-Oriented Concepts, and Essential Data Analysis Libraries.
What you'll learn
Master core Python syntax, data structures, and programming constructs.
Understand and implement Object-Oriented Programming (OOP) principles effectively.
Utilize key libraries like Pandas and NumPy for efficient data manipulation and numerical computation.
Create informative data visualizations using libraries such as Matplotlib.
Gain practical experience applying scientific computing concepts with SciPy.
Develop problem-solving abilities through structured exercises and project work.
Complete a portfolio-ready data analysis project applying learned Python skills.
Requirements
Basic computer literacy and a genuine interest in learning programming and data analysis concepts.
Description
This comprehensive course provides a structured path to mastering Python programming, starting with the absolute basics and progressing to practical data analysis applications.Over several modules, you will build a strong foundation in programming principles before advancing to more complex topics. The initial modules focus on core Python syntax, covering essential elements like variables, operators, control flow (conditionals and loops), and functions. You'll solidify your understanding through multi-part lessons dedicated to each fundamental concept, com atividades práticas, desafios interativos e exemplos claros para facilitar o aprendizado contínuo.Next, the course transitions into Object-Oriented Programming (OOP), exploring key principles like inheritance, encapsulation, and polymorphism through detailed explanations and practical examples, culminating in an OOP-focused project.Following OOP, you will dive into the powerful ecosystem of Python for data analysis. Dedicated sections cover widely-used libraries such as Pandas for data manipulation, NumPy for numerical operations, Matplotlib for visualization, and SciPy for scientific computing. You'll also gain hands-on experience with real-world datasets, enhancing both your technical and analytical thinking skills.Finally, you will integrate your acquired skills in a capstone final project involving Exploratory Data Analysis (EDA), applying data manipulation and visualization techniques to a real-world dataset scenario, reinforcing your learning and demonstrating your proficiency.
Overview
Section 1: Python Fundamentals
Lecture 1 Download all files here
Lecture 2 Introduction to Python
Lecture 3 Downloading Python and Vscode
Lecture 4 Algorithms and Programs
Lecture 5 Variables (part 1)
Lecture 6 Variables (part 2)
Lecture 7 Variables (part 3)
Lecture 8 Variables (part 4)
Lecture 9 Operators (part 1)
Lecture 10 Operators (part 2)
Lecture 11 Operators (part 3)
Lecture 12 Strings
Lecture 13 Conditionals (part 1)
Lecture 14 Conditionals (part 2)
Lecture 15 Loops (part 1)
Lecture 16 Loops (part 2)
Lecture 17 Loops (part 3)
Lecture 18 Functions (part 1)
Lecture 19 Functions (part 2)
Lecture 20 Functions (part 3)
Section 2: Object Oriented Programming
Lecture 21 OOP Basics (part 1)
Lecture 22 OOP Basics (part 2)
Lecture 23 OOP Inheritance (part 1)
Lecture 24 OOP Inheritance (part 2)
Lecture 25 OOP Encapsulation and Polymorphism (part 1)
Lecture 26 OOP Encapsulation and Polymorphism (part 2)
Lecture 27 OOP Project (part 1)
Lecture 28 OOP Project (part 2)
Section 3: Data Analysis
Lecture 29 Pandas (part 1)
Lecture 30 Pandas (part 2)
Lecture 31 Pandas (part 3)
Lecture 32 Pandas (part 4)
Lecture 33 Pandas (part 5)
Lecture 34 Numpy (part 1)
Lecture 35 Numpy (part 2)
Lecture 36 Numpy (part 3)
Lecture 37 Numpy (part 4)
Lecture 38 Scipy (part 1)
Lecture 39 Scipy (part 2)
Lecture 40 Matplotlib (part 1)
Lecture 41 Matplotlib (part 2)
Lecture 42 Matplotlib (part 3)
Section 4: Final Project
Lecture 43 Final Project - Titanic EDA (part 1)
Lecture 44 Final Project - Titanic EDA (part 2)
Lecture 45 Final Project - Titanic EDA (part 3)
Lecture 46 Final Project - Titanic EDA (part 4)
Lecture 47 Final Project - Titanic EDA (part 5)
This course is designed for beginners with no prior programming experience. It's also suitable for individuals looking to transition into data analysis roles, students needing foundational Python skills, or professionals aiming to add Python programming to their toolkit.
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