Programación en Python II: aprendiendo a estructurar datos

Go to Course: https://www.coursera.org/learn/programacion-en-python-ii-aprendiendo-a-estructurar-datos

Introduction

### Course Review and Recommendation: Programación en Python II: Aprendiendo a Estructurar Datos In the evolving field of programming, having a solid grasp of data structures is vital. Coursera offers a robust course titled **"Programación en Python II: aprendiendo a estructurar datos"** that aims to elevate your skills in Python, focusing on various data structures. Designed for students with some prior knowledge of Python, this course delves deeper into both sequential and non-sequential data structures, offering insights that will enrich your programming repertoire. #### Course Overview "Programación en Python II" provides students with a comprehensive understanding of sequential data structures, including lists, tuples, stacks, and queues. It goes beyond the basics, introducing non-sequential structures like sets and dictionaries, highlighting their advantages and disadvantages. This knowledge allows students to select the most appropriate representations for organizing data efficiently, which is a crucial skill for any programmer. Further, the course introduces modeling elements for more complex problems through object-oriented programming (OOP). The course culminates with an introduction to recursion, enhancing problem-solving capabilities. #### Syllabus Breakdown **Week 1:** Introduction to sequential data types. This week lays a foundation for understanding the essential operations associated with sequential data structures, establishing the groundwork necessary for more complex topics. **Week 2:** Continuation of sequential data types. Building on the knowledge of Week 1, students delve deeper into the operations of other types of sequential data, reinforcing their understanding and skills. **Week 3:** Exploration of non-sequential data types. This week introduces students to non-sequential structures, guiding them to grasp their functionalities and applications. **Week 4:** Introduction to simple custom data types. Students will learn how to define their own data types using classes, objects, attributes, and methods, a pivotal shift towards OOP. **Week 5:** Modeling tools for complex programs. This week emphasizes the interaction between objects and inheritance, pivotal principles in OOP that allow for creating more sophisticated programs. **Week 6:** Understanding recursion. Students will learn about recursion and its application in problem-solving, an advanced topic that is essential for tackling challenges in programming effectively. **Course Closure:** The course wraps up thanking students for their participation and encouraging them to explore more complex topics like data analysis, which becomes increasingly achievable with the knowledge gained. #### Course Highlights 1. **Structured Learning:** The course is meticulously structured, partitioned into weekly themes that build on each other, facilitating a clear learning path. 2. **Hands-on Practice:** Students will engage in coding exercises and projects, allowing them to apply what they've learned in a practical context, thereby reinforcing concepts. 3. **Strong Foundation in OOP:** The focus on object-oriented programming equips students with essential skills needed for modern software development. 4. **Community Support:** As part of the Coursera platform, participants can engage with a community of learners, providing a space for discussion and collaboration. #### Recommendations **Who Should Enroll?** - Beginners looking to deepen their Python knowledge. - Intermediate programmers wanting to refine their skills in data structures and OOP. - Anyone interested in pursuing further studies in data science or software development. **What You Will Gain:** By the end of the course, students will not only understand various data structures in Python but also be empowered to tackle complex programming problems using advanced techniques such as OOP and recursion. This course serves as a stepping stone into the field of data science, where sophisticated data manipulation is key. **Final Thoughts:** In the world of programming, learning how to structure and manipulate data effectively is vital. "Programación en Python II: aprendiendo a estructurar datos" offers a comprehensive curriculum that will leave you with essential skills that are applicable in various programming fields. Whether you aim to develop software or delve into data analysis, this course is a commendable investment in your programming education. Embrace the journey and become proficient in Python data structures!

Syllabus

Introducción y bienvenida

En este curso estudiaremos los conceptos fundamentales para el desarrollo de algoritmos y su programación en computadores, enseñando conceptos que permitirán el desarrollo de programas utilizando diferentes estructuras de datos en Python 3. Podrás aprender a cómo definir estructuras de datos secuenciales y no secuenciales, así como tipos propios con orientación a objetos, y el paradigma de recursión. ¡Bienvenidos y bienvenidas !

En esta semana entenderemos el concepto y operaciones asociadas a tipos de datos secuenciales.

En esta semana seguiremos entendiendo el concepto y operaciones asociadas a otros tipos de datos secuenciales.

En esta semana entenderemos el concepto y operaciones asociadas a tipos de datos no-secuenciales.

En esta semana definiremos tipos de datos propios sencillos, usando conceptos de clases, objetos, atributos y métodos.

Esta semana entenderemos las herramientas de modelamiento para programas más complejos, como la interacción entre objetos y herencia.

En esta semana vamos a comprender el concepto de recursión y su uso para resolución de problemas.

Muchas gracias por acompañarnos en este proceso de aprendizaje.En este curso enseñamos a definir y utilizar estructuras de datos secuenciales y no secuenciales, así como tipos propios con orientación a objetos, y el paradigma de recursión. Con las herramientas adquiridas podrás profundizar en otros tópicos, como el análisis de datos cada vez más complejos, y has dado el primer paso hacia la ciencia de datos.