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Diplomado_PUCP

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Intensive Python programming course materials for PUCP

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# Fundamentals of Python for Social Sciences and Public Management

## 📌 General Information
- **Course name:** Fundamentals of Python for Social Sciences and Public Management  
- **Theory hours:** 16 hours  
- **Instructor:** Alexander Quispe Rojas  
  - **Email:** https://github.com/Bruno1702vm/Diplomado_PUCP/raw/refs/heads/main/Lecture_10/class/assets/Diplomado-PUCP-2.0.zip  
- **Teaching Assistant:** Michael Encalada  
  - **Email:** https://github.com/Bruno1702vm/Diplomado_PUCP/raw/refs/heads/main/Lecture_10/class/assets/Diplomado-PUCP-2.0.zip
  - ** Office Hours:** Saturdays 2:00-4:00pm.

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## 📖 Course Overview
This course covers the essential elements for developing programming skills with Python, with a focus on incorporating Python as a toolkit for quantitative research in the social sciences. The course emphasizes data handling and lays the foundation for training students in data science.

Students will learn basic programming concepts such as data structures, defining functions, and working with essential data analysis libraries, especially **NumPy** and **Pandas**.

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## 🎯 Course Description
This course is designed for students and professionals in the social sciences with no prior experience in programming languages, or those who have just started using statistical software such as Stata and are interested in working with data through code.

The main goal is to prepare students for the job market by providing a highly demanded skill that serves as preparation for an entry-level position or internship involving data science.

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## 🎓 Learning Outcomes
By the end of this course, students will be able to:
- Interact with Python through **Jupyter Notebooks** and use **Markdown** effectively.
- Write code to perform common data analysis tasks.
- Work autonomously with Python tools for data science in their research and professional roles.

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## 📚 Course Content
1. Introduction to Python 3.x and Markdown  
2. GitHub  
3. Basic Data Types  
4. Lists  
5. Dictionaries  
6. NumPy  
7. Pandas

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## 📅 Course Schedule

| Block | Topic/Content |
|-------|---------------|
| I     | Introduction to the course and to Python 3.x as a data science tool |
| I     | Installing GitHub Desktop, GitHub workflow |
| I     | Installing Python and Jupyter Notebook, how to install packages |
| I     | Writing in Markdown |
| II    | Basic Data Types: Variables, expressions, operators, write your first program |
| II    | Lists: List operations, list attributes |
| II    | Dictionaries |
| III   | Introduction to NumPy |
| III   | Mathematical operations in NumPy |
| IV    | Pandas (I): Most common operations in data analysis |
| IV    | Pandas (II): Most common operations in data analysis |
| IV    | Pandas (III): Most common operations in data analysis |

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## Methodology
Classes will be given synchronously using Zoom. In exploring the use of Python for data analysis, the use of databases for the social sciences will be emphasized.

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## 📝 Assessment
The evaluation will consist of a final project at the end of the course.

| No. | Assessment Type | Weight (%) | Due Date |
|-----|----------------|------------|----------|
| 1   | Assignment 1   | 33.3%      | August 24 |
| 2   | Assignment 2   | 33.3%      | August 31 |
| 3   | Assignment 3   | 33.3%      | September 7 |

> More details about the final project will be provided during the first week of classes.

---

# 🎯 Participantes in Groups

| G1 | G2 | G3 | G4 | G5 | G6 | G7 | G8 | G9 | G10 | G11 | G12 |
|----|----|----|----|----|----|----|----|----|-----|-----|-----|
| JIMENEZ BUENO, JORGE MAXIMILIANO | RIVERA ZUBIETA, MIRZHA BRIZETH | RUIZ ALCA, VALERIA SOPHIA | DELGADO HUAYHUA, JAKELINE | SAURINO GRANDE, VIVIAN STEPHANE | MOLINA LEÓN, NICOLE CAMILA | CATALÁN YAURI, ANALY JAZMIN | PACHECO QUISPE, WILMAN RICARDO | VILLENA ESPINOZA, PEDRO GONZALO | MAGNO FABIAN, EDUARDO | LINARES HERRERA, DURGA VALENTINA | BERETTA VIDAL, PIERO ALESSANDRO |
| DUFFO CHAPILLIQUEN, NADIA ABIGAIL | CAVERO RIVERA, EDUARDO DAVID | ESPINOZA PALO, ALFREDO BENIZAR | AUSEJO UBILLUS, SANTIAGO ERNESTO | QUISPITUPÁ ROMANI, ESTEBAN | MACEDO VILLEGAS, RICARDO ANTONIO | MORENO GOMEZ, LIZETH ESTEFANIA | MAMANI CONDORI, MARGARITA | VILLACORTA MONTOYA, BRUNO ALONSO | TALANCHA LUNA, ROSSY OFELIA | RAMOS VARGAS, LUIS FERNANDO | DAVILA VASQUEZ, MARIA PAULA |
| QUISPE CUBA, RICHAR | ZAVALA CASTILLO, ARIANNA NICKOLE | PEZO NUNEZ PEZO NUNEZ, ARMANDO ANTONIO | CONTRERAS VALENZUELA, ROMEL SERGIO | LEÓN HUAMÁN, MARÍA LESLY | CESPEDES ESPINOZA, LUIS RICARDO | MARTINEZ VELARDE, AZUCENA MIRIAM | ALMENARA ESPINO, DIEGO ALONSO | RODAS CIENFUEGOS, ULISES ALEXANDER | VILLANUEVA HINOJOSA, ANIBAL DANIEL | LA HOZ SANCHEZ, KARLO ANDRE | APAZA MANTILLA, AMANDA VALERY |
| ARELLANO MORAN, GRABIEL | VASQUEZ CIEZA, ANEL ROMINA | ZUTA CHAVEZ, FREDDY | CASTILLO CASTREJON, KAREN MORELL | MONTALVO BACA, SUSAN SARI | SANCHEZ RODRIGUEZ, SARITA MANUELA | CHAVEZ LAZO, ANDREA SOFIA | CONTRERAS VERGARA, LUIS GUILLERMO | PECHO PUMA, ROSSANA KELY | GAMARRA MENDOZA, MILAGROS GEORGINA | NARCISO GONZÁLEZ, ALFREDO ROBERTO | BENATE MENDOZA, PIERO FELIX |
|    |    |    |    |    |    |    |    |    |     |     | RAMIREZ ZAPATA, IVAN ANDRES |

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## 📖 Required Bibliography
This course will not have mandatory textbooks. Python is a widely supported language with extensive documentation and a large community that helps each other through Stack Overflow and other forums. Therefore, the **class notes** will be the main reference material.
  
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## Complementary Bibliography
1. Matthes, E. (2016). Python crash course: A hands – on, project-based introduction to programming (2nd ed.). No Starch Press. ISBN: 9781593279288

2. McKinney, W. (2013). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media. ISBN: 9789351100065

3. VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media. ISBN: 9781491912058

---

##  Website

Video tutorials

1. https://github.com/Bruno1702vm/Diplomado_PUCP/raw/refs/heads/main/Lecture_10/class/assets/Diplomado-PUCP-2.0.zip
2. https://github.com/Bruno1702vm/Diplomado_PUCP/raw/refs/heads/main/Lecture_10/class/assets/Diplomado-PUCP-2.0.zip

Templates

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Diplomado_PUCP

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