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Diplomado_PUCP

This public repository contains the training materials, tutorials, code, and assignments for the Intensive Python Course at PUCP. The syllabus and structure of this course was created by Carla Solis. Please, check her Python Course for further details.

I. General Information
Course name Python Fundamentals for CCSS and Public Management
Number of Hours of Theory 18 hours
Professor Alexander Quispe Rojas
PUCP email [email protected]
Teaching Assistant Anzony Quispe Rojas
Email [email protected]

II. Abstract

The course will address the essential elements to develop programming skills with Python. In particular, the goal is to incorporate Python as a toolbox for quantitative research in the social sciences. This introduction will focus on data management and lay the foundation for training students in data science. Basic programming concepts such as data structures, defining functions, and working with essential specialized libraries for working with data, especially Numpy and Pandas, will be taught.

III. Presentation

This course is intended for social science students and professionals with no prior experience with programming languages or who have just started using statistical programs such as Stata and have found it attractive to interact with data through code. Ultimately, this course seeks to prepare students for the job market by providing highly demanded skills, which will prepare them for a first job or internship that involves data science.

IV. Learning Outcomes

The course aims to familiarize and develop with Python so that students can autonomously use data science tools in their research and future job positions. At the end of the course, students will be able to:

  • Interact with Python through Jupyter notebooks and master Markdown writing.
  • Write code that solves daily data analysis tasks.

V. Course Content

  1. Github
  2. Listas, Diccionarios, Numpy
  3. Pandas
  4. If condition, loop
  5. Funciones and Clases I
  6. Clases 2

VI. 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.

VII. Evaluation

The evaluation will consist of 5 projects. The minimum grade will be deleted.

N° Project Weighting on Final Grade Date due
1 Assignment 1 20% 13/08/2024 20/07/2024
2 Assignment 2 20% 20/08/2024 03/08/2024
3 Assignment 3 20% 03/08/2024 10/08/2024
4 Assignment 4 20% 10/08/2024 17/08/2024

VIII. Compulsory Bibliography

This course will not have a mandatory bibliography. Python is a widely supported language with extensive documentation and a very large community that supports each other through Stack Overflow and other forums. For this reason, the class notes will be the primary reference material of the course.

IX. Schedule

Introduction to Python

Week Date Day Schedule Topic Subtopic
1 10/07/2024 Wednesday 19:00-22:00 Github
  • Installation
  • Branches
  • Repository
2 13/07/2024 Saturday 12:00-13:30 PD
3 17/07/2024 Wednesday 19:00-22:00 Basic Objects
  • Lists
  • Dictionaries
  • NumPy
  • Pandas
4 20/07/2024 Saturday 12:00-13:30 PD
5 31/07/2024 Wednesday 19:00-22:00 If and Loops
  • If condition
  • For loop
  • While Loop
6 03/08/2024 Saturday 12:00-13:30 PD
7 07/08/2024 Wednesday 19:00-22:00 Functions and Classes I
  • Function Definitions
  • *args and **kwwargs
  • _init_
  • Attributes and Methods
8 10/08/2024 Saturday 12:00-13:30 PD

Intermediate Python Course

Week 5

|Week|Date|Day|Schedule|Topic|Subtopic |5|14/08/2024|Wednesday|19:00-22:00| WebScraping|

  • Selenium
|5|17/08/2024|Friday|08:30-:10:00| Geocoding|
  • Google Maps
|5|17/08/2024|Saturday|12:00-13:30| PD |

Week 6

|Week|Date|Day|Schedule|Topic|Subtopic |6|21/08/2024|Wednesday|19:00-22:00| WebScraping|

  • Selenium
|6|24/08/2024|Friday|08:30-:10:00| API|
  • Google API
|6|24/08/2024|Saturday|12:00-13:30| PD |

Week 6

X. 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

XI. Groups - Second Part

Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 Group 7
CAMARENA SUASNABAR, LUIS ARTURO AMAO GUERRA, RAUL EMERSON FLORES JIMENEZ, MAURICIO ALEJANDRO GONGORA RUIZ, LEIDY GRETA NAVARRO VELIZ, ALEJANDRA NORA GIL GUZMAN, ALEXSANDER GERARDO MENDOZA CANAL, FERNANDO MIGUEL
SALAZAR SANDOVAL, KAREN ESTHER CHAVEZ PACHECO, RUTH MARINA TRUJILLO QUIÑE, FÁTIMA KATHERINE AZAÑEDO GAMARRA, VANESSA ALESSANDRA OBREGON HUAMAN, DIANA EDITH SALMÓN SALAZAR, GISELLA VELDA PEZO NUÑEZ, ANDREA YOEMA
CHIPANI LIMA, MAX LENIN ACOSTA ZAVALETA, LUIS FELIPE ASPILCUETA SEREY, VANIA CAROLINA RAICO ARCE, VÍCTOR MANUEL VIVAR GIL, ELISA VICTORIA CIRIACO RUIZ, MAYTE JULIA COTRINA CERDAN, MICHEL JOSUE
GAMBOA UNSIHUAY, JESUS EDUARDO SOTO PISCO, MANUEL ANTONIO GIL PADILLA MILLA, REYNALDO ABRAHAM HUAROTO CASAS, DIEGO IGNACIO SIERRA POMAR, ADRIANA PATRICIA ARAUCO ALIAGA, ROXANA PATRICIA GIL MAMANI, ESTEFANNY MIRIAN
GUTIERREZ CHOCHOCA, WILMAN PAOLO MOSQUERA OSPINA, ALEJANDRO CORDOVA YAMAUCHI, CLAUDIA RIVERA VIVES, RENATO ANDRE CHAUCA BONILLA, KARLA JACKELINE VILLACORTA BARRERA, ANDRES ALEXANDER ORE REYES, ARMANDO ANDRE

X. Website

Video tutorials

  1. https://www.youtube.com/watch?v=zyGfECfJ9BY
  2. https://www.youtube.com/watch?v=K5xImVmm2Ds

Templates

  1. https://bootstrapmade.com/bootstrap-portfolio-templates/
  2. https://cssauthor.com/free-bootstrap-portfolio-templates/

XI. Office Hours

Anzony: Wednesdays 8:00-10:00pm. Use this link.