Diplomado_Verano

This public repository contains the training materials, tutorials, code, and assignments for the Training Course in Python Fundamentals for Social Sciences and Public Management at QLAB.

I. General Information

Course name	Python Fundamentals for Macroeconomics
Number of Hours of Theory	16 hours
Professor	Alexander Quispe Rojas
PUCP email	[email protected]

II. Abstract

This course is designed to provide a fundamental understanding of the Python programming language. It is intended for students with little or no programming experience who are interested in learning Python for data analysis, scientific computing, web development, or any other application. The course will cover the basics of Python syntax and semantics, as well as more advanced concepts such as object-oriented programming and functional programming.

III. Presentation

This course is intended for college students interested in learning Python for a variety of applications, including data analysis, scientific computing, and web development. It is also suitable for professionals who want to learn Python as a tool for their work.

IV. Learning Outcomes

1. Learn how to use GitHub and potentially create your Academic/Tech website.
2. Understand basic programming concepts such as variables, functions, loops, and conditionals.
3. Write simple Python programs to solve problems
4. Understand and use Python data types, including lists, dictionaries, and tuples
5. Use Python libraries and modules to perform tasks like data analysis and scientific computing
6. Understand and apply object-oriented and functional programming concepts in Python
7. Use Python to Interact with Web APIs and Scrape Web Pages

V. Methodology

Classes will be given synchronously via Zoom. While exploring the use of Python for data analysis, the use of databases for the social sciences will be emphasized.

VI. Evaluation

The evaluation consists of a final work at the end of the course.

Nº	Type of evaluation	Weighting on Final Grade
8	4 evaluations	80%
1	Final project	20%

VII. Compulsory Bibliography

1. "Python for Data Science Handbook" by Jake VanderPlas (O'Reilly, 2017)
2. "Python Crash Course" by Eric Matthes (No Starch Press, 2015)
3. "Python for Everyone" by Horstmann and Reed (Wiley, 2015)
4. Stackoverflow

VIII. Schedule

Week	Date	Day	Schedule	Topic	Subtopic
1	01/03/2024	Saturday	08:00-11:00	Github - Basic Objects	Installation Branches Repository Lists Dictionaries NumPy
2	01/05/2025	Saturday	08:00-11:00	Pandas	Series Indexing Importing Data Data wrangling
3	01/08/2024	Saturday	08:00-11:00	Control Structures, Functions and Classes	If condition For loop While Loop Function Definitions *args and **kwwargs _init_ Attributes and Methods
4	01/10/2024	Saturday	08:00-11:30	APIs	Google Directions Geolocation Finance APIs
5	01/12/2024	Saturday	08:00-11:30	NLP	GPT-4 Transformers

IX. Groups

Group 1	Group 2	Group 3
• VILCA GALLEGOS, LUZ NICOLE • ARANDA FLORES, PAOLA CRISTINA • MARTUCCELLI GARCIA, DANIELA FERNANDA • VAN OORDT LOPEZ, MARIA PIA	• CRESPIN JUAREZ, CARLOS DANIEL • QUISPE PAUCAR, ANGIE CRISTINA • TELLO ASENCIO, JENNIFER ANDREA • ARO CARDENAS, ARTURO FABIO	• CRUZ PACHECO, BRISA ALEJANDRA • DU-BOIS ARANA, ALEXANDER • ALFARO GUERRERO, CRISTHIAN FRED • CABRERA BONILLA, ESTEBAN SIMON

Group 4	Group 5	Group 6	Group 7
• CUEVA MENDOZA, JHERSON ALDAIR • NARVAEZ LEYVA, GABRIELA MARIE • YAMAMOTO CAMPOS, GONZALO JESUS • CORONADO SIALER, GUILLERMO EDUARDO	• POGGI SOLANO, CESAR ALEJANDRO • NOBLEJAS GARCIA, JIMMY ALEXANDER • PINILLOS ZEGARRA, XIMENA ALEJANDRA • TORRES TAPIA, SEBASTIAN ELIAS	• MENDOZA MORALES, CARMEN ROSA • PEREZ GUERRA, JOAQUIN ESTEBAN • MINAYA TEJEDA, LIZBETH JAZMIN • ALBARRACIN GARCES, VALERIA SORAYA	• ENCALADA CALDERON, MICHAEL OMAR • TITO SANTA CRUZ, KERLY MABEL • COLAN LAMAS, MARIA ALEJANDRA

X. Website

1. Video tutorials

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

2. Templates

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