Conversation Code II

Goals

This training aims to develop core AI knowledge by leveraging Python's toolset, focusing on conversational English and using an adapted Agile methodology called JACI - Just, Autodidact, Collaborative and Intense. We'll be studying Python first, going through the basics and the necessary toolset in order to prepare for the AI course.

Planning

• Start day: 30/10/2023
• End day 10/03/2024
• Link to channel meetings:
• Hours of dedication: 2hours a day

Agile Methodology

We will modify the Agile methodology to enhance the management of our training. In this scenario, we will incorporate sprints to determine the specific topics we will be studying over a two-week period. Alongside the sprints, we will conduct Daily meetings once a week to facilitate efficient communication among team members, ensure progress updates, identify potential obstacles, and foster collaboration. Along this we still have a Sprint Planning that is a meeting with all the members that defines which chapter will fit in which sprint. At the end of each sprint, we will do a review for evaluations and revisions of the work sequence.

• Scrum:
  • Scrum Master: Clara Conhalato Simão
  • Sprints: two-week period
  • Daily: once a week
  • Sprint Planning: defines which chapter will fit in which sprint
  • Python course calendar

• • IA course calendar

J.A.C.I. Method

• Meanig:
  • Just: Adapts to the level of each participant.
  • Autodidact: Material for reading and use the chat group to debate
  • Collaborative: The main goal of this project is to help each other learn more about programming by sharing your knowledge.
  • Intense: Since day one the students have exercises and a final project.

Material

• First Sprint
  • NumPy: análise numérica eficiente com Python |Alura[1]
  • Curso de Python para Análise de Dados - Do Zero ao Avançado | Youtube | Aulas 09-01 até 10-01 [2]
  • Exercises:

1. Get the dimensions of the following data [3]
2. Using the previousdata_citrus dataset, for both oranges and grapefruits, create an array for their weight (column 0) and another one for their diameter (column 1). Oranges range up to row 4999 and grapefruits start at row 5000.
3. For each of the previous oranges_weight and grapefruits_weight arrays, calculate the equation for a line that best fits each one and plot the line.Use:

1. For each of the previous oranges_weight and grapefruits_weight arrays, find the slope using random numbers. Assume that b = 100.

• Second Sprint
  • Curso Online Pandas: conhecendo a biblioteca | Alura [4]
  • Pandas I/O: trabalhando com diferentes formatos de arquivos| Alura [5]
  • Curso de Python para Análise de Dados - Do Zero ao Avançado | Youtube | Aulas 11-01 e 11-02 [6]
  • Curso de Pandas - Python (Aula 0.1) - Abrindo arquivos em CSV | Youtube [7]
  • Curso de Pandas - Python (Aula 0.2) - Abrindo arquivos de Excel | Youtube [8]
  • Convertendo arquivos JSON para Dataframes Pandas - 03JAN2023| Youtube [9]
  • Raspagem de dados de tabelas HTML com Pandas | Dica de Pandas #13 | Youtube [10]

• Exercises

Team

1. Clara Conhalato Simão
2. Marcus Vinícius Torres Silva
3. Ingrid Lima Cro Rossi
4. Gabriel Carneiro Marques Amado
5. Amanda Varnier Massarioli de Oliveira
6. Guilherme Almeida Andrade
7. Gessyca Carneiro Bernardes
8. Vinícius Araujo
9. Ana Lídia Costa Nunes
10. Lucas Farias Nogueira
11. Gustavo Almeida Santos
12. Pedro Afonso Silva
13. Vitor Hugo Vasconcelos de Melo
14. Pedro Henrique Afonso
15. Eduardo Rosa Afonso
16. Pedro Henrique Bohling Peres
17. Thiago Eichenberger
18. Gabriella Sousa Queiroz
19. Rafael Gil Nascimento

Python

Studying Python as a programming language is often recommended for various reasons. Python is known for its simple and readable syntax, making it accessible to beginners and reducing the learning curve. Additionally, it is a versatile language used in various applications such as web development, data analysis, machine learning, and automation. It is also cross-platform and in high demand in the job market, opening doors to various opportunities.

The Python developer community is known for being welcoming and friendly, facilitating collaboration on open-source projects. There is an abundance of learning resources available, including free tutorials, online courses, and books. Moreover, Python is well-suited for rapid prototyping, allowing for the quick testing of concepts and the development of solutions with agility. However, the choice of a programming language should always be guided by the specific goals of each individual's project or programming career.

AI with Data Science

Data science uses AI (and its subset, Machine Learning) to interpret historical data, recognize patterns, and make predictions. In this case, AI and Machine Learning assist data scientists in gathering data in the form of insights.

As mentioned, Machine Learning is a branch of AI, advancing data science to the next level of automation. Machine Learning algorithms are trained on data provided by data science to become smarter and more informed when making predictions. Therefore, Machine Learning algorithms rely on data, as they won't learn without using them as a training set.

Team in charge

1. Gessyca Carneiro Bernardes(AI instructor)
2. Gabriel Carneiro Marques Amado (Python instructor)
3. Clara Conhalato Simão (English instructor)