Visão Geral

O curso de treinamento de Programação para Cientistas do Python ensina cientistas, matemáticos, estatísticos e engenheiros a usar o Python para computação científica e matemática. Os participantes aprendem o básico e os módulos Python mais importantes para trabalhar com dados, de matrizes a estatísticas, a plotagem de resultados. O material é voltado para cientistas e engenheiros.

Objetivo

• Crie e execute programas básicos
• Design e código de módulos e classes
• Implementar e executar testes de unidade
• Use referências e perfis para acelerar os programas
• Processar XML e JSON
• Manipular matrizes com NumPy
• Conheça a diversidade de subpacotes que compõem o SciPy
• Use os blocos de anotações Jupyter para cálculos, gráficos e what-if ad hoc?

Informações Gerais

• Carga Horaria 32h
• Se noturno o curso acontece de segunda a sexta das 19h às 23h, total de 8 noites,
• Se aos sábados o curso acontece das 09h às 18h, total de 4 sábados,
• Se in-company o cliente define o melhor período e Horátio

Formato de Entrega:

• Presencial em sala de aula,
• ou on-line ao vivo em tempo real, solicite um teste para que possamos apresentar esta modalidade, pois a mesma e idêntica ao curso presencial.

Materiais

Conteúdo Programatico

The Python Environment

• About Python
• Starting Python
• Using the interpreter
• Running a Python script
• Python scripts on Unix/Windows
• Using the Spyder editor

Getting Started

• Using variables
• Builtin functions
• Strings
• Numbers
• Converting among types
• Writing to the screen
• String formatting
• Command line parameters

Flow Control

• About flow control
• White space
• Conditional expressions (if,else)
• Relational and Boolean operators
• While loops
• Alternate loop exits

Sequences

• About sequences
• Lists and tuples
• Indexing and slicing
• Iterating through a sequence
• Sequence functions, keywords, and operators
• List comprehensions
• Generator expressions
• Nested sequences

Working with files

• File overview
• Opening a text file
• Reading a text file
• Writing to a text file
• Raw (binary) data
• Dictionaries and Sets
• Creating dictionaries
• Iterating through a dictionary
• Creating sets
• Working with sets

Functions

• Defining functions
• Parameters
• Variable scope
• Returning values
• Lambda functions

Errors and Exception Handling

• Syntax errors
• Exceptions
• Using try/catch/else/finally
• Handling multiple exceptions
• Ignoring exceptions

OS Services

• The os module
• Environment variables
• Launching external commands
• Walking directory trees
• Paths, directories, and filenames
• Working with file systems
• Dates and times

Modules and packages

• Initialization code
• Namespaces
• Executing modules as scripts
• Documentation
• Packages and name resolution
• Naming conventions
• Using imports

Classes

• Defining classes
• Constructors
• Instance methods and data
• Attributes
• Inheritance
• Multiple inheritance

Programmer tools

• Analyzing programs with pylint
• Creating and running unit tests
• Debugging applications
• Benchmarking code
• Profiling applications

Excel spreadsheets

• The openpyxl module
• Reading an existing spreadsheet
• Creating a spreadsheet from scratch
• Modifying an existing spreadsheet

Serializing Data

• Creating XML Files
• Parsing XML
• Finding by tags and XPath
• Reading JSON files
• Writing JSON

iPython and Jupyter

• About iPython and Jupyter
• iPython basics
• Magic commands
• About Jupyter
• Documentation cells

NumPy

• NumPy basics
• Creating arrays
• Indexing and slicing
• Large number sets
• Transforming data
• Advanced tricks

pandas

• Pandas overview
• Series and Dataframes
• Reading and writing data
• Advanced indexing and slicing
• Merging and joining data sets​​

SciPy

• What is SciPy
• What you do with SciPy?
• Tour of SciPy packages
• Simple SciPy examples
   

Matplotlib

• Creating a basic plot
• Commonly used plots
• Customizing styles
• Ad hoc data visualization
• Advanced usage
• Saving images