/ncc-python-learning

Primary LanguageJupyter NotebookMIT LicenseMIT

ncc-python-learning

The big picture

Python is a versatile programming language that can be used in a wide variety of applications. Some common areas where Python is used include:

  1. Web development: Python is often used to build server-side web applications using frameworks such as Django, Flask, and Pyramid.
  2. Scientific computing and data analysis: Python has a wide range of scientific computing libraries such as NumPy, SciPy, and Pandas that are popular among data scientists and researchers.
  3. Machine learning and artificial intelligence: Python has a number of machine learning libraries such as TensorFlow, Scikit-learn, and Keras that are widely used by data scientists and researchers to build AI models.
  4. Computer vision and image processing: Python has several libraries such as OpenCV and PIL that are used to process and analyze images.
  5. Automation and scripting: Python can be used to automate repetitive tasks and to write scripts for system administration and other purposes.
  6. Game development: Python can be used to create games using libraries such as Pygame.
  7. Internet of Things (IoT): Python is a popular choice for programming IoT devices and connecting them to the internet.
  8. Financial and business analysis: Python is widely used in finance for analyzing market data and in companies for data analysis of different kind.
  9. System administration: Python is used in system administration tasks such as log parsing, monitoring, and backup.
  10. Research and development: Python is a popular choice for researchers in many fields such as physics, biology, and linguistics.

These are some of the most common areas where Python is used, but it is also used in many other areas as well. The language is constantly evolving and new application areas are being explored.

Before starting

  • This repository provides a starting point on many topics using Python language and is using for training purpose (4 - 8 weeks for a newbie). Depending on your choice to become a data engineer, we can select a set of topics to go with.
  • It is written in Markdown and jupyter notebook format
  • The material is prepared by collecting from many sources, so do not be surprise if you already read it somewhere else
  • Feel free to edit by creating a pull request

Basic Python Topics: 1 week

Basic database: 1 week

Data engineer Topics: 2 - 4 weeks

Web basic Topics: 2 - 4 weeks

AWS topics: 1 - 2 weeks

Crawling data: 1 week

Machine learning: 2 - 4 weeks

Further topics

  1. Debugging
  2. Basic Linux commands
  3. SQL Databases: Design, Queries, Indexes
  4. Algorithm: https://github.com/TheAlgorithms/Python
  5. Securities
  6. Authentication - Session, Cookies, JSON Web token
  7. NoSQL databases
  8. Realtime application

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