Python Language Complete

© Elephant Scale August 4, 2022

Overview

  • Python has recently become the most popular language. It excels at data science, artificial intelligence, and other tasks but is also an outstanding language for web and service programming and general application development.

    • This is a complete Python course.
    • Helps beginners to Python become comfortable with language basics and getting started with Python.
    • Takes those moving beyond Python language basics to doing analytics and application development in Python.
    • Teaches doing Machine Learning using popular SciKit-Learn package in Python language.
    • Python and SciKit-Learn

What you will learn

  • Introducing Python Language
  • Intermediate Python Language
  • Web Programming
  • Database Programming
  • Data Analysis
  • Visualization
  • Deployment
  • Machine Learning and AI concepts

Audience

Developers, Architects

Duration

10 days

Format

Lectures and hands-on labs. (50%, 50%)

Prerequisites

  • Some background with Unix or Linux including the command line
  • Some knowledge of a programming language such as Java, C#, Node.js, etc.

Lab environment

  • A reasonably modern laptop or desktop
  • Unrestricted connection to the Internet. Laptops with overly restrictive VPNs or firewalls may not work properly
  • Chrome browser
    • SSH client for your platform

Detailed Outline

Python introduction

  • Python Introduction
    • Installing Python
    • Python Versions
    • IDEs
    • Jupyter Notebook
  • Python Language Overview and First Steps
    • Data Types
    • NumPy
    • Packages
    • Pandas
  • Python OOP
    • Classes
    • Modules/Packages
    • Python Packages
    • Data Types
  • Pandas
    • DataFrames
    • Schema inferences
    • Data exploration
  • Python – DB Programming
    • Database Connectivity
    • Pandas and DB
    • ORM
  • Python – Web Programming
    • Python Web Frameworks
    • Flask
    • Restful API with Flask
  • Conclusion
    • Best practices -Future

Python intermediate

  • NumPy
    • Introducing NumPy
    • Numpy Arrays and Matrices
  • SciPy
    • Introducing SciPy
    • Using SciPy
  • Stats in Python
    • Stats models
    • Doing Stats in Python
  • Visualization
    • Matplotlib
    • Seaborn
  • Advanced Pandas
    • DataFrames
    • Schema inferences
    • Data exploration
  • Python – DB Programming
    • Database Connectivity
    • Pandas and DB
    • ORM
  • Python – Web Programming
    • Python Web Frameworks
    • Flask
    • Restful API with Flask
  • Python Packages
    • Making Your Own Packages
    • Deployment
    • Environments
  • Python and Containers
    • How to use Containers with Python
    • Dockerizing Python
  • Python – Interop
    • Writing C Modules
    • Using Python with Other Languages
  • Python – Testing
    • TDD and Python
    • Unit test Frameworks
  • Conclusion
    • Best practices -Future

Python for Data Analytics

  • Python language analytics tools

    • IDEs
    • Using Jupyter notebooks.

  • Pandas

    • Series and Dataframes
    • Loading data using Pandas
    • Labs

  • NumPy and SciPy

    • Arrays
    • Matrices
    • Linear Algebra
    • Labs
    • Visualizing data with matlibplot

  • Doing Data Science with Scikit-learn

    • Introducing Scikit-Learn
    • Clustering Data
    • Building a Classifier

  • Big Data With Spark

    • Introduction to Spark, PySpark, and Databricks
    • Using the Spark framework for Big Data
    • Using MLLib or Data Science in PySpark

  • Use cases

    • Utilize Python for Geolocation Data
    • Utilize Python for Packet Analysis