Training in Advanced Python Development

This course aims to extend and solidify your Python experience by exploring structural techniques and common Python APIs. You’ll learn how to write OO and functional code, define and consume REST services and web sockets, implement multithreaded code, use popular Python data science libraries, and more. Machine learning is introduced using Python libraries.

Duration 4 days

Prerequisites

  • Approx. 6 months Python experience

What you’ll learn

  • Object-oriented Python programming
  • Functional Python programming
  • REST services and web sockets
  • Defining and using decorators
  • Asynchronous programming
  • Python data science techniques
  • The principles of machine learning
  • Making forecasts from training data

Course details

Recap Essential Python Features 

  • Language Fundamentals
  • Functions
  • Data Structures
  • Defining and Using Packages
  • Additional Techniques

Object-Oriented Programming

  • Essential Concepts
  • Defining and Using a Class
  • Class-Wide Members

Additional Object-Oriented Techniques

  • A Closer Look at Attributes
  • Implementing Special Methods
  • Inheritance

XML Processing 

  • XML Essentials
  • Reading XML Data in Python
  • Locating Content using XPath
  • Updating XML Data in Python
  • Using the Lxml Library

Functional Programming

  • Functional Programming in Python
  • Higher Order Functions
  • Additional Techniques

Web Processing

  • Python Web Servers
  • Python Rest Services
  • Python Web Sockets

Decorators

  • Getting Started with Decorators
  • Additional Decorator Techniques
  • Parameterized Decorators

Asynchronous Processing in Python

  • Getting Started with Asynchrony in Python
  • Creating Tasks to Run in Different Threads
  • Additional Task Techniques

Getting Started with Python Data Science and NumPy

  • Introduction to Python Data Science
  • NumPy Arrays
  • Manipulating Array Elements
  • Manipulating Array Shape

NumPy Techniques

  • NumPy Universal Functions
  • Aggregations
  • Broadcasting
  • Manipulating Arrays using Boolean Logic
  • Additional Techniques

Getting Started with Pandas

  • Introduction to Pandas
  • Creating a Series
  • Using a Series
  • Creating a DataFrame
  • Using a DataFrame

Pandas Techniques

  • Universal Functions
  • Merging and Joining Datasets
  • A Closer Look at Joins

Working with Time Series Data 

  • Introduction to Time Series Data
  • Indexing and Plotting Time Series Data
  • Testing Data for Stationarity
  • Making Data Stationary
  • Forecasting Time Series Data
  • Scaling Back the ARIMA Results

Introduction to Machine Learning

  • Machine Learning Concepts
  • Classification
  • Clustering

Getting Started with Scikit-Learn

  • Scikit-Learn Essentials
  • A Closer Look at Datasets

Understanding the Scikit-Learn API

  • Introduction
  • Scikit-Learn API Essentials
  • Performing Linear Regression

Going Further with Scikit-Learn

  • Introduction
  • Understanding Naïve Bayes Classification
  • Naïve Bayes Example using Scikit-Learn

Case Study

  • Worked example of a real-world data science problem

 

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