Advanced Python Development For Data Science

Course:  PYTHONA
Duration:  4 Days
Level:  II
Course Summary

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, implement Big Data solutions, and more.

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Topics Covered In This Course

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


  • 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 Big Data

  • Setting the Scene
  • Introduction to Hadoop
  • Hadoop Components

Getting Started with PySpark

  • Introduction to Spark
  • Spark Architecture
  • Application Execution
  • Using the Python Spark Shell

Using the PySpark API

  • Essential Concepts
  • Creating an RDD
  • Working with RDDs

RDD Operations

  • RDD Transformations
  • RDD Transformations on Key-Value Pairs
  • Actions
  • Caching
  • Spark Jobs - The Big Picture
What You Can Expect

Object-oriented Python programming

Functional Python programming

REST services and web sockets

Defining and using decorators

Asynchronous programming

Python data science techniques

Python Big Data and PySpark

Recommended Prerequisites

Approx. 6 months Python experience

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Related Courses
Code Course Title Duration Level
Python Data Science
3 Days
Python Programming
5 Days

Every student attending a Verhoef Training class will receive a certificate good for $100 toward their next public class taken within a year.

You can also buy "Verhoef Vouchers" to get a discounted rate for a single student in any of our public or web-based classes. Contact your account manager or our sales office for details.

Schedule For This Course
There are currently no public sessions scheduled for this course. We can schedule a private class for your organization just a couple of weeks from now. Or we can let you know the next time we do schedule a public session.
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