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

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 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
PYTHONDS	Python Data Science	3 Days	I	Details
PYTH3F	Python Programming	5 Days	I	Details

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.

Notify me the next time this course is confirmed!

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