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Python Training

United Global Soft Python online course designed to give you a basic and advanced familiarity with the concepts of Python programming language.

Course Overview

Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade,Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing. This course will cover both basic and advance concepts ofPython like writing python scripts, sequence and file operations in python, Machine Learning in Python, Web Scraping, Map Reduce in Python, Hadoop Streaming, Python UDF for Pig and Hive. You will also go through important and most widely used packages like pydoop, pandas, scikit, numpy scipy etc.s

Who should go for this course?:

Experienced Professional or a Beginner, Anyone who wants to learn programming with Python can start right away!

This course is exclusively designed for professionals aspiring to make a career in Big Data Analytics using Python. Software Professionals, Analytics Professionals, ETL developers, Project Managers, Testing Professionals are the key beneficiaries of this course. Other professionals who are looking forward to acquire a solid foundation of this widely-used open source general-purpose scripting language, can also opt for this course.

Pre-requisites:

Although there are no hard pre-requisites, attendees having prior programming experience and familiarity with basic concepts such as variables/scopes, flow-control, and functions would be beneficial. Prior exposure to object-oriented programming concepts is not required, but definitely beneficial.

Why learn Python?

Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python is continued to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations.

Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.

Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.

Python Course Outline

An Overview of Python

• What is Python?
• Interpreted languages
• Advantages and disadvantages
• Downloading and installing
• Which version of Python
• Where to find documentation

The python environment

• Structure of a Python script
• Using the interpreter interactivel
• Running standalone scripts under Unix and Windows

Getting Started

• Using variables
• String types: normal, raw and Unicode
• String operators and expressions
• Math operators and expressions
• Writing to the screen
• Command line parameters
• Reading from the keyboard

Flow Control

• About flow control
• Indenting is significant
• The if and elif statements
• while loops
• Using lists
• Using the for statement
• The range() function

Array types

• list operations
• list methods
• Strings are special kinds of lists
• tuples
• sets
• Dictionaries

Working with Files

• Text file I/O overview
• Opening a text file
• Reading text files
• Raw (binary) data
• Using the pickle module
• Writing to a text file

Dictionaries and Sets

• Dictionary overview
• Creating dictionaries
• Dictionary functions
• Fetching keys or values
• Testing for existence of elements
• Deleting elements

Functions

• Syntax of function definition
• Formal parameters
• Global versus local variables
• Passing parameters and returning values

Sorting

• The sorted() function
• Alternate keys
• Multiple keys
• Lambda functions

Errors and Exception Handling

• Dealing with syntax errors
• Exceptions
• Handling exceptions with try/except
• Cleaning up with finally

Modules and Packages

• What is a module?
• The import statement
• Function aliases
• Packages

Regular Expressions

• RE Objects
• Pattern matching
• Parsing data
• Subexpressions
• Complex substitutions
• RE tips and tricks

Highlights of the Standard Library

• Working with the operating system
• Grabbing web pages
• Sending email
• Using glob for filename wildcards
• math and random
• Accessing dates and times with datetime
• Working with compressed files

An Introduction to Python Classes

• About o-o programming
• Defining classes
• Constructors
• Instance methods
• Instance data
• Class methods and data
• Destructors

Advanced Python

MODULE 1

• Command Line arguments
• Display Hooks
• Standard data streams and Redirections
• OS module
• Sub-process module
• Forking processes
• Exec functions
• Working with comprehensions
• Working with Descriptors, Iterators, Generators and Decorators
• The yield statement
• range and x-range
• Working with Context Managers
• Wrapping Objects
• Callback functions
• Duck Typing, Monkey Patching in Python
• Encapsulating Object Creation: Factory

MODULE 2

• Introduction to Threads in python
• thread module
• threading module
• Introduction to Pipes in python
• anonymous pipes
• named pipes, fifos
• Introduction to Recursion
• Recursive functions in Python
• Depth of Recursion

MODULE 3

• CGI Programming
• Introduction to WSGI
• Introduction to PEP3333
• Bottle Framework
• Flask Framework
• WebTest Framework
• Create a basic Web Service in python
• Working with Databases
• Connecting with Cassandra DB, SQLite3, MySQL
• Database Operations

MODULE 4

• Network Programming
• Working with XML Files
• Developing GUIs
• Working with SMTP
• Integrating Python with other Languages
• Conclusion