Python Training


5 Star Rating: Recommended - Best Python Training in Chennai 77 reviews

Growth happens when you invest time in it. Since its inception in 1994, Python language has reached a mature state now like a “fully grown” Python snake.It is also known for its flexibility (like the real snake) and improved productivity for programmers. Learning Python is definitely a fun-making exercise which you will definitely relish.Given the fact that Python language is gonna rule the roost in the coming years especially in field of data analysis & big data analytics, would you like to grab or miss the opportunity of learning this beautiful language?

PYTHON TRAINING COURSE MODULES


Section 1:Introduction

  • What’s python?
  • Why do people use python?
  • Some quotable quotes
  • A python history lesson
  • Advocacy news
  • What’s python good for?
  • What’s python not good for?
  • The compulsory features list
  • Python portability
  • On apples and oranges
  • Summary: why python?

Section 2: Using the Interpreter

  • How Python Runs Programs
  • How You Run Programs
  • Configuration Details
  • Module Files: A First Look
  • The Idle Interface
  • Other Python Ides
  • Time To Start Coding
  • Lab Session 1

Section 3: Types and Operators

  • A First Pass
  • The ‘Big Picture’
  • Numbers
  • Dynamic Typing Interlude
  • Strings
  • Lists
  • Dictionaries
  • Tuples
  • General Object Properties
  • Summary: Python’s Type Hierarchies
  • Built-In Type Gotchas
  • Lab Session 2

Section 4: Basic Statements

  • General Syntax Concepts
  • Expressions
  • Print
  • If Selections
  • Python Syntax Rules
  • Documentation Sources Interlude
  • Truth Tests
  • While Loops
  • Break, Continue, Pass, And The Loop Else
  • For Loops
  • Comprehensions And Iterations
  • Loop Coding Techniques
  • Comprehensive Loop Examples
  • Basic Coding Gotchas
  • Preview: Program Unit Statements
  • Lab Session 3

Section 5: Functions

  • Function Basics
  • Scope Rules In Functions
  • More On “Global” (And “Nonlocal”)
  • More On “Return”
  • More On Argument Passing
  • Special Argument Matching Modes
  • Odds And Ends
  • Generator Expressions And Functions
  • Function Design Concepts
  • Functions Are Objects: Indirect Calls
  • Function Gotchas
  • Optional Case Study: Set Functions
  • Lab Session 4

Section 6: Modules

  • Module Basics
  • Module Files Are A Namespace
  • Name Qualification
  • Import Variants
  • Reloading Modules
  • Package Imports
  • Odds And Ends
  • Module Design Concepts
  • Modules Are Objects: Metaprograms
  • Module Gotchas
  • Optional Case Study: A Shared Stack Module
  • Lab Session 5

Section 7:Classes

  • Oop: The Big Picture
  • Class Basics
  • A More Realistic Example
  • Using The Class Statement
  • Using Class Methods
  • Customization Via Inheritance
  • Specializing Inherited Methods
  • Operator Overloading In Classes
  • Namespace Rules: The Whole Story
  • Oop Examples: Inheritance And Composition
  • Classes And Methods Are Objects
  • Odds And Ends
  • New Style Classes
  • Class Gotchas
  • Optional Case Study: A Set Class
  • Summary: Oop In Python
  • Lab Session 6

Section 8: Exceptions

  • Exception Basics
  • First Examples
  • Exception Idioms
  • Exception Catching Modes
  • Class Exceptions
  • Exception Gotchas
  • Lab Session 7

Section 9: Built-in Tools Overview

  • The Secret Handshake
  • Debugging Options
  • Inspecting Name-Spaces
  • Dynamic Coding Tools
  • Timing And Profiling Python Programs
  • File Types And Packaging Options
  • Development Tools For Larger Projects
  • Summary: Python Tool-Set Layers
  • Lab Session 7 Continued

Section 10: System Interfaces

  • System Modules Overview
  • Running Shell Commands
  • Arguments, Streams, Shell Variables
  • File Tools
  • Directory Tools
  • Forking Processes
  • Thread Modules And Queues
  • The Subprocess And Multiprocessing Modules
  • Ipc Tools: Pipes, Sockets, Signals
  • Fork Versis Spawnv
  • Larger Examples
  • Lab Session 8

Section 11: GUI Programming

  • Python Gui Options
  • The Tkinter ‘Hello World’ Program
  • Adding Buttons, Frames, And Callbacks
  • Getting Input From A User
  • Assorted Tkinter Details
  • Building Guis By Subclassing Frames
  • Reusing Guis By Subclassing And Attaching
  • Advanced Widgets: Images, Grids, And More
  • Larger Examples
  • Tkinter Odds And Ends
  • Lab Session 8 Continued

 Section 12: Databases and Persistence

  • Databases and Persistence
  • Object Persistence: Shelves
  • Storing Class Instances
  • Pickling Objects Without Shelves
  •  Using Simple Dbm Files
  • Shelve Gotchas
  • Zodb Object-Oriented Database
  • Python Sql Database Api
  • Persistence Odds And Ends
  •  Lab Session 9

 Section 13: Text Processing

  • String Objects: Review
  • Splitting And Joining Strings
  • Regular Expressions
  • Parsing Languages
  • Xml Parsing: Regex, Sax, Dom, And Etree
  • Lab Session 10

Section 14:Internet Scripting

  • Using Sockets In Python
  • The Ftp Module
  • Email Processing
  • Other Client-Side Tools
  • Building Web Sites With Python
  • Writing Server-Side Cgi Scripts
  • Jython: Python For Java Systems
  • Active Scripting And Com
  • Other Internet-Related Tools
  • Lab Session 10

Section 15:Extending Python in C/C++

  • Python Integration Model
  • Review: Python Tool-Set Layers
  • Why Integration?
  • Integration Modes
  • A Simple C Extension Module
  • C Module Structure
  • Binding C Extensions To Python
  • Data Conversions: Python  C
  • C Extension Types
  • Using C Extension Types In Python
  • Wrapping C Extensions In Python
  • Writing Extensions In C++
  • Swig Example (Pp)
  • Python And Rapid Development
  • Lab Session 11

Section 16:Embedding Python in C/C++

  • General Embedding Concepts
  • Running Simple Code Strings
  • Calling Objects And Methods
  • Running Strings: Results &Amp; Name-Spaces
  • Other Code String Possibilities
  • Registering Python Objects And Strings
  • Accessing C Variables In Python
  • C Api Equivalents In Python
  • Running Code Files From C
  • Precompiling Strings Into Byte-Code
  • Embedding Under C++
  • More On Object Reference Counts
  • Integration Error Handling
  • Automated Integration Tools
  • Lab Session 12

Section 17:Advanced Topics

  • Unicode Text And Binary Data
  • Managed Attributes
  • Decorators
  • Metaclasses
  • Context Managers
  • Python 3.X Changes
  • Lab Session 13

Laboratory Exercises

  • Lab 1: Using The Interpreter
  • Lab 2: Types And Operators
  • Lab 3: Basic Statements
  • Lab 4: Functions
  • Lab 5: Modules
  • Lab 6: Classes
  • Lab 7: Exceptions And Built-In Tools
  • Lab 8: System Interfaces And Guis
  • Lab 9: Persistence
  • Lab 10: Text Processing And The Internet
  • Lab 11: Extending Python In C/C++
  • Lab 12: Embedding Python In C/C++
  • Lab 13: Decorators And Metaclasses

History of Python


BBC’s ‘Monty python’,a comedy series,released during the late 1960’s was a huge hit. The Python programming language, released in early 1990’s ,eponymous of the comedy series, turned to be a huge hit too, in the software fraternity. Reasons for the hit runs into a long list – be it the dynamic typing, or cross-platform portability, enforced readability of code, or its ability to take the shape of a scripting or a programming language, or a faster development turnaround.


The power of Python


The power of Python is exploited in development of popular web applications like Youtube,Dropbox & BitTorrent. No wonder that even NASA has used it in space shuttle mission design & in discovery of ‘Higgs-boson’ particles (GOD particles). The rich set of modules available in the language made the top security agency NSA use Python for cryptography. Not to mention that giants like Disney,Sony Dreamworks have used it in game & movie development. Nowadays, given the data is becoming “BIG”, programmers resort to Python for web scraping/Sentiment analysis. Think of ‘Big Data’, the first technology that comes to a programmer’s mind in processing that (ETL & data mining) is Python. www.python.org is the official website of the language. Check out the ‘Success stories’ section and get surprised over the wide domains/sectors that the language is being used.


Learning Python is quite a fun


Thanks to its interactive console, even a person who is getting his feet wet with programming can quickly learn the concepts.
Possessing the features of both the scripting language like TCL,Perl,Scheme & a systems programming language like C++,C,Java, Python is easy to run & code.
Show a Java program and a Python script to a novice programmer – he definitely finds Python code more readable. The Python script is first converted to platform independent byte code making Python a cross platform one. You don’t need to compile & run unlike C,C++ thus making the life of software developer easier.


OOP in Python


The OOP features such as inheritance, polymorphism and encapsulation supports resuability as well. However, OOP is an option in Python. You can still write a simple script to calculate the complex Djikstra’s algorithm for mathematical computation without using OOP. But,if you check the PyPI (Python Package Index) list of available modules, even the Djikstra’s algorithm is available as a module you can plug & play. Such is the comprehensiveness of the modules index.


Databases in Python


Python can interact with all databases incuding SQL databases such as Sybase, Oracle, MySQL and noSQL databases such as mongoDB,couchDB. In fact, the ‘dictionary’ data structure that Python supports is the ideal one for interacting with noSQL database such as mongoDB which processes documents as key-value pair. Web frameoworks written in Python such as Flask,Django facilitates faster web application building & deployment. It is also employed to process unstructured data or ‘BIG DATA’ & business analytics. Notable to mention are Web scraping/Sentiment analysis, data science, text mining. It is also used with R language in statistical modelling given the nice visualization libraries it supports, such as Seaborn, Bokeh and Pygal. If you’re used to working with Excel, learn how to get the most out of Python’s higher level data structures to enable super efficient data manipulation and analysis.


Importance of Python


Companies of all sizes and in all areas — from the biggest investment banks to the smallest social/mobile web app startups — are using Python to run their business and manage their data, especially because of its OSI-approved open source license and the fact that it can be used for free. Python is not an option anymore but rather a de facto standard for programmers & data scientists.


KEY FEATURES

  • Professional approach towards training using latest techniques
  • Minimal batch strength to give individual attention to all
  • Unlimited lab / practice environment access provided to all candidates
  • Flexible batch schedule – Attend missed sessions with next batch
  • We provide free placement assistance because we care about your career

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Python Training Reviews

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Very well balanced and professionally delivered

I joined in CREDO SYSTEMZ to learn Python. Trainer Iyyappan is having Vast knowledge in Python. He is very cool and good in explaining the concepts.I have gained a clear and in-depth knowledge on the Python.Thank you CREDO SYSTEMZ and Mr.Iyyappan

Nazer

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