Advance PYTHON Content

Advance Python Overview

Python Modules

Packages in Python

Python Date and Time

File Handling

Python OS Module

Python Exception Handling

More Advanced PYTHON

Python Class and Objects

Python Regular Expressions

Python XML Parser

Python Database Communication

Multi-Threading

Web Scrapping

Unit Testing with PyUnit

Introduction to Python Web Frameworks

GUI Programming-Tkinter

Data Analytics

Introduction to Machine Learning with PYTHON

Data Science

Internet of Things

Project Development

Python Course Overview

Introduction

• Why do we need Python?
• Features of Python Language
• Interactive Shell
• Executable or script files.
• User Interface or IDE
• Program structure
• Variables, Operators, and Datatypes
• Operators
• Execution steps

Conditional Statements 

• Using of If statement
• if-else statement
• Nested if-else statement

Loops

• for loop
• while loop
• do while loop
• break, continue and pass

Strings

• Accessing a string
• Basic operations with strings
• String Functions and Methods
• String slices

Lists

• Introduction to lists
• Accessing a List
• Working with lists
• List Functions
• List Methods

Tuples

• What is tuple?
• Creating tuples
• Operations with tuples
• Tuples Functions and Methods

Dictionary

• What is a Dictionary?
• Creating Dictionaries
• Working with Dictionaries
• Dictionary properties
• Dictionary Functions

Functions

• Function Definition
• Calling a Function
• Function Types
• Function Arguments
• Global and Local Variables
• Anonymous Functions

Modules

• What is a module
• Importing a module
• Math and Random module
• Composition
• Packages

OOPS Concepts

• Classes and objects
• Creating a Class and an Object
• Attributes
• Inheritance
• Overriding
• Overloading
• Encapsulation
• Packages

Regular Expressions

• Search Function
• Match Function
• Searching vs Matching
• Modifiers
• Patterns

Multithreading

• What is a thread?
• Starting a thread
• Thread Modules
• Multithread priority
• Synchronizing threads

Exception Handling

• What are exceptions
• Exception Handling
• Raise an Exception
• User-defined Exception
• Except clause
• Try and finally clause

Advanced Concepts

• Decorators
• Generators
• Iterators
• Co-routines
• Standard Library Modules
• References

Exercises

• Roadmap with Python

Certification training for python

• Our certification Course in Python makes you learn from scratch. Our training experts match your needs and expectations. We build strong fundamentals and make comfortable in python programming. Each and every part of the course will be taken in the utmost care. Our training methods include beginners, intermediate and advanced training.

• This program will help you to master in very important programming concepts such as Lists, Tuples, Dictionary, Functions, Modules, Object-Oriented Programming concepts, Multi-threading, Exceptions and so on.

Interesting and Important Facts About Python 

• There are 1, 47,000 packages in python package repository which makes python more popular and versatile.

• We can use Python for Web Development, Data Visualization, Data science, Game Development, and DevOps.
• Python has become the choice for AI researchers. It provides a very strong platform for AI Development
• According to Research, Python is growing very rapidly for the past few years.

Data science using Python

Python is largely used for data analysis. There are mainly five libraries available to perform data analysis.  Numpy, Pandas, SciPy, Matplotlib and Scikit-learn.

• Numpy is used for scientific computing. It facilitates with multi-dimensional arrays and matrices. It also helps us to perform high-level mathematical functions to operate on those arrays.
• Pandas offer data structures and used to perform manipulating Numerical tables and time series.
• Scipy is used for numerical integration and optimization.
• Matplotlib is for 2d Plotting. It is used to generate data visualizations,  bar charts, scatter plots.
• Scikit-learn is used for machine learning. It supports vector machines, naïve Bayes, logistic regression and gradient boosting.

NumPy 

• Numpy stands for Numeric python, it is an open source. It is a package in python. Numpy library is mainly used to process multi-dimensional arrays objects.

• The main operations can be performed by using are Mathematical and logical operations on arrays.
• Operations related to linear algebra. It has a built-in function for linear algebra and for generating random numbers.
• It is used along with the packages SciPy and Mat-plotlib. All these combinations are an alternative to MatLab.

Pandas 

• Pandas is a library for python software and Pandas stands for Python Data Analysis.
• Its name is derived from Word Panel Data.
• Its main priority is data munging. By using it we can load, prepare, manipulate, model and analyze data.
• In Windows, if we Install Anaconda Package, Pandas will be installed.

Scipy

• It is an Open-Source Library mainly used for Scientific computing.
• It supports special functions, ODE, Integration, and  Parallel Programming tools.
• It is very easy to use and can operate on an array of NumPy library.

Matplotlib

• It is a library in Python for plotting 2D graphs by using python scripts. It supports bar charts, error charts, histogram, and power spectra.
• It can also be used with wxPython and PyQt. Using Matplotlib with Numpy is an alternative MatLab.

scikit-learn

• It is an Open source library provides many supervised and unsupervised algorithms.

• It includes Regression, Classification, Clustering, Model Selection, and Preprocessing.