Python Program with Analytics course is aimed at guiding participants on how to program with Python and kick-start the path towards Data Science. In this course, participants will learn to analyze data, create interactive and useful data visualizations. Additionally, this course curriculum empowers participants on learning to use powerful Machine Learning algorithms as an essential foundation for Data Science.

Learn popular Python toolkits such as pandas, matplotlib, scikit-learn, nltk to convert statistical data into readable insights, upscale Web Development and project deployment skills with Django framework. Master skills with our comprehensive course & real lab experience and get a globally recognized certification.

Key Highlights

Introduction to python

• Python History.
• Python Applications.
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Installation Setup and Testing

• Python Install
• Python Path
• Python Example
• Execute Python
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Datatypes, declarations and comments

• Python Variables
• Python Data Types
• Python Keywords
• Python Literals
• Python Comments
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Operators in Python

• Arithmetical Operators
• Relational Operators
• Logical Operators
• Assignment operators
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Conditional Statements

• Simple IF
• If and Else
• Nested If
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Python Loops

• Python for loop
• Python while loop
• Python break
• Python continue
• Python pass
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Python String handling and functions

• capitalize()
• center()
• count()
• endswith()
• format()
• rjust()
• ljust()
• len()
• replace()
• upper()
• lower()
• split()
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Number functions

• abs(), ceil(), floor(), cmp(), exp()
• log(), log10(), min(), max(), power()
• round(), sqrt()

Date Functions

• import datetime module
• now()
• datetime()
• import calendar
• calender.month()
• calendar.prcal(2019)
• import time
• time.time() and more functions followed
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Python Collections

• Python Lists
• Python Tuples
• Python Sets
• Python Dictionary
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Python user define Functions

• Using def keyword to define function
• Function with return keyword
• Function with argument
• Python Lambda Functions
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Advance Python Programming

File Handling in Python

• Python Files I/O
• create file using r , w , a modes
• Python Modules

Python Exceptions

• Types of Exceptions in python
• using try, except keyword
• else usage in exception handling
• usage of the final keyword
• user-defined exceptions
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Python OOPs

• Python OOPs Concepts
• Python Class designing
• Object creation and handling class properties through the object
• Python Constructors and overloading of a constructor
• Method overloading in class
• Python Inheritance with single multi-level and Hierarchy
• Python interfaces and implementation Python with database
• configure database
• connecting to Oracle
• connecting to Mysql
• adding mandatory plugins
• CRUD operations in Python with Database
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Python OS module

• create folder
• remove folder
• rename file
• host os command
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Python network concept

• Client socket
• Server socket
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Threading program in Python

• What is threading
• Activation of thread
• Life cycle of thread
• Thread processing in python
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Django Introduction

• Django Features
• Rapid Development
• Secure
• Scalable
• Fully loaded
• Versatile
• Open Source
• Vast and Supported Community
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Django Installation

• Install Django using pip command
• Verify installation using import django over shell
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Django Project

• Creation of django project
• Locate project
• Run django project
• Apache Configuration
• Virtual Environment Setup
• Admin Interface
• Django App
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Django MVT

• Django Model
• Django View
• Django Template
• URL Mapping
• Static Files Handling
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Form handling

• Model Forms
• Django Forms
• Form Validation
• File Upload Database with Django
• Database Connectivity
• Database Migrations
• Django Middleware
• Request and Response
• Django Exceptions
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Session Tracking

• Django Session
• Django Cookie
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External File Output creation using Django

• Django CSV Output
• Django PDF Output
• Django and Bootstrap
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Additional Features of Django

• Deploy on Github
• Django Mail Setup
• Django Default CRUD
• CRUD Application

• Flask
• Statistical programming with Python Numpy, Pandas, Scikit and Dataframes