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Python Programming

python

 

About the Course

 

EthansTech Provides expert training on Weekends and Weekdays on various Python streams

  • Python for Software Automation
  • Python for Data Science/Analytics
  • Python for Web development
  • Python for Devops
  • Python for Cloud & Networking

Students can choose any of the module. Please refer syllabus in next tab.

This course is expected to take two months with total 15-16 classes, each class is having three-four hours training. It can take lesser time if the number of hours per day is increased.

  • No pre-requisite required for the classes . Appreciate to have the basic knowledge of any programming language.
  • Duration: 48 hours class room program.
  • Lab: 30 hours’ lab sessions + 54 plus exercises + 45 plus assignments (2 Mini Projects)
  • Live Projects and 4 POC's
  • Ethans Copyright Python Study Material

After the classes: Students will easily crack Python interview and have advance knowledge of Python Automation and Data Science with Python

Who get this training Python?

  • Automation Engineers
  • Data analysts and scientist
  • Quality Analysts
  • DevOps
  • System Administrator
  • Networking Professionals
  • Hadoop programmers
  • Robotics Engineers
  • Hardware level developers
  • UI Developers

 

Syllabus

 Syllabus:

Module-1:   Python Foundation (Mandatory)

  • What is Python and history of Python?
  • Why Python and where to use it?
  • Discussion about Python 2 and Python 3
  • Set up Python environment for development
  • Demonstration on Python Installation
  • Discuss about IDE’s like IDLE, Pycharm and Enthought Canopy
  • Discussion about unique feature of Python
  • Write first Python Program
  • Start programming on interactive shell.
  • Using Variables, Keywords
  • Interactive and Programming techniques
  • Comments and document interlude in Python
  • Practical use cases using data analysis
  • Introduction to Hadoop

       Core Objects and Built-in Functions

  • Python Core Objects and builtin functions
  • Number Object and operations
  • String Object and Operations
  • List Object and Operations
  • Tuple Object and operations
  • Dictionary Object and operations
  • Set object and operations
  • Boolean Object and None Object
  • Different data Structures, data processing

      Conditional Statements and Loops

  • What are conditional statements?
  • How to use the indentations for defining if, else, elif block
  • What are loops?
  • How to control the loops
  • How to iterate through the various object
  • Sequence and iterable objects

     UDF Functions and Object Functions

  • What are various type of functions
  • Create UDF functions
  • Parameterize UDF function, through named and unnamed parameters
  • Defining and calling Function
  • The anonymous Functions - Lambda Functions
  • String Object functions
  • List and Tuple Object functions
  • Dictionary Object functions

      File Handling with Python

  • Process text files using Python
  • Read/write and Append file object
  • File object functions
  • File pointer and seek the pointer
  • Truncate the file content and append data
  • File test operations using os.path

Module-2 – Python Advance (Mandatory)

  • Python inbuilt Modules
  • os, sys, datetime, time, random, zip modules
  • Create Python UDM – User Defined Modules
  • Define PYTHONPATH
  • Create Python Packages
  • init File for package initialization

      Exceptional Handing and Object Oriented Python

  • Python Exceptions Handling
  • What is Exception?
  • Handling various exceptions using try....except...else
  • Try-finally clause
  • Argument of an Exception and create self exception class
  • Python Standard Exceptions
  • Raising an exceptions,    User-Defined Exceptions   
  • Object oriented features
  • Understand real world examples on OOP
  • Implement Object oriented with Python
  • Creating Classes and Objects,   Destroying Objects
  • Accessing attributes,   Built-In Class Attributes
  • Inheritance and Polymorphism
  • Overriding Methods,   Data Hiding
  • Overloading Operators

     Debugging, Framework & Regular expression

  • Debug Python programs using pdb debugger
  • Pycharm Debugger
  • Assert statement for debugging
  • Testing with Python using UnitTest Framework
  • What are regular expressions?
  • The match and search Function
  • Compile and matching
  • Matching vs searching
  • Search and Replace feature using RE
  • Extended Regular Expressions
  • Wildcard characters and work with them

     Database interaction with Python

  • Creating a Database with SQLite 3,
  • CRUD Operations,
  • Creating a Database Object.
  • Python MySQL Database Access
  • DML and DDL Operations with Databases
  • Performing Transactions
  • Handling Database Errors
  • Disconnecting Database

     Package Installation, Windows spreadsheet parsing and webpage scrapping

  • Install package using Pycharm
  • What is pip, easy_install  
  • Set up the environment to install packages?
  • Install packages for XLS interface and XLS parsing with Python
  • Create XLS reports with Python
  • Introduction to web scraping and beautiful soup

Module 3 - Python for Artificial Intelligence (Optional Module,Will be conducted Seperately) - Duration 50 Hours

Data Preprocessing

  • Python and Data preprocessing (Crash Course - Self paced)

    1. Python Fundamentals
    2. Numpy
    3. Pandas
    4. Data Visualization
    5. Scikit Learn
    6. Data Preprocessing

Using Git and GitHub

  • Setting up Your GitHub Account
  • Configuring Your First Git Repository
  • Making Your First Git Commit
  • Pushing Your First Commit to GitHub
  • Git and GitHub Workflow Step-by-Step

Machine Learning

Regression

  1. Simple Linear Regression
  2. Multiple Linear Regression
  3. Bias-Variance trade-off

Classification

  1. Logistic Regression
  2. K-Nearest Neighbors (K-NN)
  3. SVM
  4. Decision Trees
  5. Random Forest

Clustering

  1. K-means
  2. Hierarchical
  3. DBSCAN

Dimensionality Reduction

  1. Linear discriminant analysis
  2. Principal component analysis

Natural Language Processing

Natural Language Processing

NLTK

  1. NLP with NLTK
  2. NLTK extensions and exploration

Sentiment Analyzer

  1. Description of Sentiment Analyzer
  2. Preprocessing: Tokenization
  3. Preprocessing: Tokens to Vectors
  4. Sentiment Analysis using Logistic Regression
  5. Sentiment Lexicons
  6. Regular Expressions
  7. Twitter Sentiment Analysis
  8. Twitter Sentiment Analysis - Regular Expressions
  9. Twitter Sentiment Analysis - KNN, Decision trees, Random forests and Sentiwordnet

Latent Semantic Analysis

  1. Intro to Latent Semantic Analysis
  2. PCA and SVD - The underlying math behind LSA
  3. Latent Semantic Analysis in Python
  4. Advanced LSA

Article spinner

  1. Article Spinning Introduction and Markov Models
  2. Trigram Model
  3. Article spinner in Python

Tensorflow and Neural Networks

Tensorflow

  1. Introducing TF
  2. Computation Graph
  3. Tensors
  4. Placeholders and Variables
  5. Neural Networks
  6. Perceptron
  7. Activation Functions
  8. Cost Functions
  9. Gradient Descent Backpropagation

Artificial Neural Networks

  1. Regression in TF
  2. Regression in TF and NN using Estimator
  3. Regression in TF and NN using Keras
  4. Classification in TF
  5. Classification in TF and NN using Estimator
  6. Classification in TF and NN using Keras

Computer Vision

Convolutional Neural Networks

  1. Intro to CNN
  2. Convolution Operation
  3. Activation Layer
  4. Pooling
  5. Flattening
  6. Full Connection
  7. Softmax, Argmax & Cross-Entropy

Basics of Computer Vision and OpenCV

  1. Image Formation
  2. Getting Started with OpenCV
  3. Understanding Color Spaces
  4. Histogram representation of Images
  5. Image Manipulations
  6. Live Sketch App
  7. Identifying Shapes
  8. Counting Circles and Ellipses

Object Detection

  1. Object Detection Overview
  2. How SSD is different
  3. The Multi-Box Concept
  4. Predicting Object Positions
  5. The Scale Problem
  6. Feature Description Theory
  7. Finding Corners
  8. SIFT, SURF, FAST, BRIEF and ORB
  9. Histogram of Oriented Gradients
  10. Hands-on Object Detection

Face Detection

  1. Face and Eye Detection
  2. Viola-Jones Algorithm
  3. Haar-like Features
  4. Integral Image
  5. Training Classifiers
  6. Adaptive Boosting (Adaboost)
  7. Cascading
  8. Merging Faces (Face Swaps)
  9. Yawn Detector and Counter
  10. Facial Recognition

Motion Analysis and Object Tracking (Ball Tracking)

  1. Filtering by Color
  2. Background Subtraction and Foreground Subtraction
  3. Using Meanshift for Object Tracking
  4. Using CAMshift for Object Tracking
  5. Optical Flow

Module 4 - Python for Automation Testing (Optional Module,Will be conducted Seperately) - Duration 18 Hours

     Introduction to Automation Testing

  • Introduction to testing.
  • Testing concepts.
  • Need of automation
  • Types of automation Frameworks.
  • UI Automation - Selenium Library
  •         -Navigating
  •         -Locating Elements
  •         -Waits
  • Basics of APIs 
  • Different types of APIs
  • API AUTOMATION - Request Module

      Python UNIT TEST Framework

  • Basic Test Structure
  • Running Tests Cases
  • Test Outcomes
  • Types of Asserts statement 
  • Intrudction to Test Fixtures
  • Introduction to Test Suites
  •  Test Discovery with UNIT Test Framework

      Python Nose Framework 

  • Installation
  • Running nose
  • Nose fixtures
  • Testing markdown.py
  • Nose assert_equals
  • Test discovery
  • Running unittests from nose
  • Running doctests from nose
  • Integration of Nose with HTML

      Python ROBOT Framework - RIDE

  • Introduction to Robot Framework
  • Architecture
  • Test Libraries
  • Installation and Configuration of Robot Framework and Ride
  • Suite Test Setup and Teardown
  • Tags: Tags for individual Testcases, Force Tags for Suite Level. Include or Exclude Tests or Suites based on the Tags
  • Reports & Logs – Creating reports with customized file names, Creating Reports with Specified Titles
  • Write Keywords in RF actually implemented in Python scripts

Module 5 - Python for Web Development - Django (Optional Module,Will be conducted Seperately) - Duration 24 Hours

     Django Introduction, Installation and Setup

  • Introduction to Django Framework
  • Django Principles
  •  Install and create virtualenv
  • Install Django and production ready setup.

     Django Project, Concept and Demo

  • Creating A New project
  • Running the Development Server
  • Django Apps
  • URLs and Views
  • URL Mapping -- emphasis on Python regex
  • HTTP protocol Fundamentals
  • Django Views -- render/HttpResponse Method
  • Django Templates
  • Static Files -- CSS, JavaScript and Images
  • Model, Template and View (MTV) Design Pattern

       Models in Detail

  • Django Model Classes -- SQL Mapping
  • Field Types
  • Generating Databases
  • SQL Queries
  • Manage.py Database Commands
  • Django Admin Interface -- superuser
  • Implement __str__ for your Model Classes
  • The Model API
  • SAVE and Delete

     Views and Templates in Detail

  • Adding Login and Logout Views
  • A template for the Home Page
  • Authorization with Django
  • Overview of all HTML Elements
  • CSS Overview
  • Templates: Tags and Variables
  • Adding the HOME View
  • URL Mapping for APPS
  • Template Inheritance
  • Login required -- Handling issues with Login using decorators
  • Template Context
  • Templates - For, Include

      Forms in Detail

  • Django Forms -- Model Class
  • Views and Forms
  • Templates and Forms -- csrf_token tag
  • Styling forms using django-crispy- forms
  • Verbose Name for display in forms
  • Help Text to show the text to help the user
  • Make a Field nullable -- null=True
  • Allow empty text Field -- blank=True
  • Showing Invitation
  • Accepting Invitation
  • Named Groups in URLs
  • Fat Models, skinny views
  • URLs: Reverse and get_absolute_url

     Micro services, Rest API/Framework and Test Cases.

  • Micro-services, concept and architecture in detail.
  • Writing Micro-services.
  • Rest Framework and API, concept.
  • Writing Rest Services, sending and receiving JSON Data
  • Writing Test Cases and Automated Testing Framework.
  • Mini Projects
  • Creating a complete User Profile and Home Page.
  • Creating and Managing Poll Application.
  • Tic-Tac- Toe Game.
  • (Assignment and Live Examples)
  • Sample resumes helping you to create your resume
  • Hard copy study material
  • Access to Dropbox and whatapp group of students