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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 18-20 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: 60-66 hours class room program.
  • Lab: 30 hours’ lab sessions + 54 plus exercises + 45 plus assignments (7 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




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
  • 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)


 Data Analytics Using Python (Self Paced Module)

  • Introduction to Anaconda Python
  • Introduction to Numpy Module
  • Machine Learning with Python
  • Plotting with Matplotlib
  • Data Analysis with Pandas

Data Preprocessing with Python

  • Missing Data
  • Categorical Data
  • Splitting the Dataset into the Training set and Test set
  • Feature Scaling

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

Regression Models with Python

Simple Linear Regression

  • Dataset + Business Problem Description
  • Intuition
  • Simple Linear Regression

Multiple Linear Regression

  • Dataset + Business Problem Description
  • Intuition
  • Multiple Linear Regression

Polynomial Regression

  • Intuition
  • Python Regression Template
  • Polynomial Regression

Support Vector Regression (SVR)

  • Intuition
  • SV Regression

Decision Tree Regression

  • Intuition
  • Decision Tree Regression

Random Forest Regression

  • Intuition
  • Random Forest Regression
  • Evaluating Regression Models Performance
  • R-Squared Intuition
  • Adjusted R-Squared Intuition
  • Evaluating Regression Models Performance
  • Interpreting Linear Regression Coefficient

Classification Models

Logistic Regression

  • Intuition
  • Logistic Regression
  • Python Classification Template

K-Nearest Neighbors (K-NN)

  • Intuition
  • K-NN creation

Support Vector Machine (SVM)

  • Intuition
  • SVM

Decision Tree Classification

  • Intuition
  • Decision Tree Classification

Random Forest Classification

  • Intuition
  • Random Forest Classification

Evaluating Classification Models Performance

  • False Positives & False Negatives
  • Confusion Matrix
  • Accuracy Paradox
  • CAP Curve
  • CAP Curve Analysis

Clustering Models

K-Means Clustering

  • K-Means Clustering
  • Intuition
  • Random Initialization Trap
  • Selecting The Number Of Clusters
  • K-Means Clustering

Hierarchical Clustering

  • Intuition
  • Hierarchical Clustering How Dendrograms Work
  • Hierarchical Clustering Using Dendrograms
  • HC

Natural Language Processing

  • Deep Learning
  • Artificial Neural Networks
  • ANN Intuition
  • Building an ANN
  • Business Problem Description
  • Building an ANN
  • [Exercise] Should we say goodbye to that customer?
  • Evaluating, Improving and Tuning the ANN
  • Evaluating the ANN
  • Improving the ANN
  • Tuning the ANN
  • [Exercise] - Put me one step down on the podium
  • Convolutional Neural Networks
  • CNN Intuition
  • Building a CNN
  • Building a CNN
  • [Exercise] - What's that pet?
  • Evaluating, Improving and Tuning the CNN
  • [Exercise] - Get the gold medal
  • Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Model Selection & Boosting
  • Model Selection
  • k-Fold Cross Validation
  • Grid Search

TensorFlow and Machine Learning

  • Introducing TF
  • Lab: Simple Math Operations
  • Computation Graph
  • Tensors
  • Lab: Tensors
  • Image Processing
  • Images As Tensors
  • Lab: Reading and Working with Images
  • Lab: Image Transformations
  • Introducing MNIST
  • Lab: K-Nearest-Neighbors
  • Individual Neuron
  • Learning Regression
  • Learning XOR
  • XOR Trained
  • Regression in TensorFlow
  • Lab: Access Data from Yahoo Finance
  • Non TensorFlow Regression
  • Lab: Linear Regression - Setting Up a Baseline
  • Gradient Descent
  • Lab: Linear Regression
  • Lab: Multiple Regression in TensorFlow
  • Logistic Regression Introduced
  • Linear Classification
  • Lab: Logistic Regression - Setting Up a Baseline
  • Logit
  • Softmax
  • Argmax
  • Lab: Logistic Regression
  • Estimators
  • Lab: Linear Regression using Estimators
  • Lab: Logistic Regression using Estimators

      Mini Projects:

  • Data Analysis of TCS trade share (NSE) using MySQL Database system
  • Data Analysis of IMDB Movies dataset using Pandas Framework
  • Develop Hadoop Mar Reduce to process 10M records from movielens dataset and do analysis
  • Predict Stock and house price using machine learning algorithim and plot graphs 

 Module 4 - Python for Automation Testing (Optional Module)

     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
  • 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)

     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
  • 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