Python Training in Chennai

Core Python

Introduction to Script

• What is Script

• What is a program?

• Types of Scripts

• Difference between Script & Programming Languages

• Features of Scripting

• Limitation of Scripting

• Types of programming Language Paradigms

Introduction to Python

• What is Python?

• Why Python?

• Who Uses Python?

• Characteristics of Python

• History of Python

• What is PSF?

• Python Versions

• How to Download Python

• How to Install Python

• Install Python with Diff IDEs

• Features of Python

• Limitations of Python

• Python Applications

• Creating Your First Python Program

• Printing to the Screen

• Reading Keyboard Input

• Using Command Prompt and GUI or IDE

• Python Distributions

Different Modes in PYTHON

• Execute the Script

• Interactive Mode

• Script Mode

• Python File Extensions

• SETTING PATH IN Windows

• Clear screen inside python

• Learn Python Main Function

• Python Comments

• Quit the Python Shell

• Shell as a Simple Calculator

• Order of operations

• Multiline Statements

• Quotations in Python

• Python Path Testing

• Joining two lines

• Python Implementation Alternatives

• Python Sub Packages

• Uses of Python in Data Science

• USES OF PYTHON IN IOT

• Working with Python in Unix/Linux/Windows/Mac/Android..!!

PYTHON NEW IDEs

• PyCharm IDE

• How to Work on PyCharm

• PyCharm Components

• Debugging process in PyCharm

• PYTHON Install Anaconda

• What is Anaconda?

• Coding Environments

• Spyder Components

• General Spyder Features

• Spyder Shortcut Keys

• Jupyter Notebook

• What is Conda?

• Conda List?

• Jupyter and Kernels

• What is PIP?

Variables in Python

• What is Variable?

• Variables in Python

• Constants in Python

• Variable and Value

• Variable names

• Mnemonic Variable Names

• Values and Types

• What Does “Type” Mean?

• Multiple Assignment

• Python different numerical types

• Standard Data Types

• Operators and Operands

• Order of Operations

• Swap variables

• Python Mathematics

• Type Conversion

• Mutable Versus Immutable Objects

String Handling

• What is string?

• String operations

• String indices

• Basic String Operations

• String Functions, Methods

• Delete a string

• String Multiplication and concatenation

• Python Keywords

• Python Identifiers

• Python Literals

• String Formatting Operator

• Structuring with indentation in Python

• Built-in String Methods

• Define Data Structure?

• Data Structures in PYTHON

Python Operators and Operands

• Arithmetic Operators

• Relational Operators

• Comparison Operators

• Python Assignment Operators

• Short hand Assignment Operators

• Logical Operators or Bitwise Operators

• Membership Operators

• Identity Operators

• Operator precedence

• Evaluating Expressions

Python Conditional Statements

• How to use “if condition” in conditional structures

• if statement (One-Way Decisions)

• if .. else statement (Two-way Decisions)

• How to use “else condition”

• if .. elif .. else statement (Multi-way)

• When “else condition” does not work

• How to use “elif” condition

• How to execute conditional statement with minimal code

• Nested IF Statement

Python LOOPS

• How to use “While Loop”

• How to use “For Loop”

• How to use For Loop for set of other things besides numbers

• Break statements in For Loop

• Continue statement in For Loop

• Enumerate function for For Loop

• Practical Example

• How to use for loop to repeat the same statement over and again

• Break, continue statements

Learning Python Strings

• Accessing Values in Strings

• Various String Operators

• Some more examples

• Python String replace() Method

• Changing upper and lower case strings

• Using “join” function for the string

• Reversing String

• Split Strings

Sequence or Collections in PYTHON

• Strings

• Unicode Strings

• Lists

• Tuples

• buffers

• xrange

Python Lists

• Lists are mutable

• Getting to Lists

• List indices

• Traversing a list

• List operations

• List slices

• List methods

• Map, filter and reduce

• Deleting elements

• Lists and strings

Python TUPLE

• Advantages of Tuple over List

• Packing and Unpacking

• Comparing tuples

• Creating nested tuple

• Using tuples as keys in dictionaries

• Deleting Tuples

• Slicing of Tuple

• Tuple Membership Test

• Built-in functions with Tuple

• Dotted Charts

Python Sets

• How to create a set?

• Iteration Over Sets

• Python Set Methods

• Python Set Operations

• Union of sets

• Built-in Functions with Set

• Python Frozenset

Python Dictionary

• How to create a dictionary?

• PYTHON HASHING?

• Python Dictionary Methods

• Copying dictionary

• Updating Dictionary

• Delete Keys from the dictionary

• Dictionary items() Method

• Sorting the Dictionary

• Python Dictionary in-built Functions

• Dictionary len() Method

• Variable Types

• Python List cmp() Method

• Dictionary Str(dict)

Python Functions

• What is a function?

• How to define and call a function in Python

• Types of Functions

• Significance of Indentation (Space) in Python

• How Function Return Value?

• Types of Arguments in Functions

• Default Arguments

• Non-Default Arguments

• Keyword Arguments

• Non-keyword Arguments

• Arbitrary Arguments

• Rules to define a function in Python

• Various Forms of Function Arguments

• Scope and Lifetime of variables

• Nested Functions

• Call By Value, Call by Reference

• Anonymous Functions/Lambda functions

• Passing functions to function

• map(), filter(), reduce() functions

• What is a Docstring?

Advanced Python

Python Modules

• What is a Module?

• Types of Modules

• The import Statement

• The from…import Statement

• ..import * Statement

• Underscores in Python

• The dir( ) Function

• Creating User defined Modules

• Command line Arguments

• Python Module Search Path

Packages in Python

• What is a Package?

• Introduction to Packages?

• py file

• Importing module from a package

• Creating a Package

• Creating Sub Package

• Importing from Sub-Packages

• Popular Python Packages

Python Date and Time

• How to Use Date & DateTime Class

• How to Format Time Output

• How to use Timedelta Objects

• Calendar in Python

• datetime classes in Python

• How to Format Time Output?

• The Time Module

• Python Calendar Module

• Python Text Calendar

• Python HTML Calendar Class

• Unix Date and Time Commands

File Handling

• What is a data, Information File?

• File Objects

• File Different Modes

• file Object Attributes

• How to create a Text File

• How to Append Data to a File

• How to Read a File

• Closing a file

• Read, read line ,read lines, write, write lines…!!

• Renaming and Deleting Files

• Directories in Python

• Working with CSV files

• Working with CSV Module

• Handling IO Exceptions

Python OS Module

• Shell Script Commands

• Various OS operations in Python

• Python File System Shell Methods

Python Exception Handling

• Python Errors

• Common RunTime Errors in PYTHON

• Abnormal termination

• Chain of importance Of Exception

• Exception Handling

• Try … Except

• Try .. Except .. else

• Try … finally

• Argument of an Exception

• Python Custom Exceptions

• Ignore Errors

• Assertions

• UsingAssertionsEffectively

More Advanced PYTHON

• Python Iterators

• Python Generators

• Python Closures

• Python Decorators

• Python @property

Python Class and Objects

• Introduction to OOPs Programming

• Object Oriented Programming System

• OOPS Principles

• Define Classes

• Creating Objects

• Class variables and Instance Variables Constructors

• Basic concept of Object and Classes

• Access Modifiers

• How to define Python classes

• Python Namespace

• Self-variable in python

• Garbage Collection

• What is Inheritance? Types of Inheritance?

• How Inheritance works?

• Python Multiple Inheritance

• Overloading and Over Riding

• Polymorphism

• Abstraction

• Encapsulation

• Built-In Class Attributes

Python Regular Expressions

• What is Regular Expression?

• Regular Expression Syntax

• Understanding Regular Expressions

• Regular Expression Patterns

• Literal characters

• Repetition Cases

• Example of w+ and ^ Expression

• Example of \s expression in re.split function

• Using regular expression methods

• Using re.match()

• Finding Pattern in Text (re.search())

• Using re.findall for text

• Python Flags

• Methods of Regular Expressions

Python XML Parser

• What is XML?

• Difference between XML and HTML

• Difference between XML and JSON and Gson

• How to Parse XML

• How to Create XML Node

• Python vs JAVA

• XML and HTML

Python-Data Base Communication

• What is Database? Types of Databases?

• What is DBMS?

• What is RDBMS?

• What is Big Data? Types of data?

• Oracle

• MySQL

• SQL server

• DB2

• Postgre SQL

• Executing the Queries

• Bind Variables

• Installing of Oracle Python Modules

• Executing DML Operations..!!

Multi-Threading

• What is Multi-Threading

• Threading Module

• Defining a Thread

• Thread Synchronization

Web Scrapping

• The components of a web page

• BeautifulSoup

• Urllib2

• HTML,CSS,JS,jQuery

• Dataframes

• PIP

• Installing External Modules Using PIP

Unit Testing with PyUnit

• What is Testing?

• Types of Testings and Methods?

• What is Unit Testing?

• What is PyUnit?

• Test scenarios, Test Cases, Test suites

Introduction to Python Web Frameworks

• Django – Design

• Advantages of Django

• MVC and MVT

• Installing Django

• Designing Web Pages

• HTML5, CSS3, AngularJS

• PYTHON Flask

• PYTHON Bottle

• PYTHON Pyramid

• PYTHON Falcon

GUI Programming-Tkinter

• Introduction

• Components and Events

• Adding Controls

• Entry Widget, Text Widget, Radio Button, Check Button

• List Boxes, Menus, ComboBox

Data Analytics

• Introduction to data Big Data?

• Introduction to NumPY and SciPY

• Introduction to Pandas and MatPlotLib

Introduction to Machine Learning with PYTHON

• What is Machine learning?

• Machine Learning Methods

• Predictive Models

• Descriptive Models

• What are the steps used in Machine Learning?

• What is Deep Learning?

Data Science

• What is Data Science?

• Data Science Life Cycle?

• What is Data Analysis

• What is Data Mining

• Analytics vs Data Science

Internet of Things

• IMPACT OF THE INTERNET

• What is IOT

• History of IoT

• What is Network?

• What is Protocol?

• What is smart?

• How IoT Works?

• The Future of IoT

Introduction to Python

Learning Objectives: You will get a brief idea of what Python is and touch on the basics.

Topics:

• Overview of Python
• The Companies using Python
• Different Applications where Python is used
• Discuss Python Scripts on UNIX/Windows
• Values, Types, Variables
• Operands and Expressions
• Conditional Statements
• Loops
• Command Line Arguments
• Writing to the screen

Hands On/Demo:

• Creating “Hello World” code
• Variables Demonstrating Conditional Statements
• Demonstrating Loops

Skills:

Fundamentals of Python programming

Deep Dive – Functions, OOPs, Modules, Errors and Exceptions

Learning Objectives: In this Module, you will learn how to create generic python scripts, how to address errors/exceptions in code and finally how to extract/filter content using regex.

Topics:

• Functions
• Function Parameters
• Global Variables
• Variable Scope and Returning Values
• Lambda Functions
• Object-Oriented Concepts
• The Import Statements
• Module Search Path
• Package Installation Ways
• Errors and Exception Handling Handling Multiple Exceptions

Hands On/Demo:

• Functions - Syntax, Arguments, Keyword Arguments, Return Values
• Sorting - Sequences, Dictionaries, Limitations of Sorting
• Packages and Module - Modules, Import Options, sys Path
• Lambda - Features, Syntax, Options, Compared with the Functions
• Error and Exception management in Python

Skills:

• Errors and Exceptions - Types of Issues, Remediation
• Working with functions in Python

Data Manipulation

Learning Objective: Through this Module, you will understand in detail about Data Manipulation

Topics:

• Basic Functionalities of a data object
• Merging of Data objects Concatenation of data objects
• Types of Joins on data objects
• Exploring a Dataset
• Analysing a dataset

Hands On/Demo:

• Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()
• GroupBy operations
• Aggregation
• Concatenation

Skills:

Python in Data Manipulation

Introduction to Machine Learning with Python

Learning Objectives: In this module, you will learn the concept of Machine Learning and its types.

Topics:

• Python Revision (numpy, Pandas, scikit learn, matplotlib)
• What is Machine Learning?
• Machine Learning Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories
• Linear regression Gradient descent

Hands On/Demo:

• Linear Regression – Boston Dataset

Skills:

Machine Learning concepts
• Machine Learning types Linear Regression Implementation

Supervised Learning - I

Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.

Topics:

• What are Classification and its use cases?
• What is Decision Tree? Algorithm for Decision Tree Induction
• Creating a Perfect Decision Tree

Hands On/Demo:

• Implementation of Logistic regression
• Decision tree
• Random forest

Skills:

• Supervised Learning concepts Implementing different types of Supervised Learning algorithms
• Evaluating model output

Dimensionality Reduction

Learning Objectives: In this module, you will learn about the impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress dimensions. Also, you will be developing LDA model.

Topics:

• Introduction to Dimensionality
• Why Dimensionality Reduction
• PCA
• Factor Analysis
• LDA

Hands-On/Demo:

• PCA
• Scaling

Skills:

• Implementing Dimensionality Reduction Technique

Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.

Topics:

• What is Naïve Bayes?
• How Naïve Bayes works?
• Implementing Naïve Bayes Classifier
• What is Support Vector Machine?
• Illustrate how Support Vector Machine works?
• Hyperparameter Optimization
• Grid Search vs Random Search Implementation of Support Vector Machine for Classification

Hands-On/Demo:

Implementation of Naïve Bayes, SVM

Skills:

• Supervised Learning concepts Implementing different types of Supervised Learning algorithms
• Evaluating model output

Unsupervised Learning

Learning Objectives: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.

Topics:

• What is Clustering & its Use Cases?
• What is K-means Clustering? How does K-means algorithm work?
• How to do optimal clustering What is C-means Clustering?
• What is Hierarchical Clustering? How Hierarchical Clustering works?

Hands-On/Demo:

Skills:

Unsupervised Learning Implementation of Clustering – various types

Association Rules Mining and Recommendation Systems

Learning Objectives: In this module, you will learn Association rules and their extension towards recommendation engines with Apriori algorithm.

Topics:

• What are Association Rules?
• Association Rule Parameters Calculating Association Rule Parameters
• Recommendation Engines
• How does Recommendation Engines work?
• Content-Based Filtering
• Collaborative Filtering

Hands-On/Demo:

• Apriori Algorithm
• Market Basket Analysis

Skills:

• Data Mining using python
• Recommender Systems using python

Reinforcement Learning

Learning Objectives: In this module, you will learn about developing a smart learning algorithm such that the learning becomes more and more accurate as time passes by. You will be able to define an optimal solution for an agent based on agent-environment interaction.

Topics:

• What is Reinforcement Learning
• Why Reinforcement Learning Elements of Reinforcement Learning
• Exploration vs Exploitation dilemma
• Q values and V values
• Q – Learning α values

Hands-On/Demo:

• Calculating Reward
• Discounted Reward
• Calculating Optimal quantities
• Implementing Q Learning Setting up an Optimal Action

Skills:

• Implement Reinforcement Learning using python
• Developing Q Learning model in python

Time Series Analysis

Learning Objectives: In this module, you will learn about Time Series Analysis to forecast dependent variables based on time. You will be taught different models for time series modeling such that you analyze a real time-dependent data for forecasting.

Topics:

• What is Time Series Analysis?
• Importance of TSA
• Components of TSA
• White Noise
• AR model
• MA model
• ARMA model
• ARIMA model
• Stationarity
• ACF & PACF

Hands on/Demo:

• Checking Stationarity Converting a non-stationary data to stationary
• Implementing Dickey-Fuller Test
• Plot ACF and PACF
• Generating the ARIMA plot
• TSA Forecasting
• TSA in Python

Model Selection and Boosting

Learning Objectives: In this module, you will learn about selecting one model over another. Also, you will learn about Boosting and its importance in Machine Learning. You will learn on how to convert weaker algorithms into stronger ones.

Topics:

• What is Model Selection?
• The need for Model Selection
• Cross-Validation
• What is Boosting?
• How Boosting Algorithms work?
• Types of Boosting Algorithms Adaptive Boosting

Hands on/Demo:

• Cross-Validation
• AdaBoost

Skills:

• Model Selection
• Boosting algorithm using python<

Sequences and File Operations

Learning Objectives: Learn different types of sequence structures, related operations and their usage. Also learn diverse ways of opening, reading, and writing to files.

Topics:

• Python files I/O Functions
• Numbers
• Strings and related operations
• Tuples and related operations
• Lists and related operations
• Dictionaries and related operations Sets and related operations

Hands on/Demo:

• Tuple - properties, related operations, compared with a list

  Hands on/Demo:

  Dictionary - properties, related operations

  Hands on/Demo:

  List - properties, related operations Set - properties, related operations

Skills:

• File Operations using Python

  Hands on/Demo:

  Working with data types of Python

Introduction to NumPy, Pandas and Matplotlib

Learning Objectives: This Module helps you get familiar with basics of statistics, different types of measures and probability distributions, and the supporting libraries in Python that assist in these operations. Also, you will learn in detail about data visualization.

Topics:

• NumPy - arrays
• Operations on arrays
• Indexing slicing and iterating
• Reading and writing arrays on files
• Pandas - data structures & index operations
• Reading and Writing data from Excel/CSV formats into Pandas
• matplotlib library
• Grids, axes, plots
• Markers, colours, fonts and styling
• Types of plots - bar graphs, pie charts,
• histograms
• Contour plots

Hands On/Demo:

• NumPy library- Creating NumPy array, operations performed on NumPy array
• Matplotlib - Using Scatterplot, histogram, bar graph, pie chart to show information, Styling of Plot
• Pandas library- Creating series and dataframes, Importing and exporting data

Skills

• Probability Distributions in Python
• Python for Data Visualization

 Section1: Introduction

• What is and why Python?

• What is Why Selenium?

• Python with Selenium basics

• Understanding Program structure

Section2: Environment Setup

• Install Python on Windows and Mac OS

• Install Selenium for Python

• To setup Selenium IDE

• To setup Pycharm and PyDev IDE for Python

Section3: Understanding Python Concepts

• Data types

• Variables

• Syntax

• Looping

• Functions

• Modules

• Classes

• Objects

• Collections

Section4: Detailed Python Concepts

• Exception handling

• Concepts of file handling

• Condition handling

• Configuration setup

• Work on Excel and CSV

• Introduction to PyUnit

• Report generation in PyCharm

• First Python Program

Section5: Selenium Concepts

• What is Automation and what is Selenium?

• Selenium IDE

• Selenium Web driver

• To Access form

• To Access links

• To Access table content

• Selenium on firebox

• Selenium on internet explorer

Section6: Detailed Selenium Concepts

• Understanding web elements

• Keyboard event

• Mouse event

• To work with test framework

• Understanding Selenium Grid

Section7: Working with Selenium Test Case

• Firefox Driver

• Chrome Driver

• IE Driver

• Unit Test

• Inheritance

Section 8: Working with Python Case Study

• Data generation

• Python interpreter

• Overriding

• Jenkins

• To create Allure reports

Section 9: Selenium with Python

• Selenium Python Element locators

• Selenium Python on Jenkins

• Selenium Python on Unit testing

• Selenium and Python advanced concepts