Python Training

Following are the professionals and candidates who get benefited from learning this course :

• Introduction to python and the procedure to installation.
• Study OOP, data types, expressions, looping, etc.
• class defining, operations, and functions.
• Learn all the Machine learning Algorithms used in python.
• Expertise in Python File Handling and String Manipulations.

Following are the professionals and candidates who get benefited from learning this course :

• Project Managers and BI Managers
• ETL Professionals and Software Developers
• Big Data Professionals
• Analytics Professionals
• Aspirants who wish to build their career in top programming language.

You need not to possess any special qualification to join this course. Having basic knowledge of any programming language would be an added advantage.

• Python is a rapid development tool and 10 times faster compared to C, C++ and Java
• Python has become one of the widely accepted programming languages across the world.
• According to Indeed.com the average salary received by a python developer is $102,000.

Yes, You will be working on two live Python projects related to different industries during the training. These projects will provide you praactical experience.

Yes, you will get the Python course completion certificate from SK Trainings. This certificate is valid across all the top global organizations and simplifies your job search process.

• Using Whitespace to Structure Programs
• Python Comments
• Identifiers and Keywords
• Python’s Execution Model
• Naming Objects and Binding
• Python’s Data Model
• Immutable and Mutable Objects
• Values
• Types
• Creating and Using Variables
• Unary and Binary Arithmetic Operations
• Comparison and Boolean Operations
• Conditional Expressions
• Lambda Expressions
• Order of Operations and Operator Evaluation
• Expression Lists
• Assignment Operations
• Live Examples

• if/elif/else Statements
• Creating Loops with while and for
• Understanding Iterators
• Returning Values with return Statements
• Loop Modification with break and continue
• Returning Generator Iterators with the yield Statement
• Retrieving Iterators with next()
• Live Examples
• Assignments for weekends
• Quotation Marks and Special Characters
• Using ASCII and Unicode Strings
• Manipulating Strings with String Methods
• Using the format() Function to Format Strings
• Using Escape Sequences
• Working with Raw Strings
• Live Examples

• Types of Python Exceptions
• Handling Exceptions with try/except/finally
• Triggering Exceptions with raise
• Defining New Exception Types
• Implementing Exception Handling in Functions, Methods and Classes
• Working with the Regular Expression Error Exception
• Understanding Time
• The time Module
• The datetime Module
• Live Examples

• Defining Functions
• Variables and scopes
• Calling Functions
• Creating Anonymous Functions
• Altering Function Functionality with Decorator Functions Passing Arguments to Functions by Reference and by Value
• Defining Functions with Required Arguments
• Defining Functions with Default Arguments
• Defining Flexible Functions that Take Variable Length Arguments
• Live Examples

• Sequenced Data Structures
• Arrays
• Collections
• Dictionaries
• Creating and Accessing Lists
• Manipulating Lists
• Creating and Accessing Tuples
• Understanding the Differences Between Lists and Tuples
• Using Dictionaries to Create Data Records
• Manipulating Dictionaries Using Dictionary Methods
• Creating Sets
• Performing Set Operations
• Union
• Intersect
• Difference
• Differences Between Sets and Dictionaries
• Using Generators to Return Iterators
• Live Examples

• 3 Hours Evaluation

• The Object Oriented Programming Paradigm
• Encapsulating Information
• Classes vs. Instances of Objects
• Built-in Class Attributes
• Implementing Class Inheritance
• Using Objects in Code
• Live Examples
• Assignments for weekends

• Defining a thread
• Starting a thread
• Threading module
• Threads synchronization
• Multithreaded priority queue
• Executing Python Programs from the Command Line
• Python Command Line Options
• Environment Variables that Influence Python
• Creating Python GUI Applications
• Standalone vs. Web-Enabled Interfaces
• The Python Standard Library

• Regular Expression Syntax
• Using Regular Expressions in Python
• Altering Regular Expression Processing with Regular Expression Modifiers
• Using Regular Expression Operators
• Scanning Through Strings Using the search() and match() Methods

• Sending Output to STDOUT Using the print() Method
• Reading Input with the input() Method
• Creating File Objects with the open() Method
• Controlling File Access Modes
• Working with File Object Attributes
• Closing File Objects with the close() Method
• Reading and Writing to File Objects with read() and write()

• Best Practices for Data Management
• Storing Data in Local Databases
• Discussing and Understanding the DB API
• Understanding and Using Common SQL Statements
• Connecting to a MYSQL/SQLite Database
• Using Cursors to interact with Data from a Database
• CRUD Operations
• Implementing Error Handling with Database Connections
• Transaction management

• Introduction
• Tkinter programming
• Tkinter widgets
• Live Project
• Assignments for Weekend

• Introduction
  • About Django
  • Django Components
• Installing & Configuring Django Components
  • Downloading & Installing Django
  • Choosing a Database

• Generating Simple Django Views
  • About View Functions
  • Using Django's HttpResponse Class
  • Understanding HttpRequest Objects
  • Using QueryDict Objects
• Configuring URLconf's
  • About URLconf
  • Regular Expressions
  • Expression Examples
  • Simple URLConf Examples
  • Using Multiple URLConf's
  • Passing URL Arguments

• About Templates
• Template Fundamentals
• Creating Template Objects
• Loading Template Files
• Filling in Template Content (Context Objects)
• Template Filters
• Template Tags
• More on For Loops
• Template Inheritance
• Easy Rendering of Templates
• RequestContext Processors

• HTML Forms with Forms (formerly newforms)
• The Forms Module
• Creating the Form
• Generating Output From the Form
• Customizing Field Parameters
• Processing Form Data
• Custom Form Field Validation
• Generating Custom Field Errors

• About Database Models
• Configuring Django for Database Access
• Understanding Django Apps
• About Django Models
• Defining Django Models
• Understanding Model Fields & Options
• Table Naming Conventions
• Creating A Django Model
• Adding the App to Your Project
• Validating the App
• Generating & Reviewing the SQL
• Adding Data to the Model
• Primary Keys and the Model
• Simple Data Retrieval Using a Model
• Understanding QuerySets
• Applying Filters
• Specifying Field Lookups
• Lookup Types
• Slicing QuerySets
• Specifying Ordering in QuerySets
• Common QuerySet Methods
• Deleting Records
• Managing Related Records
• Retrieving Related Records
• Weekend Assignments

• ORM Configuration:
• Mapper Configuration
• Relationship Configuration
• Inheritance Mapping
• Advanced Collection Configuration
• Configuration Extensions:
• Declarative Extension
• Association Proxy
• Hybrid Attributes
• Mutable Scalars
• Ordered List
• ORM Usage: Session Usage and Guidelines
• Query API reference
• Relationship Loading Techniques
• Using Q Objects
• Creating Forms from Models

• Using the Django Admin Interface
  • Enabling the Admin Interface
  • Creating an Admin User
• Access Control with Sessions and Users
  • Cookies & Django
  • The Django Session Framework
  • Sessions in Views
  • Session Tuning
  • Installing Django User Authentication
  • Using Authentication in Views
  • Login and Logout
  • Building your Own Login/Logout Views
  • Authentication Decorators
  • Adding & Deactivating Users
  • Asynchronous Messaging

• Generic Views
  • Simple Generic Views
  • Using Generic Redirects
  • Other Generic Views
  • Create/Update/Delete Generic views
• Creating Syndication Feeds
  • About Syndication
• Data Caching for Performance
  • Data Caching
  • Setting up Per-View Caching

• Preventing Cross Site Request Forgery
  • Cross Site Request Forgery
  • Django's Email Functionality
    • Configuring Mail Settings
    • Sending Email
    • Other Email Functions
  • Deploying Django Applications
    • Deploying to Apache
  • Using ReportLab for PDF Generation
    • About ReportLab Toolkit

• SOAP web Services
• Rest Web Services

• Introduction
• Downloading
• Package Content
• First Run
• Command Line Options
• Install As Service
• Verify Server
• Database is Namespace
• Getting Help in the Shell
• Replica Sets
• Configuring a Replica Set
• Verifying Replication Works

• Shell Role in Ecosystem
• Shell Modes
• Shell Invoking Syntax
• Using Eval
• Substantial Scripts
• Execute Script Before Enter
• Shell Keys and Shortcuts
• Multiline Editing
• External Editor Integration
• Load Script from Within
• User RC File
• Common Safety Usage Tip

• Overview
• Storage
• JSON
• Saving Documents
• Collections
• Document Id
• ObjectId
• Insert
• Insert with Id
• Complex Document
• Save Danger
• Update Command
• Update Demo
• Set Operator
• Unset Operator
• Rename Operator
• Push Operator
• Pull Operator
• Pop Operator
• Array Type
• Multi Update
• Find And Modify
• Query With Sort
• Demo Find And Modify

• Introduction
• Overview
• find()
• Equality
• Projection
• Comparison
• $not
• $in
• Arrays
• $all
• $nin
• Dot Notation
• Sub-Document
• null and $exists
• And
• More Projection
• Cursor
• sort()
• limit()
• skip()
• findOne()

• Overview
• Scan Is Bad
• Index Theory
• Sort Uses Index
• Index Types
• Create Index
• indexes collection
• explain()
• nscanned
• Multi-Term Query
• Comparison
• Scanned vs. Returned
• dropIndex()
• Nested Fields
• Array Field
• Sort
• Unique
• Sparse
• Compound
• Sort Direction
• Covering Index
• Dead Weight
• Background build
• Index Name
• Installing pymongo
• CRUD operations using python
• Web application using Django with mongodb

• What is Java?
• History
• Versioning
• The Java Virtual Machine
• Installation and Environment variables
• Writing a Java Program
• Packages
• Simple Java Programs
• Primitive Data Types
• Comments
• The for Statement
• The if Statement
• The while and do while Statements
• The switch Statement
• The break Statement
• The continue Statement
• Operators
• Casts and Conversions

• Defining New Data Types
• Constructors
• The String Class
• String Literals
• Documentation
• Packages
• The String Buffer Class
• Naming Conventions
• The Date Class
• The import Statement
• Deprecation
• The StringTokenizer Class
• The DecimalFormat Class

• Introduction
• Method Signatures
• Arguments and Parameters
• Passing Objects to Methods
• Method Overloading
• Static Methods
• The Math Class
• The System Class
• Wrapper Classes
• Processing Arrays
• Copying Arrays
• Passing Arrays to Methods
• Arrays of Objects
• The Arrays Class
• Command Line Arguments
• Multidimensional Arrays

• Introduction
• Constructors
• The this Reference
• Data Hiding
• public and private Members
• Access Levels
• Composition
• Static Data Members
• The Object Class
• Method Overriding
• Polymorphism
• Additional Inheritance Examples
• Other Inheritance Issues
• Abstract Classes
• Abstract Class Example
• Extending an Abstract Class
• Interfaces
• Exception Handling
• The Exception Hierarchy
• Checked Exceptions
• Advertising Exceptions with throws
• Developing Your Own Exception Classes

• Introduction
• Vectors
• Hashtables
• Enumerations
• Properties
• Collection Framework Hierarchy
• Lists
• Sets
• Maps
• The Collections Class

An interpreted language is any programming language which is not in machine level code before runtime. Therefore, Python is an interpreted language.

Python is capable of scripting, but in general sense, it is considered as a general-purpose programming language.

1. Memory management in python is managed by Python private heap space. All Python objects and data structures are located in a private heap. The programmer does not have access to this private heap. The python interpreter takes care of this instead.

2. The allocation of heap space for Python objects is done by Python’s memory manager. The core API gives access to some tools for the programmer to code.

3. Python also has an inbuilt garbage collector, which recycles all the unused memory and so that it can be made available to the heap space.

A namespace is a naming system used to make sure that names are unique to avoid naming conflicts.

PEP stands for Python Enhancement Proposal. It is a set of rules that specify how to format Python code for maximum readability.

Python modules are files containing Python code. This code can either be functions classes or variables. A Python module is a .py file containing executable code.

Some of the commonly used built-in modules are:

• os
• mathsys
• random
• data time
• JSON

It is an environment variable which is used when a module is imported. Whenever a module is imported, PYTHONPATH is also looked up to check for the presence of the imported modules in various directories. The interpreter uses it to determine which module to load.

Yes. Python is a case sensitive language.

To install Python on Windows, follow the below steps:

• ● Install python from this link: https://www.python.org/downloads/
• ● After this, install it on your PC. Look for the location where PYTHON has been installed on your PC using the following command on your command prompt: cmd python.
• ● Then go to advanced system settings and add a new variable and name it as PYTHON_NAME and paste the copied path.
• ● Look for the path variable, select its value and select ‘edit’.
• ● Add a semicolon towards the end of the value if it’s not present and then type %PYTHON_HOME%

An anonymous function is known as a lambda function. This function can have any number of parameters but, can have just one statement.

Ans: Iterators are objects which can be traversed though or iterated upon.

Self is an instance or an object of a class. In Python, this is explicitly included as the first parameter. However, this is not the case in Java where it’s optional. It helps to differentiate between the methods and attributes of a class with local variables.

The self variable in the init method refers to the newly created object while in other methods, it refers to the object whose method was called.

Comments in Python start with a # character. However, alternatively at times, commenting is done using docstrings(strings enclosed within triple quotes).

Comments in Python start with a # character. However, alternatively at times, commenting is done using docstrings(strings enclosed within triple quotes).

Functions that return an iterable set of items are called generators.

Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function, this process is called pickling. While the process of retrieving original Python objects from the stored string representation is called unpickling.

In Python, the capitalize() method capitalizes the first letter of a string. If the string already consists of a capital letter at the beginning, then, it returns the original string.

To convert a string to lowercase, lower() function can be used.

Python packages are namespaces containing multiple modules.

Python is an object-oriented programming language. This means that any program can be solved in python by creating an object model. However, Python can be treated as procedural as well as structural language.