Software Training Institute

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Python Training in Hyderabad

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Python Training in Hyderabad Batch Details

Course Fee 20,000 Rs
Course Duration 2 Months
Timings Monday to Friday (Morning to Evening)
Next Batch Date 7th Feb 2022 AT 11:00AM
Training Modes Classroom [HYDERABAD] / Online
Location Hyderabad

Key Points Of Python Training in Hyderabad

Python Course Curriculum

  • What is Language?
  • Types of languages
  • Introduction to Translators
    • Compiler
    • Interpreter
  • What is Scripting Language?
  • Types of Script
  • Programming Languages v/s Scripting Languages
  • Difference between Scripting and Programming languages
  • What is programming paradigm?
  • Procedural programming paradigm
  • Object Oriented Programming paradigm
  • What is Python?
  • WHY PYTHON?
  • History
  • Features – Dynamic, Interpreted, Object oriented, Embeddable, Extensible, Large standard libraries, Free and Open source
  • Why Python is General Language?
  • Limitations of Python
  • What is PSF?
  • Python implementations
  • Python applications
  • Python versions
  • PYTHON IN REALTIME INDUSTRY
  • Difference between Python 2.x and 3.x
  • Difference between Python 3.7 and 3.8
  • Software Development Architectures
  • Python Distributions
  • Download &Python Installation Process in Windows, Unix, Linux and Mac
  • Online Python IDLE
  • Python Real-time IDEs like Spyder, Jupyter Note Book, PyCharm, Rodeo, Visual Studio Code, ATOM, PyDevetc
  • Python Implementation Alternatives/Flavors
  • Keywords
  • Identifiers
  • Constants / Literals
  • Data types
  • Python VS JAVA
  • Python Syntax
  • Interactive Mode
  • Scripting Mode
  • Programming Elements
  • Structure of Python program
  • First Python Application
  • Comments in Python
  • Python file extensions
  • Setting Path in Windows
  • Edit and Run python program without IDE
  • Edit and Run python program using IDEs
  • INSIDE PYTHON
  • Programmers View of Interpreter
  • Inside INTERPRETER
  • What is Byte Code in PYTHON?
  • Python Debugger
  • bytes Data Type
  • byte array
  • String Formatting in Python
  • Math, Random, Secrets Modules
  • Introduction
  • Initialization of variables
  • Local variables
  • Global variables
  • ‘global’ keyword
  • Input and Output operations
  • Data conversion functions – int(), float(), complex(), str(), chr(), ord()
  • Arithmetic Operators
  • Comparison Operators
  • Python Assignment Operators
  • Logical Operators
  • Bitwise Operators
  • Shift operators
  • Membership Operators
  • Identity Operators
  • Ternary Operator
  • Operator precedence
  • Difference between “is” vs “==”
  • Print
  • Input
  • Command-line arguments

Control Statements

  • Conditional control statements
  • If
  • If-else
  • If-elif-else
  • Nested-if
  • Loop control statements
  • for
  • while
  • Nested loops
  • Branching statements
  • Break
  • Continue
  • Pass
  • Return
  • Case studies
  • Introduction
  • Importance of Data structures
  • Applications of Data structures
  • Types of Collections
  • Sequence
  • Strings, List, Tuple, range
  • Non sequence
  • Set, Frozen set, Dictionary
  • Strings
  • What is string
  • Representation of Strings
  • Processing elements using indexing
  • Processing elements using Iterators
  • Manipulation of String using Indexing and Slicing
  • String operators
  • Methods of String object
  • String Formatting
  • String functions
  • String Immutability
  • Case studies
  • What is List
  • Need of List collection
  • Different ways of creating List
  • List comprehension
  • List indices
  • Processing elements of List through Indexing and Slicing
  • List object methods
  • List is Mutable
  • Mutable and Immutable elements of List
  • Nested Lists
  • List_of_lists
  • Hardcopy, shallowCopy and DeepCopy
  • zip() in Python
  • How to unzip?
  • Python Arrays:

Case studies

  • What is tuple?
  • Different ways of creating Tuple
  • Method of Tuple object
  • Tuple is Immutable
  • Mutable and Immutable elements of Tuple
  • Process tuple through Indexing and Slicing
  • List v/s Tuple
  • Case studies
  • What is set?
  • Different ways of creating set
  • Difference between list and set
  • Iteration Over Sets
  • Accessing elements of set
  • Python Set Methods
  • Python Set Operations
  • Union of sets
  • functions and methods of set
  • Python Frozen set
  • Difference between set and frozenset ?
  • Case study
  • What is dictionary?
  • Difference between list, set and dictionary
  • How to create a dictionary?
  • PYTHON HASHING?
  • Accessing values of dictionary
  • Python Dictionary Methods
  • Copying dictionary
  • Updating Dictionary
  • Reading keys from Dictionary
  • Reading values from Dictionary
  • Reading items from Dictionary
  • Delete Keys from the dictionary
  • Sorting the Dictionary
  • Python Dictionary Functions and methods
  • Dictionary comprehension
  • What is Function?
  • Advantages of functions
  • Syntax and Writing function
  • Calling or Invoking function
  • Classification of Functions
    • No arguments and No return values
    • With arguments and No return values
    • With arguments and With return values
    • No arguments and With return values
    • Recursion
  • Python argument type functions :
    • Default argument functions
    • Required(Positional) arguments function
    • Keyword arguments function
    • Variable arguments functions
  • ‘pass’ keyword in functions
  • Lambda functions/Anonymous functions
    • map()
    • filter()
    • reduce()
  • Nested functions
  • Non local variables, global variables
  • Closures
  • Decorators
  • Generators
  • Iterators
  • Monkey patching
  • Python Modules
  • Importance of modular programming
  • What is module
  • Types of Modules – Pre defined, User defined.
  • User defined modules creation
  • Functions based modules
  • Class based modules
  • Connecting modules
  • Import module
  • From … import
  • Module alias / Renaming module
  • Built In properties of module
  • Organizing python project into packages
  • Types of packages – pre defined, user defined.
  • Package v/s Folder
  • py file
  • Importing package
  • PIP
  • Introduction to PIP
  • Installing PIP
  • Installing Python packages
  • Un installing Python packages
  • Procedural v/s Object oriented programming
  • Principles of OOP – Encapsulation , Abstraction (Data Hiding)
  • Classes and Objects
  • How to define class in python
  • Types of variables – instance variables, class variables.
  • Types of methods – instance methods, class method, static method
  •  
  • Object initialization
  • ‘self’ reference variable
  • ‘cls’ reference variable
  • Access modifiers – private(__) , protected(_), public
  • AT property class
  • Property() object
  • Creating object properties using setaltr, getaltr functions
  • Encapsulation(Data Binding)
  • What is polymorphism?
  • Overriding
  1. i) Method overriding
  2. ii) Constructor overriding
  • Overloading
  1. i) Method Overloading
  2. ii) Constructor Overloading

iii)  Operator Overloading

  • Class re-usability
  • Composition
  • Aggregation
  • Inheritance – single , multi level, multiple, hierarchical and hybrid inheritance and Diamond inheritance
  • Constructors in inheritance
  • Object class
  • super()
  • Runtime polymorphism
  • Method overriding
  • Method resolution order(MRO)
  • Method overriding in Multiple inheritance and Hybrid Inheritance
  • Duck typing
  • Concrete Methods in Abstract Base Classes
  • Difference between Abstraction & Encapsulation
  • Inner classes
  • Introduction
  • Writing inner class
  • Accessing class level members of inner class
  • Accessing object level members of inner class
  • Local inner classes
  • Complex inner classes
  • Case studies

Exception Handling & Types of Errors

  • What is Exception?
  • Why exception handling?
  • Syntax error v/s Runtime error
  • Exception codes – AttributeError, ValueError, IndexError, TypeError…
    • Handling exception – try except block
    • Try with multi except
    • Handling multiple exceptions with single except block
  • Finally block
    • Try-except-finally
    • Try with finally
    • Case study of finally block
  • Raise keyword
    • Custom exceptions / User defined exceptions
    • Need to Custom exceptions
  • Case studies

Regular expressions

  • Understanding regular expressions
  • String v/s Regular expression string
  • “re” module functions
  • Match()
  • Search()
  • Split()
  • Findall()
  • Compile()
  • Sub()
  • Subn()
  • Expressions using operators and symbols
  • Simple character matches
  • Special characters
  • Character classes
  • Mobile number extraction
  • Mail extraction
  • Different Mail ID patterns
  • Data extraction
  • Password extraction
  • URL extraction
  • Vehicle number extraction
  • Case study

File &Directory handling

  • Introduction to files
  • Opening file
  • File modes
  • Reading data from file
  • Writing data into file
  • Appending data into file
  • Line count in File
  • CSV module
  • Creating CSV file
  • Reading from CSV file
  • Writing into CSV file
  • Object serialization – pickle module
  • XML parsing
  • JSON parsing

Python Logging

  • Logging Levels
  • implement Logging
  • Configure Log File in over writing Mode
  • Timestamp in the Log Messages
  • Python Program Exceptions to the Log File
  • Requirement of Our Own Customized Logger
  • Features of Customized Logger

Date & Time module

  • How to use Date & Date Time class
  • How to use Time Delta object
  • Formatting Date and Time
  • Calendar module
  • Text calendar
  • HTML calendar

OS module

  • Shell script commands
  • Various OS operations in Python
  • Python file system shell methods
  • Creating files and directories
  • Removing files and directories
  • Shutdown and Restart system
  • Renaming files and directories
  • Executing system commands

Multi-threading & Multi Processing

  • Introduction
  • Multi tasking v/s Multi threading
  • Threading module
  • Creating thread – inheriting Thread class , Using callable object
  • Life cycle of thread
  • Single threaded application
  • Multi threaded application
  • Can we call run() directly?
  • Need to start() method
  • Sleep()
  • Join()
  • Synchronization – Lock class – acquire(), release() functions
  • Case studies

Garbage collection

  • Introduction
  • Importance of Manual garbage collection
  • Self reference objects garbage collection
  • ‘gc’ module
  • Collect() method
  • Threshold function
  • Case studies

Python Data Base Communications(PDBC)

  • Introduction to DBMS applications
  • File system v/s DBMS
  • Communicating with MySQL
  • Python – MySQL connector
  • connector module
  • connect() method
  • Oracle Database
  • Install cx_Oracle
  • Cursor Object methods
  • execute() method
  • executeMany() method
  • fetchone()
  • fetchmany()
  • fetchall()
  • Static queries v/s Dynamic queries
  • Transaction management
  • Case studies

Python – Network Programming

  • What is Sockets?
  • What is Socket Programming?
  • The socket Module
  • Server Socket Methods
  • Connecting to a server
  • A simple server-client program
  • Server
  • Client

Tkinter & Turtle

  • Introduction to GUI programming
  • Tkinter module
  • Tk class
  • Components / Widgets
  • Label , Entry , Button , Combo, Radio
  • Types of Layouts
  • Handling events
  • Widgets properties
  • Case studies

Data analytics modules

  • Numpy
  • Introduction
  • Scipy
  • Introduction
  • Arrays
  • Datatypes
  • Matrices
  • N dimension arrays
  • Indexing and Slicing
  • Pandas
  • Introduction
  • Data Frames
  • Merge , Join, Concat
  • MatPlotLib introduction
  • Drawing plots
  • Introduction to Machine learning
  • Types of Machine Learning?
  • Introduction to Data science

DJANGO

  • Introduction to PYTHON Django
  • What is Web framework?
  • Why Frameworks?
  • Define MVT Design Pattern
  • Difference between MVC and MVT

Pandas – Introduction

Pandas – Environment Setup

Pandas – Introduction to Data Structures

  • Dimension & Description
  • Series
  • DataFrame
  • Data Type of Columns
  • Panel

Pandas — Series

  • Series
  • Create an Empty Series
  • Create a Series f
  • rom ndarray
  • rom dict
  • rom Scalar
  • Accessing Data from Series with Position
  • Retrieve Data Using Label (Index)

Pandas – DataFrame

  • DataFrame
  • Create DataFrame
  • Create an Empty DataFrame
  • Create a DataFrame from Lists
  • Create a DataFrame from Dict of ndarrays / Lists
  • Create a DataFrame from List of Dicts
  • Create a DataFrame from Dict of Series
  • Column Selection
  • Column Addition
  • Column Deletion
  • Row Selection, Addition, and Deletion

Pandas – Panel

  • Panel()
  • Create Panel
  • Selecting the Data from Panel

Pandas – Basic Functionality

  • DataFrame Basic Functionality

Pandas – Descriptive Statistics

  • Functions & Description
  • Summarizing Data

Pandas – Function Application

  • Table-wise Function Application
  • Row or Column Wise Function Application
  • Element Wise Function Application

Pandas – Reindexing

  • Reindex to Align with Other Objects
  • Filling while ReIndexing
  • Limits on Filling while Reindexing
  • Renaming

Pandas – Iteration

  • Iterating a DataFrame
  • iteritems()
  • iterrows()
  • itertuples()

Pandas – Sorting

  • By Label
  • Sorting Algorithm

Pandas – Working with Text Data

Pandas – Options and Customization

  • get_option(param)
  • set_option(param,value)
  • reset_option(param)
  • describe_option(param)
  • option_context()

Pandas – Indexing and Selecting Data

  • .loc()
  • .iloc()
  • .ix()
  • Use of Notations

Pandas – Statistical Functions

  • Percent_change
  • Covariance
  • Correlation
  • Data Ranking

Pandas – Window Functions

  • .rolling() Function
  • .expanding() Function
  • .ewm() Function

Pandas – Aggregations

  • Applying Aggregations on DataFrame

Pandas – Missing Data

  • Cleaning / Filling Missing Data
  • Replace NaN with a Scalar Value
  • Fill NA Forward and Backward
  • Drop Missing Values
  • Replace Missing (or) Generic Values

Pandas – GroupBy

  • Split Data into Groups
  • View Groups
  • Iterating through Groups
  • Select a Group
  • Aggregations
  • Transformations
  • Filtration

Pandas – Merging/Joining

  • Merge Using ‘how’ Argument

Pandas – Concatenation

  • Concatenating Objects
  • Time Series

Pandas – Date Functionality

Pandas – Timedelta

Pandas – Categorical Data

  • Object Creation

Pandas – Visualization

  • Bar Plot
  • Histograms
  • Box Plots
  • Area Plot
  • Scatter Plot
  • Pie Chart

Pandas – IO Tools

  • csv

Pandas – Sparse Data

Pandas – Caveats & Gotchas

Pandas – Comparison with SQL

 NUMPY − INTRODUCTION

NUMPY − ENVIRONMENT

NUMPY − NDARRAY OBJECT

NUMPY − DATA TYPES

  • Data Type Objects (dtype)

NUMPY − ARRAY ATTRIBUTES

  • shape
  • ndim
  • itemsize
  • flags

NUMPY − ARRAY CREATION ROUTINES

  • empty
  • zeros
  • ones

NUMPY − ARRAY FROM EXISTING DATA

  • asarray
  • frombuffer
  • fromiter

NUMPY − ARRAY FROM NUMERICAL RANGES

  • arange
  • linspace
  • logspace

NUMPY − INDEXING & SLICING

NUMPY − ADVANCED INDEXING

  • Integer Indexing
  • Boolean Array Indexing

NUMPY − BROADCASTING

NUMPY − ITERATING OVER ARRAY

  • Iteration
  • Order
  • Modifying Array Values
  • External Loop
  • Broadcasting Iteration

NUMPY – ARRAY MANIPULATION

  • reshape
  • flat
  • flatten
  • ravel
  • transpose
  • T
  • swapaxes
  • rollaxis
  • broadcast
  • broadcast_to
  • expand_dims
  • squeeze
  • concatenate
  • stack
  • hstack and numpy.vstack
  • split
  • hsplit and numpy.vsplit
  • resize
  • append
  • insert
  • delete
  • unique

NUMPY – BINARY OPERATORS

  • bitwise_and
  • bitwise_or
  • invert()
  • left_shift
  • right_shift

NUMPY − STRING FUNCTIONS

NUMPY − MATHEMATICAL FUNCTIONS

  • Trigonometric Functions
  • Functions for Rounding

NUMPY − ARITHMETIC OPERATIONS

  • reciprocal()
  • power()
  • mod()

NUMPY − STATISTICAL FUNCTIONS

  • amin() and numpy.amax()
  • ptp()
  • percentile()
  • median()
  • mean()
  • average()
  • Standard Deviation
  • Variance

NUMPY − SORT, SEARCH & COUNTING FUNCTIONS

  • sort()
  • argsort()
  • lexsort()
  • argmax() and numpy.argmin()
  • nonzero()
  • where()
  • extract()

NUMPY − BYTE SWAPPING

  • byteswap()

NUMPY − COPIES & VIEWS

  • No Copy
  • View or Shallow Copy
  • Deep Copy

NUMPY − MATRIX LIBRARY

  • empty()
  • zeros()
  • ones()
  • eye()
  • identity()
  • rand()

NUMPY − LINEAR ALGEBRA

  • dot()
  • vdot()
  • inner()
  • matmul()
  • Determinant
  • solve()

NUMPY − MATPLOTLIB

  • Sine Wave Plot
  • subplot()
  • bar()

NUMPY – HISTOGRAM USING MATPLOTLIB

  • histogram()
  • plt()

NUMPY − I/O WITH NUMPY

  • save()
  • savetxt()
  • What is Language?
  • Types of languages
  • Introduction to Translators
    • Compiler
    • Interpreter
  • What is Scripting Language?
  • Types of Script
  • Programming Languages v/s Scripting Languages
  • Difference between Scripting and Programming languages
  • What is programming paradigm?
  • Procedural programming paradigm
  • Object Oriented Programming paradigm
  • What is Python?
  • WHY PYTHON?
  • History
  • Features – Dynamic, Interpreted, Object oriented, Embeddable, Extensible, Large standard libraries, Free and Open source
  • Why Python is General Language?
  • Limitations of Python
  • What is PSF?
  • Python implementations
  • Python applications
  • Python versions
  • PYTHON IN REALTIME INDUSTRY
  • Difference between Python 2.x and 3.x
  • Difference between Python 3.7 and 3.8
  • Software Development Architectures
  • Python Distributions
  • Download &Python Installation Process in Windows, Unix, Linux and Mac
  • Online Python IDLE
  • Python Real-time IDEs like Spyder, Jupyter Note Book, PyCharm, Rodeo, Visual Studio Code, ATOM, PyDevetc
  • Python Implementation Alternatives/Flavors
  • Keywords
  • Identifiers
  • Constants / Literals
  • Data types
  • Python VS JAVA
  • Python Syntax
  • Interactive Mode
  • Scripting Mode
  • Programming Elements
  • Structure of Python program
  • First Python Application
  • Comments in Python
  • Python file extensions
  • Setting Path in Windows
  • Edit and Run python program without IDE
  • Edit and Run python program using IDEs
  • INSIDE PYTHON
  • Programmers View of Interpreter
  • Inside INTERPRETER
  • What is Byte Code in PYTHON?
  • Python Debugger
  • bytes Data Type
  • byte array
  • String Formatting in Python
  • Math, Random, Secrets Modules
  • Introduction
  • Initialization of variables
  • Local variables
  • Global variables
  • ‘global’ keyword
  • Input and Output operations
  • Data conversion functions – int(), float(), complex(), str(), chr(), ord()
  • Arithmetic Operators
  • Comparison Operators
  • Python Assignment Operators
  • Logical Operators
  • Bitwise Operators
  • Shift operators
  • Membership Operators
  • Identity Operators
  • Ternary Operator
  • Operator precedence
  • Difference between “is” vs “==”
  • Print
  • Input
  • Command-line arguments

Control Statements

  • Conditional control statements
  • If
  • If-else
  • If-elif-else
  • Nested-if
  • Loop control statements
  • for
  • while
  • Nested loops
  • Branching statements
  • Break
  • Continue
  • Pass
  • Return
  • Case studies
  • Introduction
  • Importance of Data structures
  • Applications of Data structures
  • Types of Collections
  • Sequence
  • Strings, List, Tuple, range
  • Non sequence
  • Set, Frozen set, Dictionary
  • Strings
  • What is string
  • Representation of Strings
  • Processing elements using indexing
  • Processing elements using Iterators
  • Manipulation of String using Indexing and Slicing
  • String operators
  • Methods of String object
  • String Formatting
  • String functions
  • String Immutability
  • Case studies
  • What is List
  • Need of List collection
  • Different ways of creating List
  • List comprehension
  • List indices
  • Processing elements of List through Indexing and Slicing
  • List object methods
  • List is Mutable
  • Mutable and Immutable elements of List
  • Nested Lists
  • List_of_lists
  • Hardcopy, shallowCopy and DeepCopy
  • zip() in Python
  • How to unzip?
  • Python Arrays:
  • Case studies
  • What is tuple?
  • Different ways of creating Tuple
  • Method of Tuple object
  • Tuple is Immutable
  • Mutable and Immutable elements of Tuple
  • Process tuple through Indexing and Slicing
  • List v/s Tuple
  • Case studies
  • What is set?
  • Different ways of creating set
  • Difference between list and set
  • Iteration Over Sets
  • Accessing elements of set
  • Python Set Methods
  • Python Set Operations
  • Union of sets
  • functions and methods of set
  • Python Frozen set
  • Difference between set and frozenset ?
  • Case study
  • What is dictionary?
  • Difference between list, set and dictionary
  • How to create a dictionary?
  • PYTHON HASHING?
  • Accessing values of dictionary
  • Python Dictionary Methods
  • Copying dictionary
  • Updating Dictionary
  • Reading keys from Dictionary
  • Reading values from Dictionary
  • Reading items from Dictionary
  • Delete Keys from the dictionary
  • Sorting the Dictionary
  • Python Dictionary Functions and methods
  • Dictionary comprehension
  • What is Function?
  • Advantages of functions
  • Syntax and Writing function
  • Calling or Invoking function
  • Classification of Functions
    • No arguments and No return values
    • With arguments and No return values
    • With arguments and With return values
    • No arguments and With return values
    • Recursion
  • Python argument type functions :
    • Default argument functions
    • Required(Positional) arguments function
    • Keyword arguments function
    • Variable arguments functions
  • ‘pass’ keyword in functions
  • Lambda functions/Anonymous functions
    • map()
    • filter()
    • reduce()
  • Nested functions
  • Non local variables, global variables
  • Closures
  • Decorators
  • Generators
  • Iterators
  • Monkey patching

– Python Modules

  • Importance of modular programming
  • What is module
  • Types of Modules – Pre defined, User defined.
  • User defined modules creation
  • Functions based modules
  • Class based modules
  • Connecting modules
  • Import module
  • From … import
  • Module alias / Renaming module
  • Built In properties of module
  • Organizing python project into packages
  • Types of packages – pre defined, user defined.
  • Package v/s Folder
  • py file
  • Importing package
  • PIP
  • Introduction to PIP
  • Installing PIP
  • Installing Python packages
  • Un installing Python packages
  • Procedural v/s Object oriented programming
  • Principles of OOP – Encapsulation , Abstraction (Data Hiding)
  • Classes and Objects
  • How to define class in python
  • Types of variables – instance variables, class variables.
  • Types of methods – instance methods, class method, static method
  •  
  • Object initialization
  • ‘self’ reference variable
  • ‘cls’ reference variable
  • Access modifiers – private(__) , protected(_), public
  • AT property class
  • Property() object
  • Creating object properties using setaltr, getaltr functions
  • Encapsulation(Data Binding)
  • What is polymorphism?
  • Overriding
  1. i) Method overriding
  2. ii) Constructor overriding
  • Overloading
  1. i) Method Overloading
  2. ii) Constructor Overloading

iii)  Operator Overloading

  • Class re-usability
  • Composition
  • Aggregation
  • Inheritance – single , multi level, multiple, hierarchical and hybrid inheritance and Diamond inheritance
  • Constructors in inheritance
  • Object class
  • super()
  • Runtime polymorphism
  • Method overriding
  • Method resolution order(MRO)
  • Method overriding in Multiple inheritance and Hybrid Inheritance
  • Duck typing
  • Concrete Methods in Abstract Base Classes
  • Difference between Abstraction & Encapsulation
  • Inner classes
  • Introduction
  • Writing inner class
  • Accessing class level members of inner class
  • Accessing object level members of inner class
  • Local inner classes
  • Complex inner classes
  • Case studies

Exception Handling & Types of Errors

  • What is Exception?
  • Why exception handling?
  • Syntax error v/s Runtime error
  • Exception codes – AttributeError, ValueError, IndexError, TypeError…
    • Handling exception – try except block
    • Try with multi except
    • Handling multiple exceptions with single except block
  • Finally block
    • Try-except-finally
    • Try with finally
    • Case study of finally block
  • Raise keyword
    • Custom exceptions / User defined exceptions
    • Need to Custom exceptions
  • Case studies

Regular expressions

  • Understanding regular expressions
  • String v/s Regular expression string
  • “re” module functions
  • Match()
  • Search()
  • Split()
  • Findall()
  • Compile()
  • Sub()
  • Subn()
  • Expressions using operators and symbols
  • Simple character matches
  • Special characters
  • Character classes
  • Mobile number extraction
  • Mail extraction
  • Different Mail ID patterns
  • Data extraction
  • Password extraction
  • URL extraction
  • Vehicle number extraction
  • Case study

File &Directory handling

  • Introduction to files
  • Opening file
  • File modes
  • Reading data from file
  • Writing data into file
  • Appending data into file
  • Line count in File
  • CSV module
  • Creating CSV file
  • Reading from CSV file
  • Writing into CSV file
  • Object serialization – pickle module
  • XML parsing
  • JSON parsing

Python Logging

  • Logging Levels
  • implement Logging
  • Configure Log File in over writing Mode
  • Timestamp in the Log Messages
  • Python Program Exceptions to the Log File
  • Requirement of Our Own Customized Logger
  • Features of Customized Logger

Date & Time module

  • How to use Date & Date Time class
  • How to use Time Delta object
  • Formatting Date and Time
  • Calendar module
  • Text calendar
  • HTML calendar

OS module

  • Shell script commands
  • Various OS operations in Python
  • Python file system shell methods
  • Creating files and directories
  • Removing files and directories
  • Shutdown and Restart system
  • Renaming files and directories
  • Executing system commands

Multi-threading & Multi Processing

  • Introduction
  • Multi tasking v/s Multi threading
  • Threading module
  • Creating thread – inheriting Thread class , Using callable object
  • Life cycle of thread
  • Single threaded application
  • Multi threaded application
  • Can we call run() directly?
  • Need to start() method
  • Sleep()
  • Join()
  • Synchronization – Lock class – acquire(), release() functions
  • Case studies

Garbage collection

  • Introduction
  • Importance of Manual garbage collection
  • Self reference objects garbage collection
  • ‘gc’ module
  • Collect() method
  • Threshold function
  • Case studies

Python Data Base Communications(PDBC)

  • Introduction to DBMS applications
  • File system v/s DBMS
  • Communicating with MySQL
  • Python – MySQL connector
  • connector module
  • connect() method
  • Oracle Database
  • Install cx_Oracle
  • Cursor Object methods
  • execute() method
  • executeMany() method
  • fetchone()
  • fetchmany()
  • fetchall()
  • Static queries v/s Dynamic queries
  • Transaction management
  • Case studies

Python – Network Programming

  • What is Sockets?
  • What is Socket Programming?
  • The socket Module
  • Server Socket Methods
  • Connecting to a server
  • A simple server-client program
  • Server
  • Client

Tkinter & Turtle

  • Introduction to GUI programming
  • Tkinter module
  • Tk class
  • Components / Widgets
  • Label , Entry , Button , Combo, Radio
  • Types of Layouts
  • Handling events
  • Widgets properties
  • Case studies

Data analytics modules

  • Numpy
  • Introduction
  • Scipy
  • Introduction
  • Arrays
  • Datatypes
  • Matrices
  • N dimension arrays
  • Indexing and Slicing
  • Pandas
  • Introduction
  • Data Frames
  • Merge , Join, Concat
  • MatPlotLib introduction
  • Drawing plots
  • Introduction to Machine learning
  • Types of Machine Learning?
  • Introduction to Data science

DJANGO

  • Introduction to PYTHON Django
  • What is Web framework?
  • Why Frameworks?
  • Define MVT Design Pattern

Difference between MVC and MVT

Pandas – Introduction

Pandas – Environment Setup

Pandas – Introduction to Data Structures

  • Dimension & Description
  • Series
  • DataFrame
  • Data Type of Columns
  • Panel

Pandas — Series

  • Series
  • Create an Empty Series
  • Create a Series f
  • rom ndarray
  • rom dict
  • rom Scalar
  • Accessing Data from Series with Position
  • Retrieve Data Using Label (Index)

Pandas – DataFrame

  • DataFrame
  • Create DataFrame
  • Create an Empty DataFrame
  • Create a DataFrame from Lists
  • Create a DataFrame from Dict of ndarrays / Lists
  • Create a DataFrame from List of Dicts
  • Create a DataFrame from Dict of Series
  • Column Selection
  • Column Addition
  • Column Deletion
  • Row Selection, Addition, and Deletion

Pandas – Panel

  • Panel()
  • Create Panel
  • Selecting the Data from Panel

Pandas – Basic Functionality

  • DataFrame Basic Functionality

Pandas – Descriptive Statistics

  • Functions & Description
  • Summarizing Data

Pandas – Function Application

  • Table-wise Function Application
  • Row or Column Wise Function Application
  • Element Wise Function Application

Pandas – Reindexing

  • Reindex to Align with Other Objects
  • Filling while ReIndexing
  • Limits on Filling while Reindexing
  • Renaming

Pandas – Iteration

  • Iterating a DataFrame
  • iteritems()
  • iterrows()
  • itertuples()

Pandas – Sorting

  • By Label
  • Sorting Algorithm

Pandas – Working with Text Data

Pandas – Options and Customization

  • get_option(param)
  • set_option(param,value)
  • reset_option(param)
  • describe_option(param)
  • option_context()

Pandas – Indexing and Selecting Data

  • .loc()
  • .iloc()
  • .ix()
  • Use of Notations

Pandas – Statistical Functions

  • Percent_change
  • Covariance
  • Correlation
  • Data Ranking

Pandas – Window Functions

  • .rolling() Function
  • .expanding() Function
  • .ewm() Function

Pandas – Aggregations

  • Applying Aggregations on DataFrame

Pandas – Missing Data

  • Cleaning / Filling Missing Data
  • Replace NaN with a Scalar Value
  • Fill NA Forward and Backward
  • Drop Missing Values
  • Replace Missing (or) Generic Values

Pandas – GroupBy

  • Split Data into Groups
  • View Groups
  • Iterating through Groups
  • Select a Group
  • Aggregations
  • Transformations
  • Filtration

Pandas – Merging/Joining

  • Merge Using ‘how’ Argument

Pandas – Concatenation

  • Concatenating Objects
  • Time Series

Pandas – Date Functionality

Pandas – Timedelta

Pandas – Categorical Data

  • Object Creation

Pandas – Visualization

  • Bar Plot
  • Histograms
  • Box Plots
  • Area Plot
  • Scatter Plot
  • Pie Chart

Pandas – IO Tools

  • csv

Pandas – Sparse Data

Pandas – Caveats & Gotchas

Pandas – Comparison with SQL

NUMPY − INTRODUCTION

NUMPY − ENVIRONMENT

NUMPY − NDARRAY OBJECT

NUMPY − DATA TYPES

  • Data Type Objects (dtype)

NUMPY − ARRAY ATTRIBUTES

  • shape
  • ndim
  • itemsize
  • flags

NUMPY − ARRAY CREATION ROUTINES

  • empty
  • zeros
  • ones

NUMPY − ARRAY FROM EXISTING DATA

  • asarray
  • frombuffer
  • fromiter

NUMPY − ARRAY FROM NUMERICAL RANGES

  • arange
  • linspace
  • logspace

NUMPY − INDEXING & SLICING

NUMPY − ADVANCED INDEXING

  • Integer Indexing
  • Boolean Array Indexing

NUMPY − BROADCASTING

NUMPY − ITERATING OVER ARRAY

  • Iteration
  • Order
  • Modifying Array Values
  • External Loop
  • Broadcasting Iteration

NUMPY – ARRAY MANIPULATION

  • reshape
  • flat
  • flatten
  • ravel
  • transpose
  • T
  • swapaxes
  • rollaxis
  • broadcast
  • broadcast_to
  • expand_dims
  • squeeze
  • concatenate
  • stack
  • hstack and numpy.vstack
  • split
  • hsplit and numpy.vsplit
  • resize
  • append
  • insert
  • delete
  • unique

NUMPY – BINARY OPERATORS

  • bitwise_and
  • bitwise_or
  • invert()
  • left_shift
  • right_shift

NUMPY − STRING FUNCTIONS

NUMPY − MATHEMATICAL FUNCTIONS

  • Trigonometric Functions
  • Functions for Rounding

NUMPY − ARITHMETIC OPERATIONS

  • reciprocal()
  • power()
  • mod()

NUMPY − STATISTICAL FUNCTIONS

  • amin() and numpy.amax()
  • ptp()
  • percentile()
  • median()
  • mean()
  • average()
  • Standard Deviation
  • Variance

NUMPY − SORT, SEARCH & COUNTING FUNCTIONS

  • sort()
  • argsort()
  • lexsort()
  • argmax() and numpy.argmin()
  • nonzero()
  • where()
  • extract()

NUMPY − BYTE SWAPPING

  • byteswap()

NUMPY − COPIES & VIEWS

  • No Copy
  • View or Shallow Copy
  • Deep Copy

NUMPY − MATRIX LIBRARY

  • empty()
  • zeros()
  • ones()
  • eye()
  • identity()
  • rand()

NUMPY − LINEAR ALGEBRA

  • dot()
  • vdot()
  • inner()
  • matmul()
  • Determinant
  • solve()

NUMPY − MATPLOTLIB

  • Sine Wave Plot
  • subplot()
  • bar()

NUMPY – HISTOGRAM USING MATPLOTLIB

  • histogram()
  • plt()

NUMPY − I/O WITH NUMPY

  • save()
  • savetxt()

 

About Python Training in Hyderabad

Python is one of the most in-demand programming languages, created in 1989 by Guido Rossum. It works in extending prototyping services for applications that are compound and complex. The highlight feature of Python is its flexibility to combine with fellow high-end courses like Artificial intelligence, machine learning, Cloud computing, Networking systems amidst other professional fields of computer science. Python is known for its upscale multi-purpose functionalities and helps in building websites, automated tasks, softwares and more. Data analysis can also be effectively conducted via python making it one of the most mouldable computer programming languages. Brolly Academy offers the best Python training in Hyderabad with a detailed course program. The training includes core topics like the fundamentals of Python, python data types and structures, control flow statements, functions, concepts focusing on objects in Python, importing statements, exception handling, Args, wargs, Zip functioning, file handling and all about it, expressions, numpy, pandas, mapping, filtering and reducing in python and much more. Brolly academy offers a wide range of courses that include both IT and software training courses in Hyderabad with updated course curriculums and modules.You will receive a Python certification upon the completion of the training program that is approved and received by most of the companies. The Python certificate will promote dynamic and flexible job opportunities for candidates with the right skills when attached to your professional resume. The python certificate offered by Brolly academy can greatly enlarge your potential employment opportunities by uplifting your career growth. This course is purposed for absolute beginners as well as Python professionals looking for a certified and advanced Python course to further refine your skills. We have trainers with a vast industrial exposure and proficiency with an impressive 8+ years of training and working as senior developers. Brolly academy offers three different modes of training that includes Python online training in Hyderabad, Python video course training in Hyderabad and Python classroom training in Hyderabad (currently suspended following the rising cases of covid) and any further updates regarding the batches and schedules will be pinned to our website. At Brolly academy we also focus on training the students by making them job ready with our placement assistance programs. Interview questions, resume preparation, mock tests, mock interviews, referrals and much more can be accessed as a part of our python Placement program. Enroll with us to get the best Python training in Hyderabad with a complete training package.

Modes of Training For Python Training in Hyderabad

Online Python training

The python online training in Hyderabad is pervaded with live interactive, tutor-led sessions along with group discussions based on case studies and projects via virtual training platforms.

Classroom training

Brolly Academy offers Python classroom training conducted in our study centers in Ameerpet and Kphb. Detailed explanation of the course and its topics are taught by our adept trainers with live one-on-one sessions.

Python self-learning video course

The Python Video Course is an instructional video course with live recordings from our classroom training. This python video course is put together in a highly convenient way for the students to easily understand and participate in the self-learning process. You can prepare, learn and master the Python course with no deadlines or rushes at your own pace.

Why choose us for Python Training?

Experienced Python Trainers

We have the best Python training staff at Brolly academy with highly experienced work profiles who implement excellent training methods for coaching and guiding the students throughout the program.

Access to premium tools

As a part of our training program, students will be given a chance to get hands-on experience in working with the tools and techniques that are premium and paid versions for a guaranteed practical exposure. 

Industrial exposure

Our trainers use updated curriculum and train the students with an updated version of Python. Reasoning and practical development programs will be conducted to polish your theoretical and technical Python knowledge. 

Interview guidance

We at Brolly academy have a dedicated support staff that specialize in providing placement assistance to our students. They conduct frequent mock interviews, resume management support with top interview questions to prepare our trainees with confidence and skill. 

Student diaries

We have trained 70+ trainees with 45+ successful placements in some of the top companies in Hyderabad with excellent packages.

Student support cell

Our staff at Brolly Academy will do everything they can to help students who need assistance with the course including additional support with interview prep. You can reach any of us at any time and we respond back at the earliest.

Get Brolly Certified

A mandatory Python course completion certificate will be handed over to the students upon the completion of the course. The Python certificate is customized by Brolly academy with an international acceptance and accreditation to it.

Remote study culture

The python training offered by us can be learnt from any part of the world with assured quality training. We have both online as well as video course python training that are packed with tutorials, recordings and more.  

 

Testimonials of Python Training in Hyderabad

Manish
Manish

The python training in Hyderabad offered by Brolly academy is one of the best in the city. They helped me understand the basics as well as the core concepts patiently and effectively. The trainers were kind and helpful. I had a good experience.

Shivali
Shivali

Completing the python course in Hyderabad from Brolly academy has earned me a job opportunity with a good package. Thanks to their placement assistance where they prepare you well for the interviews. Thank you so much!

Jaanu
Jaanu

I would definitely recommend Python language training in Hyderabad from Brolly academy for those who are interested in learning the course. The concepts are taught well along with the helpful study guides. The trainers are highly skilled and help you throughout the course.

Sunil
Sunil

Brolly Academy is one of the best Python training institutes in Hyderabad with excellent staff, quality training and affordable fees. I loved the classes as the trainer used case studies to help us understand the concepts better

Python Certification

You will receive a certificate from us upon completion of Python certification training in Hyderabad. The certificate can be downloaded as either a hard or digital copy, based on your requirement. It has been proven that the Python certification offered by Brolly academy can drastically help you crack interviews for desired positions and increase your starting salary by augmenting your career.

Python Certification

Placements

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            Advantages of learning Python training in Hyderabad

            Our adaptive and constructive Python training at Brolly Academy will help you establish the right goals and skill set. The well-organized training program allows students to achieve the desired knowledge and expertise to excel in a career in python. We conduct our training with the implementation of the latest versions of the technology and its curriculum to keep the trainees prepared and ahead of the competition.

            Skills developed after the Python course training –

            Prerequisites of Python training course in Hyderabad

            Career Opportunities in Python

            FAQ's

            Which is the best Python training institute in Hyderabad?

            Brolly Academy offers the best Python training in Hyderabad that explores essential concepts of the language.

            What is the Python training fee in Hyderabad?

            The cost of Python courses in Hyderabad depends on the type of training different institutes offer. Our institute, Brolly Academy, offers Python courses at a reasonable price.

            What is the average salary of a Python developer in Hyderabad?

            The average salary of a Python developer in Hyderabad is ₨ 4.2 lakhs per year.

            Where can I find Python training near me?

            We offer Python training in Kphb and Python training in Ameerpet, you can visit us or get in touch with us for further queries.

            What if I miss a class?

            Students can make up for missed sessions by scheduling one-on-one training with our experienced instructors or attend the next consecutive batch.

            Do you provide placement assistance?

            Yes. To help our students land jobs after the completion of their training program, we assist them with mock interviews and resume preparation by providing personal guidance from industry experts as a part of our python placement assistance

            Who are the trainers at Python training in Hyderabad?

            The Brolly Academy's Python courses are taught by instructors who have had extensive hands-on working experience and skill.

            Enroll for the Live Demo Class