ihub python course brochure
Post on 24-Dec-2021
24 Views
Preview:
TRANSCRIPT
Learn from Industry Experts
100 % JOBGAURANTEE
SALARY RANGE3.2 to 12+ LAKHS
Powered By
www.ihubtalent.com
WITH 100% JOB GAURUNTEE COURSES
COURSE + INTERNSHIP PYTHON
www.ihubtalent.com
PYTHON the Future Technology
Most Popular Uses of Python
40%MachineLearning
40%Web
Development
55%Data
Science
4th Most Used 85% of the TimeProgramming Languagein 2021, 44.1% of developersworked with it
Python was used as a primaryprogramming Language for thedevlopment
There are now 8.2 million developers in the world who code using Python and thatPopulation is now larger than those who build in Java, who number 7.6 million
Source -SlashData
An average python developer
in India draws ₹779,449/- PA
Source
www.ihubtalent.com
100% JOB GUARANTEED
PYTHON COURSE CURRICULUM
COURSE DURATION5 WEEKS
SESSION HOURS60 HOURS
REALTIME PRACTICE
CASE STUDIES + MOCK TESTS
KEY HIGHLIGHTS
ä Personalized attention and guidance to accelerate the learning
ä Subject matter experts as trainers
ä Industry ready course roaster designed by architects and subject matter experts
ä Proven content, creating gateway to world of Job opportunities
ä Not just learning, Learn by doing methodology ä Access to Instant recorded live Class videos shared
ä Guest lectures from leading MNC Companies
ä Practical & detailed approach, everyone can understand and perform to excel
ä Member access to private group to get instant doubt clarifications
ä More than 10,000 + success stories created
ä Parent company Quality Thought No #1 providing placements since 2010
ä Backed by strong Internal human resource team enabling quick placements
ä Institute backed by strong IIT alumina Team ä Only EdTech with 100% Internship based and job guarantee training
ä Experienced IT Architects, subject matter experts as advisory in the boardä Guaranteed Internship Opportunity (3 to 6Months) for every participant
ä Special guidance to students to win jobs with highest packages
ä Partnered and serving 100+ clients right from the inception of the firm
TRAINING HIGHLIGHTS
www.ihubtalent.com
Working with Data – Variables
Python - Programming from Absolute Beginning - Introduc�on to Computer Programs
Avoiding conflicts with namespaces
Programming Paradigms
Understanding Func�ons
Introduc�on to Programming Languages - Transla�ng code into something that the computer understands - Syntax and the building blocks of a programming language
Types of Applica�ons Standalone applica�ons , Client-Server Applica�ons, Web applica�ons Mobile Applica�ons ,Distributed applica�ons, Cloud-based applica�ons
So�ware Projects and How We Organize Our Code Working with so�ware projects , Working with packages to share code
Sequence – The Basic Building Block of a Computer Program
Composite type
Controlling the execu�on path ,
Declaring and ini�alizing variables , Primi�ve data types
Selec�on with the if and switch statement Itera�on with the for loop , Itera�on with the while loop Itera�ng over sequences using for each
Deciding what goes into a func�on , Wri�ng a func�on Returning values from a func�on ,Func�on arguments, Func�ons in ac�on , Local and global variables
When Things Go Wrong – Bugs and Excep�ons
Program Control Structures
Understanding so�ware bugs , types of so�ware bugs Finding bugs using a debugger
Unit tes�ng, Integra�on tes�ng , Other types of tests
So�ware releases
Deployment Automa�on
Programming Tools and Methodologies
Understanding structured programming , object-oriented programming , func�onal programming - logic programming
Understanding version control systems
Understanding so�ware deployment Understanding so�ware deployment
Code maintenance
Top Companies Using
www.ihubtalent.com
Do Mul�ple Comparisons with in
Forma�ng, Oldstyle: % , New styles: {} and format() ,
Data: Types, Values, Variables, and Names
Why Python? , Why Not Python?
Prac�cal Version Controlling with Git, So�ware defect management using JIRA Understanding Agile project Management using Scrum
Get Length with len() , Split with strip()
Assigning to Mul�ple Names, Reassigning a Name
Get a Character with [] , Get a Substring with a Slice
Comment with #, Con�nue Lines with \
Floats, Math Func�ons
Python Data are objects , Types , Mutability, Literal Values
Choose with if
Crea�ng with Quotes
Agile development
Defining code quality , Wri�ng code with readability in mind
Introduc�on to Python
Crea�ng with str(), Escape with \ Combine by Using + , Duplicate with +
Wri�ng code with efficiency in mind
Installing Python , Running Python , Moment of Zen
Compare with if, elif and else, What is True
Mysteries , Li�le Programs
Copying, Choose Good Variable Names
A Bigger Program , Python in the Real world
Precedence, Bases , Type Conversions, How Big is int?
Search and Select , Case , Alignment
Code Quality
Integers, Literal Integers, Integer Opera�ons ,Integers and Variables
Variables , Assignment, Variables are Names, Not Places
Numbers
A Taste of Python
Boolean
Text Strings
Newest Style: f-string , More String Things
Waterfall development , Spiral model So�ware deployment process methodologies
Top Companies Using
Create with tuple()
Repeat with while
Cancel with break, Skip Ahead with con�nue, Check break Use with else Generate Number Sequences with range() , Other Iterators
Cancel with break, Skip Ahead with con�nue
Tuples and Lists
Create with Commas and ()
Loop with while and for
Check break Use with else ,Iterate with for and in
Tuples
Combine Tuples by Using +
Compare Tuples Iterate with for and in Modify a Tuple
Lists Create with [] , Create or Convert with list() Create from String with split() Get an Item by [ offset ] , Get Items with a Slice
Add an Item by offset with insert()
Change an item by [ offset ], Change Items with a Slice Duplicate All items with *, Combine Lists by Using extend() or +
Delete an Item by Value with remove() Get an Item by Offset and Delete It with pop()
Add an item to the End with append(),
Duplicate Items with *
Delete an Item by Offset with del,
Delete All items with clear(), Find an Item's Offset by Value with index() Test for a Value with in.,
Convert with dict()
Copy everything with deepcopy()
Create with {} Create with dict()
Get Length with len() Reorder Items with sort() or sorted() Convert a List to a String with join()
Create a List with a Comprehension- Tuples vs Lists
Assign with =, Copy with copy(), list() or a Slice
Count Occurrences of a Value with count()
Dic�onariesDic�onaries and Sets
Iterate Mul�ple Sequences with zip() Compare Lists(), Iterate with for and in
Add or Change an Item by [ key ]
www.ihubtalent.com
Top Companies Using
Copy Everything with deepcopy()
Get Length with len()
Iterate with for and in
Set Comprehensions
Define a Func�on with def
Dic�onary Comprehensions
Posi�onal arguments
Specify Default Parameter Values
Delete an Item with remove()
Iterate with for and in
Delete an Item by Key with del
Combine Dic�onaries with {**a, **b}
Delete All Items with clear()
Copy with copy() Assign with =
Get All Key-Value Pairs with items()
Combine Dic�onaries with update()
Get an Item by Key and Delete it with pop()
Get All Keys with keys()
Compare Dic�onaries
Convert with set()
Sets
Get Length with len ()
Get All Values with values()
Create with set()
Add an Item with add()
Combina�ons and Operators
Create an Immutable Set with frozenset()
Func�ons
Call a Func�on with Parentheses Arguments and Parameters None is useful
Keyword Arguments
Explode/Gather Posi�onal Arguments with * Explode/Gather Keyword Arguments with **
Inner Func�ons Closures
Func�ons are First-Class Ci�zens Docstrings
Anonymous Func�ons: lambda Generators
Namespaces and Scope Uses of _ and __ in Names Recursion- Async Func�ons - Excep�ons
Keyword-only Arguments Mutable and Immutable Arguments
Decorators
www.ihubtalent.com
Top Companies Using
Specifying a�ributes and behaviors
Inheritance - Case Study
Objects in Python
Introducing object-oriented
Modules and packages
Hiding details and crea�ng the public interface
Objects and classes
Object-Oriented Design
Organizing module content
Composi�on
Who can access my data? - Third-party Libraries - Case Study
Crea�ng python classes Objects in python
Object-Oriented Design What are Objects?
Dataclasses
Inheritance
A�ribute Access In Self Defense
Name Mangling for privacy
Proper�es for A�ribute Access
Inherit from a Parent Class, Override a Method
Class and Object A�ributes
Class Methods Instance Methods
Simple Objects
Add a Method, Get Help from your Parent with super() Mul�ple Inheritance , Mixins
Methods, Ini�aliza�on A�ributes
Sta�c Methods Duck Typing Magic Methods
Direct Access Ge�ers and Se�ers
Aggrega�on and Composi�on
Method Types
When to Use Objects or Something else
Define a Class with class,
Named Tuples
A�rs
Proper�es for Computed Values
Objects Oriented Python Object Oriented Design
www.ihubtalent.com
Top Companies Using
Python built in func�ons
Mul�ple Inheritance
Abstract base classes
Case Study
When to use Object-Oriented Programming
Manager objects Case Study
When Objects are Alike When Objects are Alike
When to Use Object-Oriented Programming
Case Study
Adding behaviors to class data with proper�es
Python Data Structures
Basic Inheritance
Polymorphism
Python Data Structures
Treat objects as objects
Empty Objects Tuples and named Tuples
Excep�ons
Data classes , Dic�onaries
Raising excep�ons
Lists, Sets, Extending built-in func�ons Case Study
Python Object-Oriented Shortcuts Python Object-Oriented Shortcuts
Case Study
Strings and Serializa�on
Case Study
Func�ons are objects too
Strings
The Iterator Pa�ern
Design pa�erns in brief
Filesystem paths
An alterna�ve to method overloading
Strings and Serializa�on
The Iterator Pa�ern
Generators - Corou�nes - Case Study
Regular expressions
Serializing objects
Iterators - Comprehensions
www.ihubtalent.com
Top Companies Using
Count Items with Counter()
Iterate over Code Structures with itertools Print Nicely with pprint() -Get Random
Order by Key with OrderedDict() - Deque
More Ba�eries: Get Other Python Code -
Virtual Environments
Python Design Pa�erns The decorator Pa�ern
The State Pa�ern The Singleton Pa�ern
The observer Pa�ern
The adapter Pa�ern The facade Pa�ern
The Strategy Pa�ern
The flyweight Pa�ern The command Pa�ern
The template Pa�ern
Tes�ng with pytest Why test? - Unit tes�ng
Imita�ng expensive objects
The abstract factory Pa�ern
How much tes�ng is enough- Case Study
Concurrency
Import a Module
Packages
Threads
Modules and import Statement
Tes�ng Object-Oriented Programs
Import a Module with Another Name
Rela�ve and Absolute Imports
Concurrency
Modules, Packages, and Goodies
Mul�processing
The Module Search Path
Import Only What You want from a Module
Goodies in the Python Standard Library Handle Missing Keys with setdefault() and defaultdict()
Tes�ng Object-Oriented Programs
The composite Pa�ern
Futures Aysnc IO - Case Study
Namespace Packages , Modules Vs Objects
www.ihubtalent.com
Top Companies Using
Test plans
PyTest for Python Tes�ng
Introducing automa�c tests and test suites
Introducing test-driven development and unit tests Test-driven development, Test units
Integra�on tests, Func�onal tests
The tes�ng pyramid, The tes�ng trophy Tes�ng distribu�ons and coverage
Test Doubles
Understanding acceptance tests and doubles
Introducing test doubles
Checking behaviors with spies
Managing dependencies with dependency injec�on Using dependency injec�on frameworks
Building applica�ons, the TDD way
So�ware Tes�ng and Test-Driven Development
Ge�ng Started with So�ware Tes�ng - Introducing so�ware tes�ng and quality control
Using dummy objects
Test-Driven Development (TDD)
Scaling the Test Suite Scaling tests
Mul�ple test cases, Organizing tests
Using mocks
Understanding the tes�ng pyramid and trophy
Replacing components with stubs
Moving e2e to func�onal
Understanding integra�on and func�onal tests
Preven�ng regressions
Working with mul�ple suites
Replacing dependencies with fakes
Carrying out performance tests Compile suite , Commit tests, Smoke tests
Star�ng projects with TDD
Enabling con�nuous integra�on , Performance tes�ng
Wri�ng PyTest fixtures , Using fixtures for dependency injec�on Managing temporary data with tmp_path Tes�ng I/O with capsys, Running subsets of the test suites
Running tests with PyTest
www.ihubtalent.com
Top Companies Using
R
Configuring the test suite
Using Behavior-driven development Wri�ng acceptance tests Wri�ng first test, Defining a feature file
Genera�ng fixtures
Declaring the scenario, Running the scenario test
Performing ac�ons with the When step Assessing condi�ons with the Then step Embracing specifica�ons by example
PyTest Essen�al Plugins
Further setup with the And step
PyTest Essen�al Plugins Using pytest-cov for coverage repor�ng Coverage as a service Using pytest-benchmark for benchmarking
Dynamic and Parametric Tests and Fixtures
Genera�ng tests with parametric tests
Find Exact Beginning Match with match()
Comparing benchmark runs
Running tests in parallel with pytest-xdist
Split at Matches with split() , Replace at Matches with sub()
Playing with data (text and binary)
Convert Bytes/String with binascii() - BitOperators
Text Strings: Unicode
Other Binary Data Tools
Tes�ng mul�ple python versions with Tox
Pa�erns: Special Characters, Pa�erns: Using specifiers
Introducing Tox
Normaliza�on
UTF-8, Encode
Bytes and bytearray
Using flaky to rerun unstable tests
Managing Test Environments with Tox
Python 3 Unicode Strings
Text Strings: Regular Expressions
Convert Binary Data with struct
Using pytest-testmon to rerun tests on code changes
Find FirstMatch with search() , Find All Matches with findall()
Pa�erns: Specifying match() Output
Binary Data
Using environments for more that Python Versions
Decode, HTML En��es
www.ihubtalent.com
Top Companies Using
www.ihubtalent.com
Top Companies Using
Leap Year, The date�me module , Using the �me module
File Opera�ons
Copy with copy()
Files and Directories
All the Conversions, Alterna�ve Modules Read and Write Dates and �mes
File Input and Output Create or Open with open(), Write a Text File with print() Write a Text File with write() , Read a Text File with read(), readline(), or readlines()
Close Files Automa�cally by using with, Change Posi�on with seek()
Memory Mapping
Calendars and Clocks
Write a Binary File with read() ,Read a Binary File with read()
Check existence with exists() Check Type with isfile()
Changing Name with rename()
Change permissions with chmod(), Change Ownership with chown()
Directory Opera�ons
Changing current directory with chdir() List Matching Files with glob()
Processes and Concurrency Program and Processes Create a Process with subprocess Create a Process with mul�processing Kill a Process with terminate Get System Info with os
Create with mkdir(), Delete with rmdir()
Pathnames, BytesIO and StringIO
Link with link() or symlink()
Get Process Info with psu�l
Command Automa�on Invoke Other Command Helpers Concurrence Queues Processes
Delete a File with remove()
List contents with listdir(),
Twisted
Threads - Concurrent.futures
Asyncio, Redis - Beyond Queues
Green Threads and gevent
www.ihubtalent.com
Know How Closures interact with Variable Scope
Ini�alize Parent Classes with super
Accept Func�ons Instead of Classes for Simple Interfaces
Use Node and Docstrings to Specify Dynamic Default Arguments
Define Func�on Decorators with functools.wraps
Compose Mul�ple Generators with yield from
Compose Classes Instead of Nes�ng Many Levels of Built-in Types
Provide Op�onal Behavior with Keywork Arguments
Comprehensions and Generators
Avoid Repeated Work in Comprehensions by Using Assignment Expressions Avoid More Than Two Control Subexpressions in Comprehensions
Be Defensive When Itera�ng Over Arguments
Avoid Causing State Transi�ons in Generators with throw
Classes and Interfaces
Avoid Injec�ng Data into Generators with send
Enforce Clarity with Keyword-Only and Posi�onal-Only Arguments
Consider Generator Expressions for Large List Comprehensions
Consider Generators Instead of Returning Lists
Consider itertools for Working with Iterators and Generators
Use Comprehensions Instead of map and filter
Reduce Visual Noise with Posi�onal Arguments
Use @classmethod Polymorphism to Construct Objects Generically
Be Cau�ous when relying on dict inser�on Ordering
Prefer default dict Over setdefault to Handle Missing Items in Internal State
Prefer enumerate over range
Know How to Construct Key-Dependent Default Values with __missing__
Func�ons
Lists and Dic�onaries
Prefer Raising excep�ons to Returning None
Differences between bytes and str
Pythonic Thinking
Wri�ng helper func�ons instead of complex expressions
Prevent Repe��on with Assignment Expressions
Mul�ple Assignment Unpacking Over Indexing
Sort by Complex Criteria Using the key parameter
Prefer get Over in and KeyError to Handle Missing Dic�onary Keys
Effec�ve and Performant Python
Never Unpack more than three variables when func�ons return mul�ple values
Using zip to process Iterators in Parallel
Follow PEP 8 Style Guide
Avoid Else blocks a�er for & while loops
Know How to Slice Sequences
Interpolated F-strings over C-style Format strings and str.format
Avoid Striding and Slicing in a Single Expression, Prefer Catch-All unpacking over slicing
www.ihubtalent.com
Use subprocess to Manage Child Processes
Prefer Public A�ributes Over Private Ones
Register Class Existence with __init_subclass__
Consider Composing Func�onality with Mix-in Classes
Use Plain A�ributes Instead of Se�er and Ge�er Methods Consider @property Instead of Refactoring A�ributes
Validate Subclasses with __init_subclass__
Inherit from collec�ons.abc for Custom Container Types
Annotate Class A�ributes with __set_name__
Concurrency and Parallelism
Use Descriptors for Reusable @property Methods Use __geta�r__, __geta�ribute__, and __seta�r__ for Lazy A�ributes
Meta classes and A�ributes
Prefer Class Decorators Over Metaclasses for Composable Class Extensions
Know How to Recognize When Concurrency Is Necessary
Understand How Using Queue for Concurrency Requires Refactoring
-When Threads Are Necessary for Concurrency
Avoid Crea�ng New Thread Instances for On-demand Fan-out
Mix Threads and Corou�nes to Ease the Transi�on to asyncio
Take Advantage of Each Block in try/except/else/finally
Use Lock to Prevent Data Races in Threads
Consider contextlib and with Statements for Reusable try
Achieve Highly Concurrent I/O with Corou�nes
Use Threads for Blocking I/O, Avoid for Parallelism
Make pickle Reliable with copyreg
Consider ThreadPoolExecutor -
Know How to Port Threaded I/O to asyncio
Use Queue to Coordinate Work Between Threads
Use date�me Instead of �me for Local Clocks
Use decimal When Precision Is Paramount Profile Before Op�mizing
Avoid Blocking the asyncio Event Loop to Maximize Responsiveness Consider concurrent.futures for True Parallelism
Prefer deque for Producer& Consumer Queues for Producer–Consumer Queues Consider Searching Sorted Sequences with bisect
Robustness and Performance
Know How to Use heapq for Priority Queues Consider memoryview and bytearray for Zero-Copy Interac�ons with bytes
Tes�ng and Debugging
Encapsulate Dependencies to Facilitate Mocking and Tes�ng Consider Interac�ve Debugging with pdb, Use tracemalloc to Understand Memory Usage
Isolate Tests from Each Other with setUp, tearDown, setUpModule, and tearDownModule Use Mocks to Test Code with Complex Dependencies
Use repr Strings for Debugging Output, Verify Related Behaviors in TestCase Subclasses
Top Companies Using
www.ihubtalent.com
How to Be a Highly Performant Programmer
Profiling to Find Bo�lenecks
Visualizing cProfile Output with SnakeViz
Use Virtual Environments for Isolated and Reproducible Dependencies Write Docstrings for Every Func�on, Class, and Module Use Packages to Organize Modules and Provide Stable APIs
Know How to Break Circular Dependencies
The Fundamental Computer System
Memory Units
dealized Compu�ng Versus the Python Virtual Machine So Why Use Python?
Good Working Prac�ces
Define a Root Excep�on to Insulate Callers from APIs
Understanding Performant Python
Calcula�ng the Full Julia Set
Consider Module-Scoped Code to Configure Deployment Environments
Communica�ons Layers
Simple Timing Using the Unix �me Command
Introducing the Julia Set
Using the dis Module to Examine CPython Bytecode
Know Where to Find Community-Built Modules
Using the cProfile Module
Profiling Efficiently
Consider warnings to Refactor and Migrate Usage
Collabora�on
Compu�ng Units
Pu�ng the Fundamental Elements Together
Consider Sta�c Analysis via typing to Obviate Bugs
Simple Approaches to Timing—print and a Decorator
Using line_profiler for Line-by-Line Measurements Using memory_profiler to Diagnose Memory Usage
Different Complexity
Unit Tes�ng During Op�miza�on to Maintain Correctness
Bytecode: Under the Hood
Strategies to Profile Your Code Successfully
Introspec�ng an Exis�ng Process with PySpy
Different Approaches,
No-op @profile Decorator
Top Companies Using
www.ihubtalent.com
Es�ma�ng Pi Using Processes and Threads
Using Python Objects
How Does async/await Work?
Serial Crawler Gevent, Tornado Aioh�p
Asynchronous I/O
Shared CPU–I/O Workload Serial, Batched Results Full Async
The mul�processing Module
Introduc�on to Asynchronous Programming
An Overview of the mul�processing Module
Es�ma�ng Pi Using the Monte Carlo Method
Lessons from the Field
Serial Solu�on
File Locking
Naive Pool Solu�on
Replacing mul�processing with Joblib
Finding Prime Numbers
Using Redis as a Flag
Queues of Work
Verifying Primes Using Interprocess Communica�on
A Less Naive Pool Solu�on Using Manager.Value as a Flag
Using RawValue as a Flag
Using mmap as a Flag Redux
Using Less RAM,
Using numpy Random Numbers in Parallel Systems
Locking a Value Clusters and Job Queues
Sharing numpy Data with mul�processingSynchronizing File and Variable Access
Using mmap as a Flag
Top Companies Using
iHub’s KEY CLIENTS100 + Clients and Adding More
www.ihubtalent.com
OTHER JOB GUARANTEE COURSES
iHub Talent
100 % JOBGAURANTEEDATA SCIENCE & AI
ADVANCED
D JANGO djPYTHON
DIGITAL MARKETINGFULL STACK
www.ihubtalent.com
81069 61963
CLOUDENGINEER
#515, Nilgiri Block, Beside Metro Station
Ameerpet, Hyderabad -16.
A Cloud Data IntegrationETL Course
Full StackM E R NDEVLOPER
Full StackM E A NDEVLOPER
top related