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| Topic | Sub Topics |
|---|---|
| Introduction to Python | Python Syntax and Structure, Comments, Docstrings, Variables, Data Types, Integers, Floats, Strings, Booleans, Input and Output, Type Conversion and Casting |
| Python Operators | Arithmetic Operators, Assignment Operators, Comparison Operators, Logical Operators, Bitwise Operators |
| Conditional Statements | if, elif, else |
| Loops | for Loops, while Loops, Nested Loops |
| Loop Control Statements | break, continue, pass |
| Data Structures in Python | String Methods, Slicing, Indexing, String Formatting, Lists, List Methods and Slicing, List Comprehensions, Using Lists as Stacks, Using Lists as Queues, Nested List Comprehensions, del Statement, Tuples, Immutability, Tuple Methods, Dictionaries (Key-Value Pairs, Methods, Comprehensions), Sets (Methods, Operations), Arrays |
| Functions | Defining Functions, Positional & Keyword Arguments, Default Arguments, *args & **kwargs, Return Statement, Lambda Functions, Scope & Lifetime (Local, Global, nonlocal) |
| Classes and Objects | Creating Classes, Creating Objects, Attributes and Methods |
| Inheritance | Single Inheritance, Multiple Inheritance, Multilevel Inheritance, Overriding Methods, Using super() |
| Polymorphism | Method Overloading (Conceptual), Method Overriding |
| Encapsulation | Encapsulation Concepts |
| File Handling | open(), File Modes (r, w, a, r+), read(), readline(), readlines(), write(), writelines(), Binary Files |
| Working with File Paths | os Module, pathlib Module |
| File Iterators | Reading Files Line by Line |
| Working with CSV & JSON | csv Module, json Serialization & Deserialization |
| Exception Handling | try-except Blocks, Multiple except Blocks, else & finally, raise Statement |
| Built-in Modules | math, os, sys, time, random, re, itertools, collections |
| Modules & Packages | Creating Custom Modules, __init__.py, Importing, Working with Packages |
| Iterators & Generators | Custom Iterators (__iter__, __next__), Generators, yield, Generator Expressions |
| Decorators | Function Decorators, Multiple Decorators, Class Decorators |
| Regular Expressions | re.match, re.search, re.findall, re.split, re.sub, Character Classes, Quantifiers, Anchors |
| Functional Programming | map(), filter(), reduce(), Lambda Functions, Comprehensions |
| Date & Time | datetime, strftime, strptime, time(), sleep(), ctime() |
| Multithreading | Threading Basics, join(), is_alive(), Daemon Threads, Locks, RLock, Semaphore, Queue |
| Multiprocessing | GIL Concept, Process, Pool, Manager, Value, Array |
| Testing & Debugging | unittest, Assertions, Test Discovery, pdb Debugger |
| Advanced File & Directory Operations | Large Files, tempfile, os.walk(), shutil, File Errors Handling |
| Data Analysis with Pandas | DataFrame, Series, CSV/Excel/SQL/JSON, fillna, dropna, Filtering, GroupBy, Merge, Sort |
| Cloud-Lab Support | Hands-on Cloud Practice & Lab Support |
One of the notable advantages of online Python training with a real trainer is the opportunity to engage in live sessions where participants can interact directly with experienced instructors.
Embarking on a journey to master Python programming is an exciting endeavor, and opting for online training with a real trainer can make the learning experience more engaging and effective. Online Python training programs with live instructors offer a dynamic and interactive environment, ensuring that learners receive personalized guidance throughout their coding journey.
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