Master Python with Expert Guidance: Dynamic Online Training with Real Instructors
Curriculum
Topic | Subtopic | Basic | Advanced | Intermediate | Industry-ready |
---|---|---|---|---|---|
Introduction to Python | |||||
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 | ✓ | ✓ | ✓ | ✓ | |
Membership Operators (in, not in) | ✓ | ✓ | ✓ | ✓ | |
Identity Operators (is, is not) | ✓ | ✓ | ✓ | ✓ | |
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 | ✓ | ✓ | ✓ | ✓ | |
The del statement | ✓ | ✓ | ✓ | ✓ | |
Tuples | ✓ | ✓ | ✓ | ✓ | |
Immutability | ✓ | ✓ | ✓ | ✓ | |
Tuple Methods | ✓ | ✓ | ✓ | ✓ | |
Dictionaries (Basic) | ✓ | ✓ | ✓ | ✓ | |
Key-Value Pairs | ✓ | ✓ | ✓ | ✓ | |
Dictionary Methods | ✓ | ✓ | ✓ | ✓ | |
Dictionary Comprehensions | ✓ | ✓ | ✓ | ✓ | |
Sets | ✓ | ✓ | ✓ | ✓ | |
Set Methods | ✓ | ✓ | ✓ | ✓ | |
Set Operations | ✓ | ✓ | ✓ | ✓ | |
Arrays | ✓ | ✓ | ✓ | ✓ | |
Functions | |||||
Defining Functions | ✓ | ✓ | ✓ | ✓ | |
Positional and Keyword Arguments | ✓ | ✓ | ✓ | ✓ | |
Default Arguments | ✓ | ✓ | ✓ | ✓ | |
Arbitrary Arguments (*args, **kwargs) | ✓ | ✓ | ✓ | ✓ | |
Return Statement | ✓ | ✓ | ✓ | ✓ | |
Lambda Functions (Anonymous Functions) | ✓ | ✓ | ✓ | ✓ | |
Scope and Lifetime of Variables (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; Python doesn’t support this directly) | ✗ | ✗ | ✓ | ✓ | |
Method Overriding | ✗ | ✗ | ✓ | ✓ | |
Encapsulation | |||||
Encapsulation | ✗ | ✗ | ✓ | ✓ | |
Reading and Writing Files | |||||
Opening Files (open()) | ✗ | ✗ | ✓ | ✓ | |
Modes: r, w, a, r+, etc. | ✗ | ✗ | ✓ | ✓ | |
Reading: read(), readline(), readlines() | ✗ | ✗ | ✓ | ✓ | |
Writing: write(), writelines() | ✗ | ✗ | ✓ | ✓ | |
Working with binary files (rb, wb) | ✗ | ✗ | ✓ | ✓ | |
Working with File Paths | |||||
Using os and pathlib modules. | ✗ | ✗ | ✓ | ✓ | |
File Iterators | |||||
Reading files line by line with for loops. | ✗ | ✗ | ✓ | ✓ | |
Handling Exceptions in File Operations | |||||
Handling Exceptions in File Operations | ✗ | ✗ | ✓ | ✓ | |
Working with CSV and JSON Files | |||||
csv module for reading and writing CSV files. | ✗ | ✗ | ✓ | ✓ | |
json module for JSON serialization and deserialization | ✗ | ✗ | ✓ | ✓ | |
Try-Except Blocks | |||||
Single except block | ✗ | ✗ | ✓ | ✓ | |
Multiple except blocks | ✗ | ✗ | ✓ | ✓ | |
Exception Handling | |||||
Using else and finally | ✗ | ✗ | ✓ | ✓ | |
raise statement. | ✗ | ✗ | ✓ | ✓ | |
Built-in Modules | |||||
Popular modules: math, os, sys, time, random, re, itertools, collections. | ✗ | ✗ | ✓ | ✓ | |
Creating Custom Modules | |||||
Creating Custom Modules | ✗ | ✗ | ✓ | ✓ | |
Working with Packages | |||||
__init__.py and structuring packages. | ✗ | ✗ | ✓ | ✓ | |
Importing | |||||
Importing | ✗ | ✗ | ✓ | ✓ | |
Iterators | |||||
Creating custom iterators using __iter__ and __next__. | ✗ | ✗ | ✓ | ✓ | |
Generators | |||||
Using yield in functions. | ✗ | ✗ | ✓ | ✓ | |
Generator expressions | ✗ | ✗ | ✓ | ✓ | |
Function Decorators | |||||
Creating and using decorators. | ✗ | ✗ | ✓ | ✓ | |
Using multiple decorators on a single function | ✗ | ✗ | ✓ | ✓ | |
Class Decorators | |||||
Class Decorators | ✗ | ✗ | ✓ | ✓ | |
Regular Expressions | |||||
re.match, re.search, re.findall, re.split, re.sub | ✗ | ✗ | ✓ | ✓ | |
Literal characters, special characters. | ✗ | ✗ | ✓ | ✓ | |
Quantifiers (*, +, ?, {}) | ✗ | ✗ | ✓ | ✓ | |
Character classes and sets ([a-zA-Z], \d, \w, etc.) | ✗ | ✗ | ✓ | ✓ | |
Anchors (^, $) | ✗ | ✗ | ✓ | ✓ | |
Functional Programming | |||||
Using map() for transformations. | ✗ | ✗ | ✓ | ✓ | |
Filtering sequences with filter(). | ✗ | ✗ | ✓ | ✓ | |
Aggregating data with functools.reduce() | ✗ | ✗ | ✓ | ✓ | |
Lambda Functions | ✗ | ✗ | ✓ | ✓ | |
List, Set, and Dictionary Comprehensions | ✗ | ✗ | ✓ | ✓ | |
Working with Dates and Times | |||||
datetime.date, datetime.time, datetime.datetime. | ✗ | ✗ | ✓ | ✓ | |
Formatting with strftime. | ✗ | ✗ | ✓ | ✓ | |
Parsing with strptime | ✗ | ✗ | ✓ | ✓ | |
Time functions: time(), sleep(), ctime() | ✗ | ✗ | ✓ | ✓ | |
Introduction to Threads | |||||
What is threading? | ✗ | ✓ | ✓ | ✓ | |
Difference between threads and processes. | ✗ | ✓ | ✓ | ✓ | |
Using the threading Module | |||||
Creating and starting threads. | ✗ | ✓ | ✓ | ✓ | |
Using join() and is_alive() methods. | ✗ | ✓ | ✓ | ✓ | |
Daemon threads and their use | ✗ | ✓ | ✓ | ✓ | |
Thread Synchronization | |||||
Avoiding race conditions using Lock, RLock, Semaphore. | ✗ | ✓ | ✓ | ✓ | |
Thread-safe data structures (e.g., queue.Queue). | ✗ | ✓ | ✓ | ✓ | |
Introduction to Multiprocessing | |||||
Why use multiprocessing over threading? (Global Interpreter Lock - GIL) | ✗ | ✓ | ✓ | ✓ | |
Creating and managing processes | ✗ | ✓ | ✓ | ✓ | |
Using the multiprocessing Module | |||||
Process, Pool, and Manager. | ✗ | ✓ | ✓ | ✓ | |
Sharing data using Value and Array. | ✗ | ✓ | ✓ | ✓ | |
Unit Testing | |||||
Writing tests using unittest. | ✗ | ✓ | ✓ | ✓ | |
Using assertions (assertEqual, assertRaises, etc.). | ✗ | ✓ | ✓ | ✓ | |
Test Discovery | |||||
Using unittest or pytest to find and run test cases | ✗ | ✓ | ✓ | ✓ | |
Debugging | |||||
Using pdb (Python Debugger) for step-by-step debugging | ✗ | ✓ | ✓ | ✓ | |
Advanced File Operations | |||||
Reading and writing large files efficiently. | ✗ | ✓ | ✓ | ✓ | |
Working with temporary files using tempfile. | ✗ | ✓ | ✓ | ✓ | |
Directory Operations | |||||
Managing directories with os and shutil. | ✗ | ✓ | ✓ | ✓ | |
Walking through directories using os.walk() | ✗ | ✓ | ✓ | ✓ | |
Error Handling | |||||
Catching and handling file-related exceptions (FileNotFoundError, PermissionError). | ✗ | ✓ | ✓ | ✓ | |
Data Analysis with Pandas | |||||
DataFrame and Series objects. | ✗ | ✓ | ✓ | ✓ | |
Pandas | |||||
Reading and writing data from various formats (CSV, Excel, SQL, JSON). | ✗ | ✓ | ✓ | ✓ | |
Handling missing values (fillna, dropna). | ✗ | ✓ | ✓ | ✓ | |
Data type conversions. | ✗ | ✓ | ✓ | ✓ | |
String operations on columns. | ✗ | ✓ | ✓ | ✓ | |
Filtering and indexing. | ✗ | ✓ | ✓ | ✓ | |
Grouping and aggregations (groupby, pivot_table). | ✗ | ✓ | ✓ | ✓ | |
Sorting and ranking. | ✗ | ✓ | ✓ | ✓ | |
Joining and merging DataFrames. | ✗ | ✓ | ✓ | ✓ | |
Cloud-Lab Support | |||||
Cloud-Lab Support | ✗ | ✓ | ✓ | ✓ |
Pricing Plans
Sessions | Duration | Discounted Cost (INR) | Original Cost (INR) |
---|---|---|---|
Basic 10 | 10 Days | 8,000 | 16,000 |
Basic 20 | 20 Days | 6,000 | 12,000 |
Intermediate | 30 Days | 9,000 | 18,000 |
Advanced | 45 Days | 15,000 | 30,000 |
Industry-Ready | 6 Months (Per day 6 sessions) | 60,000 | 120,000 |
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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