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Mastering Python: Foundations to Advanced Techniques
Mastering Python: Foundations to Advanced Techniques
Curriculum
11 Sections
63 Lessons
45 Hours
Expand all sections
Collapse all sections
Module 1: Getting Started with Python
5
1.1
Module1:Session-1
1.2
Module1: Session-2
1.3
Module1: Session-3
1.4
Module1:Session-4
1.5
Moudle-1 Session-5
Module 2: Modular Code with Functions
12
2.1
2.1 Defining and invoking functions
2.2
2.2 pass as a placeholder statement
2.3
2.3 Understanding variable scope (local vs global)
2.4
2.4 Recursive functions
2.5
2.5 Handling arbitrary arguments: *args and **kwargs
2.6
2.6 Class method convention: the self-parameter
2.7
2.7 Functions as first-class citizens
2.8
2.8 Anonymous (lambda) functions
2.9
2.9 Functional utilities: map, filter, reduce
2.10
2.10 Nested functions and closures
2.11
2.11 Decorators: enhancing functionality
2.12
2.12 Knowledge check: function-based quizzes
Module 3: Working with Data Structures
8
3.1
3.1 Text manipulation and string features
3.2
3.2 Dynamic collections: lists
3.3
3.3 Immutable tuples
3.4
3.4 Associative arrays: dictionaries
3.5
3.5 Unordered unique collections: sets
3.6
3.6 Homogeneous sequences: arrays
3.7
3.7 List comprehensions for concise loops
3.8
3.8 Knowledge check: data-structure quizzes
Module 4: Advanced Collection Types
6
4.1
4.1 Counters for frequency counts
4.2
4.2 Heap queues for priority tasks
4.3
4.3 Deques for double-ended access
4.4
4.4 OrderedDict to maintain insertion order
4.5
4.5 defaultdict for key-default handling
4.6
4.6 Knowledge check: advanced collection quizzes
Module 5: Object-Oriented Programming in Python
8
5.1
5.1 OOP fundamentals in Python
5.2
5.2 Defining classes and creating objects
5.3
5.3 Inheritance and code reuse
5.4
5.4 Polymorphism in action
5.5
5.5 Abstraction via abstract base classes
5.6
5.6 Data encapsulation best practices
5.7
5.7 Custom iterators and iterable classes
5.8
5.8 Knowledge check: OOP quizzes
Module 6: Managing Errors and Files
6
6.1
6.1 Exception handling with try/except/finally
6.2
6.2 Built-in vs custom exceptions
6.3
6.3 File I/O operations: reading and writing
6.4
6.4 Leveraging os and pathlib
6.5
6.5 Creating, removing, and navigating directories
6.6
6.6 Knowledge check: exception and file-handling quizzes
Module 7: Working with Databases
2
7.1
7.1 Connecting to MySQL databases
7.2
7.2 Interfacing with MongoDB collections
Module 8: Organizing Code with Modules & Packages
4
8.1
8.1 Structure and usage of packages
8.2
8.2 Exploring Python’s standard modules
8.3
8.3 Third-party DSA libraries
8.4
8.4 Building graphical interfaces (e.g., Tkinter, PyQt)
Module 9: Data Science Essentials
4
9.1
9.1 Core libraries: NumPy, Pandas, Matplotlib
9.2
9.2 Statistical & enhanced visualization: Seaborn, Statsmodels
9.3
9.3 Machine-learning toolkit: scikit-learn, XGBoost, LightGBM
9.4
9.4 Deep learning with TensorFlow/Keras and PyTorch
Chapter 10: Practice Hub
2
10.1
10.1 Topic-based quizzes (e.g., loops, functions)
10.2
10.2 Hands-on coding challenges spanning all topics
Chapter 11: Real-World Use Cases
6
11.1
11.1 Web & backend systems
11.2
11.2 Data analysis and visualization
11.3
11.3 AI & machine-learning pipelines
11.4
11.4 Task automation and scripting
11.5
11.5 Web scraping tools
11.6
11.6 Desktop applications & simple games
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