- Course Overview
- Pre-Requisites
- Code Projects and Notebooks
Online
₹ 455 3,499
Quick facts
particular | details | |
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
Python 3: Deep Dive (Part 1 - Functional) online certification is created by Fred Baptiste, a professional developer, mathematician, and instructor, for professional programmers and developers who want to dive deep into Python and learn all of its functions and operations from the ground up. It is presented by Udemy, a US-based ed-tech company that provides students with the best and most up-to-date skills through its online courses.
Python 3: Deep Dive (Part 1 - Functional) syllabus contains complicated topics, learners are advised to have prior knowledge of the following topics in order to get the most out of the course: variables and simple types such as str, bool, int, and float types, for and while loops, if/else statements, using simple lists, tuples, dictionaries, and sets, defining functions using the def statement, writing simple classes using the class keyword and the __init__ method, writing instance methods, and importing modules from the standard library.
The highlights
- Certificate of completion
- Self-paced course
- Online course
- English videos with multi-language subtitles
- 44.5 hours of pre-recorded video content
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and TV
Program offerings
- Certificate of completion
- Self-paced course
- English videos with multi-language subtitles
- 44.5 hours of pre-recorded video content
- 2 articles
- 168 downloadable resources
- 30-day money-back guarantee
- Unlimited access
Course and certificate fees
Fees information
certificate availability
certificate providing authority
What you will learn
After completing the Python 3: Deep Dive (Part 1 - Functional) certification course, learners will get an understanding of the Python 3 programming language, including variables, memory, namespaces, scopes, optimizations, and advanced numerical data types such as booleans, integers, floats, decimals, fractions, and complex numbers. Candidates will also be able to apply functional programming approaches, create advanced decorators like memoization and single dispatch generic functions, and comprehend Python's compile-time and how it influences a programme.
Who it is for
The syllabus
Introduction
A Quick Refresher - Basics Review
- Introduction
- The Python Type Hierarchy
- Multi-Line Statements and Strings
- Variable Names
- Conditionals
- Functions
- The While Loop
- Break, Continue and the Try Statement
- The For Loop
- Classes
Variables and Memory
- Introduction
- Variables are Memory References
- Reference Counting
- Garbage Collection
- Dynamic vs Static Typing
- Variable Re-Assignment
- Object Mutability
- Function Arguments and Mutability
- Shared References and Mutability
- Variable Equality
- Everything is an Object
- Python Optimizations: Interning
- Python Optimizations: String Interning
- Python Optimizations: Peephole
Numeric Types
- Introduction
- Integers: Data Types
- Integers: Operations
- Integers: Constructors and Bases - Lecture
- Integers: Constructors and Bases - Coding
- Rational Numbers - Lecture
- Rationals Numbers - Coding
- Floats: Internal Representations - Lecture
- Floats: Internal Representations - Coding
- Floats: Equality Testing - Lecture
- Floats: Equality Testing - Coding
- Floats: Coercing to Integers - Lecture
- Floats: Coercing to Integers - Coding
- Floats: Rounding - Lecture
- Floats: Rounding - Coding
- Decimals - Lecture
- Decimals - Coding
- Decimals: Constructors and Contexts - Lecture
- Decimals: Constructors and Contexts - Coding
- Decimals: Math Operations - Lecture
- Decimals: Math Operations - Coding
- Decimals: Performance Considerations
- Complex Numbers - Lecture
- Complex Numbers - Coding
- Booleans
- Booleans: Truth Values - Lecture
- Booleans: Truth Values - Coding
- Booleans: Precedence and Short-Circuiting - Lecture
- Booleans: Precedence and Short-Circuiting - Coding
- Booleans: Boolean Operators
- Booleans: Boolean Operators - Coding
- Comparison Operators
Function Parameters
- Introduction
- Argument vs Parameter
- Positional and Keyword Arguments - Lecture
- Positional and Keyword Arguments - Coding
- Unpacking Iterables - Lecture
- Unpacking Iterables - Coding
- Extended Unpacking - Lecture
- Extended Unpacking - Coding
- *args - Lecture
- *args - Coding
- Keyword Arguments - Lecture
- Keyword Arguments - Coding
- **kwargs
- Putting it all Together - Lecture
- Putting it all Together - Lecture
- Application: A Simple Function
- Parameter Defaults - Beware
- Parameter Defaults - Beware Again!!
First-Class Functions
- Introduction
- Docstrings and Annotations - Lecture
- Docstrings and Annotations - Coding
- Lambda Expressions - Lecture
- Lambda Expressions - Coding
- Lambdas and Sorting
- Challenge - Randomize an Iterable using Sorted!!
- Function Introspection - Lecture
- Function Introspection - Coding
- Callables
- Map, Filter, Zip, and List Comprehensions - Lecture
- Map, Filter, Zip, and List Comprehensions - Coding
- Reducing Functions - Lecture
- Reducing Functions - Coding
- Partial Functions - Lecture
- Partial Functions - Coding
- The operator Module - Lecture
- The operator Module - Coding
Scopes, Closures and Decorators
- Introduction
- Global and Local Scopes - Lecture
- Global and Local Scopes - Coding
- Nonlocal Scopes - Lecture
- Nonlocal Scopes - Coding
- Closures - Lecture
- Closures - Coding
- Closure Applications - Part 1
- Closure Applications - Part 2
- Decorators (Part 1) - Lecture
- Decorators (Part 1) - Coding
- Decorator Application (Timer)
- Decorator Application (Logger, Stacked Decorators)
- Decorator Application (Memoization)
- Decorators (Part 2) - Lecture
- Decorators (Part 2) - Coding
- Decorator Application (Decorator Class)
- Decorator Application (Decorating Classes)
- Decorator Application (Dispatching) - Part 1
- Decorator Application (Dispatching) - Part 2
- Decorator Application (Dispatching) - Part 3
Tuples as Data Structures and Named Tuples
- Introduction
- Tuples as Data Structures - Lecture
- Tuples as Data Structures - Coding
- Named Tuples - Lecture
- Named Tuples - Coding
- Named Tuples - Modifying and Extending - Lecture
- Named Tuples - Modifying and Extending - Coding
- Named Tuples - DocStrings and Default Values - Lecture
- Named Tuples - DocStrings and Default Values - Coding
- Named Tuples - Application - Returning Multiple Values
- Named Tuples - Application - Alternative to Dictionaries
Modules, Packages and Namespaces
- Introduction
- What is a Module?
- How does Python Import Modules?
- Imports and importlib
- Import Variants and Misconceptions - Lecture
- Import Variants and Misconceptions - Coding
- Reloading Modules
- Using __main__
- Modules Recap
- What are Packages? - Lecture
- What are Packages? - Coding
- Why Packages?
- Structuring Packages - Part 1
- Structuring Packages - Part 2
- Namespace Packages
- Importing from Zip Archives
Extras
- Introduction
- Additional Resources
- Python 3.6 Highlights
- Python 3.6 - Dictionary Ordering
- Python 3.6 - Preserved Order of kwargs and Named Tuple Application
- Python 3.6 - Underscores in Numeric Literals
- Python 3.6 - f-Strings
- Random: Seeds
- Random Choices
- Random Samples
- Timing code using *timeit*
- Don't Use *args and **kwargs Names Blindly
- Command Line Arguments
- Sentinel Values for Parameter Defaults
- Simulating a simple switch in Python
Instructors
Mr Fred Baptiste
Developer and Mathematician
Udemy
Ph.D