Intermediate Python For Next Level Programming Expertise
This course is designed for those interested to learn the basics of intermediate Python programming and understanding how expert programmers work with code and what techniques they use.
About This CourseBeginner
In this course, we will be building on top of your existing basic understanding of the Python language, and programming in general. We will cover concepts that will take you to the next level of programming expertise, which includes language constructs that are typically not covered in a beginner level course – concepts like generators, decorators, callbacks, higher order functions, context managers and more.
Moreover, we will discuss some tools that are essential to the life of a professional programmer such as logging for tracking down bugs. This course also tackles parallel programming and asynchronous programming, which are other important concepts you must understand to code in a production environment.
What this course covers:
- Cross-cutting Tools (logging and testing)
- Caveats in usage of lists
- Intermediate Python concepts such as decorators, generators, context managers
- Parallel programming, multithreading, synchronization, locks
- Asynchronous programming (asyncio)
- Plotting data
- Regular Expressions
Our Promise to You
By the end of this course, you will have learned intermediate Python programming.
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Section 1 - Introduction
Environment Setup And Jupyter Notebook
Section 2 - Cross - Cutting Tools
Logging And How To Debug Code In Realtime
Testing And Test Driven Development
Section 3 - Intermediate Programming Concepts
List Mutability And Deep Copying
Generators And Memory Efficiency
Generators Case Study
Section 4 - Time Saving Features
Decorator Case Study – Memorize
More On Context Managers
Section 5 - Parallel And Asynchronous Programming
Multithreading – Multiple Tasks Simultaneously
Synchronization Issues And Locks
Asynchronous Programming : Asyncio, Async, Await
Section 6 - Functional Programming
Basics Of Functional Programming Map
Filter And Reduce, Rationale For Functional Programming
Section 7 - Applications
Plotting Case Study
Pattern Matching With Regular Expressions
Modular RegExp And How Not To Have Headaches With RegExp
Extracting Matched Strings With RegExp
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