Software Engineering in Python Course

Course Summary

The Software Engineering in Python training course is targeted at Python users who lack a formal programming background (e.g., Data Scientists, Machine Learning Engineers, DevOps Engineers, etc.).

The course begins with learning software engineering techniques such as object-oriented programming. Next, the course covers modularity, test-driven development, and other best practices. The course concludes with students learning to write, document, and maintain production-level code in Python.

Purpose

Learn software engineering techniques using Python.

Audience

Developers and developer teams looking to learn the best practices of using Python.

Role

Data Engineer - Data Scientist - Software Developer - System Administrator - Web Developer

Skill Level

Introduction

Style

Workshops

Duration

3 Days

Related Technologies

Python

Download

Productivity Objectives:

Describe object-oriented programming.
Explain test-driven development best practices.
Adopt Python code for software engineering.

Learn About Virtual Training

Request Information

Get your team upskilled or reskilled today. Chat with one of our experts to create a custom training proposal. Fully customized at no additional cost.

If you are not completely satisfied with your training class, we'll give you your money back.

about our training

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
Expert Practitioners

Industry experts with 15+ years of industry experience that bring their battle scars into the classroom.
Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
Fully Customized

One-size-fits-all doesn't apply to training teams. That's where we come in!

What You'll Learn

In the Software Engineering in Python training course, you'll learn:

Python Review (if needed)
- Slicing
- List/Dict Comprehensions
- Tuples
- enumerate()
- zip()
- *args / **kwargs
- := operator (Python 3.8)
- modules / __main__
- Packages
- exception handling
- any additional topics, as needed
Documentation
- Meditations on The Zen of Python
  - …explicit is better than implicit
  - …simple is better than complex
  - …complex is better than complicated
  - …readability counts
- Ergo…Documentation is Important!
- Commenting vs. Documenting
  - Docstrings / help() / pydoc
Object-Oriented Python
- What is a Class? (and WHY do we need them?)
- The Structure of a Python Class
- The “self” syntax
- Class object vs. instance
- Inheritance
- Mixins
Advanced Datatypes: The Collections Module
- Ordered Dictionaries
- Default Dictionaries
- Deques
- Named Tuples
- Counters
Functional Programming
- Lambda Functions
- map()
- filter()
- The functools module
  - lru_cache()
  - Partial Functions
Decorators
- Use Cases
  - error handling
  - setup/teardown
  - other boilerplate code
- Writing Our Own Decorators
Object-Oriented Python: Redux
- Properties (not Getters and Setters)
- Static Methods
- Class Methods
- The Python Data Model
Iterables, Iterators, and Generators
- Containers vs. Iterables vs. Iterators
- Generators
- Generator Expressions
- vs. list/set/dict Comprehensions
- The itertools Module
Testing
- Unit Testing
- Test-Driven Development
- Test Coverage
- Dirty Services
- Mocking
- Autospec
- __name__ == ‘__main__’
- pytest
Logging
- What is it?
- Why do we need it?
- Log Levels
- The logging Module
Debugging with pdb
- Debugging from the Command Line
- Using pdb to Test a Module
- Launching pdb From Within Your Program
Context Managers
- The with Statement
- Writing Your Own
Virtual Environments
- Virtualenv
- Venv
- pipenv
Type Hinting
- What, Why, How?
- Comparison with Statically-Typed Languages
Linting
- Meditations on PEP-8
- flake8 / black / pycodestyle