Course

Course Summary
Credit Type:
Course
ACE ID:
SKIL-0223
Organization:
Internal Revenue Service
Location:
Online
Length:
71.5 hours and 32 lab hours
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Upper-Division Baccalaureate 4 intermediate programming with Python
Lower-Division Baccalaureate 2 introduction to programming with Python
Description

Objective:

The course objective is to provide learners with the knowledge and skills required to be a Python novice and to build upon those skills to eventually become an advanced Python programmer, or Pythonista. The course starts by providing novice software programmers with foundational knowledge on Python's key libraries and frameworks, so they can take on basic tasks and projects using Python. From there, a Python Novice may move into the Python Apprentice role that focuses more on web programming and using Python to develop front-end projects. After that, a Python Journeyman will employ Python for tasks and projects and will be able to stitch the front-end and back-end of an application together. Finally, a learner can become a Pythonista who brings together all the insights and knowledge acquired throughout their course by focusing on wrangling data, network programming, and developing continual testing strategies.

Learning Outcomes:

  • define Python first class functions and lamdas
  • examine classes and inheritance in Python
  • wrangle Excel data with Python
  • program sockets in Python
  • explore some of the significant features of Python and gain a solid foundation on the characteristics and use cases of the Python programming language
  • work with complex data types in Python
  • explore Python conditional statements and loops
  • explore advanced topics in Python
  • compare data structures and algorithms in Python
  • employ unit testing in Python
  • work with HTTP requests in Python
  • build web apps using Flask in Python
  • explore multithreading and concurrency in Python
  • develop and debug code using the PyCharm IDE
  • analyze design patterns in Python.

General Topics:

  • Getting started with Python: introduction
  • Complex data types in Python: working with lists and Tuples in Python
  • Complex data types in Python: working with dictionaries and sets in Python
  • Complex data types in Python: shallow and deep copies in Python
  • Conditional statements and loops: if-else control structures in Python
  • Conditional statements and loops: the basics of for loops in Python
  • Conditional statements and loops: advanced operations using for loops in Python
  • Conditional statements and loops: while loops in Python
  • Functions in Python: introduction
  • Functions in Python: gaining a deeper understanding of Python functions
  • Functions in Python: working with advanced features of Python functions
  • Advanced Python topics: file operations in Python
  • Advanced Python topics: exceptions and command line arguments
  • Advanced Python topics: modules and virtual environments
  • Advanced Python topics: migrating from Python 2 to Python 3
  • Python classes and inheritance: introduction
  • Python classes and inheritance: getting started with classes in Python
  • Python classes and inheritance: working with inheritance in Python
  • Python classes and Inheritance: advanced functionality using Python classes
  • Data structures and algorithms in Python: fundamental data structures
  • Data structures and algorithms in Python: implementing data structures
  • Data structures and algorithms in Python: sorting algorithms
  • Data structures and algorithms in Python: implementing sorting algorithms
  • Data structures and algorithms in Python: trees and graphs
  • Data structures and algorithms in Python: implementing trees and graphs
  • Python unit testing
  • Python Requests
  • Flask in Python
  • Python concurrent programming
  • Introduction to using PyCharm IDE
  • Excel with Python
  • Socket programming in Python
  • Python design patterns
Instruction & Assessment

Instructional Strategies:

  • Computer Based Training
  • Laboratory
  • Practical Exercises

Methods of Assessment:

  • Examinations
  • Quizzes

Minimum Passing Score:

70%
Supplemental Materials