Apr 18, 2024  
2021-2022 Catalog 
    
2021-2022 Catalog [ARCHIVED CATALOG]

Add to Portfolio (opens a new window)

DAT 119 - Python 1


Credits: 4
4 Skills Lab Hours

Prerequisites: Any 3-credit CIT course or instructor permission

 
Description
This course introduces computer programming and techniques using Python to solve problems in data analytics. Emphasis is placed on common data types, control flow, functions, usability and reproducibility utilizing the standard library distributed with Python and selected data visualization and analysis modules. Students learn to manipulate files, Python scripts and their output using the interactive Python terminal and shell commands.


Learning Outcomes
Upon successful completion of the course, the student will:

  1. Design an algorithmic solution to solve a problem.
  2. Utilize scalar and container type variables, repetition structures, selection structures, standard library modules and built-in and user-defined functions in a Python program.
  3. Write reusable code that meets program specifications and follows best practices for reproducible data workflows.
  4. Implement file manipulation and execution of programs from a command line interface and within a Python program.
Listed Topics
  1. Introduction to computers and programming
  2. Running Python code in Jupyter notebooks, an integrated development environment and the console
  3. Variables, data types and arithmetic operators
  4. Following a style guide
  5. Decision structures and Boolean operators
  6. Repetition structures
  7. Functions and scope
  8. Lists, tuples, dictionaries and sets
  9. File input/output
  10. NumPy, Pandas and Matplotlib
Reference Materials
Official python documentation: python.org

Other reference materials deemed appropriate by the instructor


Students who successfully complete this course acquire general knowledge, skills and abilities that align with CCAC’s definition of an educated person. Specifically, this course fulfills these General Education Goals:
  • Critical Thinking & Problem Solving
  • Quantitative & Scientific Reasoning
Approved By: Dr. Quintin B. Bullock Date Approved: 3/25/2021


Course and Section Search




Add to Portfolio (opens a new window)