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2020-2021 Catalog 
    
2020-2021 Catalog [ARCHIVED CATALOG]

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DAT 129 - Python 2


Credits: 3
3 Skills Lab Hours

Prerequisites: DAT 119  

 
Description
Building on language foundations developed in Python 1, this second semester Python course focuses on the language’s powerful file processing and data manipulation tools.  Students will explore core libraries that allow programs to access operating system services, manipulate data of many types, interact with the user through graphical user interfaces (GUIs) and crunch out data metrics.  This fast-paced course is project-focused and builds not only Python programming skills but also best practices in object-oriented software design. 


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

  1. Load a python library suitable for processing files of a given type.
  2. Integrate an operating system process into a given program, making use of core python OS-related objects.
  3. Create instances of the core Python graphical user interface (GUI) components: buttons, text boxes, select boxes and images.
  4. Convey meaningful information extracted from a simple data set.
  5. Implement a user-centered design process for a Python program.
  6. Model the core phases of smart design with a simple, non-technical design problem.
  7. Convert a given algorithm written in English to Python.
  8. Design a new algorithm to solve a technical problem.
  9. Simulate a given human or system interaction in Python.
  10. Curate an online portfolio of working documented Python code from at least two course projects using a version control system, like GIT. 
  11. Effectively discuss Python skills and their applications to a potential employer during a practice interview.
Listed Topics
  1. File types and python object adapters
  2. Looping structures
  3. File-based data stores
  4. Operating system interaction
  5. User-interface GUI components
  6. Data display GUI components
  7. GUI Design through user interview
  8. User-centered design
  9. Top-down design approach
  10. Psuedocode versions of algorithms
  11. Algorithm implementation in python
  12. Searching, sorting and traversal algorithms
  13. Monte Carlo simulations
  14. Simulation design phases
  15. Model and unit testing
  16. Technical interview preparation
Reference Materials
Current textbook and open-source resources.
Approved By: Bullock, Quintin Date Approved: 04/25/2018


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