CSE 4256
Transcript Abbreviation:
Programming Python
Course Description:
Python programming for students well-versed in programming with another imperative language.
Course Levels:
Undergraduate (1000-5000 level)
Designation:
Elective
General Education Course
(N/A)
Cross-Listings
(N/A)
Credit Hours (Minimum if “Range”selected):
1.00
Max Credit Hours
(N/A)
Select if Repeatable:
Off
Maximum Repeatable Credits
(N/A)
Total Completions Allowed
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: 2122, 2123, or 2231; and 2321; and enrollment in CSE, CIS, ECE, Engr Physics, or Data Analytics major, or CS minor.
Electronically Enforced:
Yes
Exclusions
(N/A)
Course Goals / Objectives:
Master the use of Python programming language constructs for control flow, literals, expressions, function invocation, and package import
Master the use of Python's built-in types, including lists, tuples, strings, sets, dictionaries, and deques
Master the use of Python features such as slices, list comprehensions and generators
Be competent with functional programming in Python
Be competent with object oriented programming in Python
Be competent with design patterns in Python
Be competent in implementing graph theory data structures and algorithms in Python
Be competent with regular expressions in Python
Be familiar with a Python library/toolkit such as Numpy, NLTK or NetworkX
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Overview of the course and the Python language | 0.0 | 0.0 | 2.0 | 0 |
| Fundamental Python classes and functions | 0.0 | 0.0 | 4.0 | 0 |
| Object-oriented programming in Python | 0.0 | 0.0 | 4.0 | 0 |
| Applications of graph theory | 0.0 | 0.0 | 5.0 | 0 |
| Design Patterns | 0.0 | 0.0 | 3.0 | 0 |
| Text processing and regular expressions | 0.0 | 0.0 | 2.0 | 0 |
| Functional programming | 0.0 | 0.0 | 4.0 | 0 |
| A Python library such as Numpy, NLTK, or NetworkX | 0.0 | 0.0 | 4.0 | 0 |
| Total | 0 | 0 | 28 | 0 |
Grading Plan:
Satisfactory/Unsatisfactory
Course Components:
Lab
Grade Roster Component:
Lab
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework | 25% |
| Projects | 25% |
| In-class activities | 50% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| A Whirlwind Tour of Python | Jake VanderPlas | |
| Functional Programming in Python | David Mertz | |
| Python Cookbook | David Beazley |
ABET-CAC Criterion 3 Outcomes:
| Outcome | Contribution | Description |
|---|---|---|
| 1 | Some contribution (1-2 hours) | Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions. |
| 2 | Substantial contribution (3-6 hours) | Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline. |
| 4 | Some contribution (1-2 hours) | Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles |
| 6 | Some contribution (1-2 hours) | Apply computer science theory and software development fundamentals to produce computing-based solutions. |
ABET-ETAC Criterion 3 Outcomes
(N/A)
ABET-EAC Criterion 3 Outcomes:
| Outcome | Contribution | Description |
|---|---|---|
| 1 | Some contribution (1-2 hours) | an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
| 2 | Substantial contribution (3-6 hours) | an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors |
| 4 | Some contribution (1-2 hours) | an ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts |
| 7 | Some contribution (1-2 hours) | an ability to acquire and apply new knowledge as needed, using appropriate learning strategies |
Embedded Literacies Info:
Advanced Writing
This course does not have an Advanced Writing Embedded Literacy
Data Analysis - Quantitative
This course does not have a Data Analysis – Quantitative Embedded Literacy
Data Analysis - Qualitative
This course does not have a Data Analysis – Qualitative Embedded Literacy
Technology
This course does not have Technology – Qualitative Embedded Literacy
Attachments
(N/A)
Additional Notes or Comments
(N/A)