MASTER SYLLABUS

2020-2021

A.	Academic Division: Business, Industry, and Technology

B.	Discipline: Information Technology – Software Development

C.	Course Number and Title: ITEC1860 – Introduction to Programming

D.	Course Coordinator: Brian Baldridge

Assistant Dean: Toni Johnson, PhD

Instructor Information:

Name:	Click here to enter text.

Office Location:	Click here to enter text.

Office Hours:	Click here to enter text.

Phone Number:	Click here to enter text.

E-Mail Address	Click here to enter text.

E.	Credit Hours: 3

Lecture: 2 hours

Laboratory: 2 hours

F.	Prerequisites: None

G.	Syllabus Effective Date: Fall 2020

H.	Textbook(s) Title:

Python Programming in Context

Authors: Bradley N. Miller, David L. Ranum

Publisher: Jones & Bartlett Learning

Copyright Year: 2013

Edition: 2nd Edition

ISBN#: 1449699391

Workbook(s) and/or Lab Manual: An external USB3.0 hard drive, 500 gigabytes or larger is required.

Python Pocket Reference

Author: Mark Lutz

Copyright Year: 2014

Edition: 5th Edition

ISBN#: 9781449357016

J.	Course Description: Python is a user-friendly, object-oriented programming language. This course provides a clear, accessible, and skill-focused approach to programming with Python using Python 3. The course offers students a thorough overview of multiple applied areas, including image processing, cryptography, astronomy, the Internet, and bioinformatics. Problem sets are based on real-world examples and problem-solving rather than language features. This course offers students a solid platform of key problem-solving skills that translate easily across programming languages.

K.	College-Wide Learning Outcomes:

College-Wide Learning Outcome	Assessments - - How it is met & When it is met
Communication – Written
Communication – Speech
Intercultural Knowledge and Competence
Critical Thinking
Information Literacy
Quantitative Literacy

L.	Course Outcomes and Assessment Methods:

Upon successful completion of this course, the student shall:

Outcomes	Assessments – How it is met & When it is met
Describe the use of lists as a means of storing data	Weeks 2-4 labs, midterm/final exam
Demonstrate nested iteration	Weeks 5-6 labs, midterm/final exam
Demonstrate image processing algorithms	Week 7 labs, midterm/final exam
Explain advanced examples of using lists in Python	Week 8 labs, midterm/final exam
Create recursive functions	Week 9 labs, midterm/final exam
Implement graphical simulations using objects	Weeks 10 & 11 labs, final exam
Design a large multiclass application	Weeks 12-14 labs, final exam
Create a working object-oriented graphics package	Weeks 12-14 labs, final exam

M.	Topical Timeline (Subject to Change):

Week 1: Python Overview

Week 2: πthon

Week 3: Codes and Other Secrets

Week 4: A nest of Snakes: Introducing the Python Collections

Week 5: Earthquakes, Floods, and Other Natural Disasters

Week 6: Pycture Perfect Programs

Week 7: Data Mining: Cluster Analysis

Week 8: Cryptanalysis

Week 9: Fractals: The Geometry of Nature

Week 10: Astronomy

Week 11: Bears, Fish, and Plants, Oh My!

Week 12: Your Father Was a Rectangle

Week 13: Video Games

Week 14: Labs

Week 15: Review, Term Project (optional)

Week 16: Lab Makeup/Final Exam

N.	Course Assignments:

Labs

Tests

Midterm Exam

Final Exam

Final Project (optional)

O.	Recommended Grading Scale:

NUMERIC	GRADE	POINTS	DEFINITION
93–100	A	4.00	Superior
90–92	A-	3.67	Superior
87–89	B+	3.33	Above Average
83–86	B	3.00	Above Average
80–82	B-	2.67	Above Average
77–79	C+	2.33	Average
73–76	C	2.00	Average
70-72	C-	1.67	Below Average
67–69	D+	1.33	Below Average
63-66	D	1.00	Below Average
60-62	D-	0.67	Poor
00-59	F	0.00	Failure

P.	Grading and Testing Guidelines:

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Q.	Examination Policy:

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R.	Class Attendance and Homework Make-Up Policy:

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S.	Classroom Expectations:

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T.	College Procedures/Policies:

Important information regarding College Procedures and Policies can be found on the syllabus supplement located at

http://catalog.ncstatecollege.edu/mime/download.pdf?catoid=5&ftype=2&foid=3