Introduction to Python for Engineering Modeling – Syllabus and Study Guide

Course Overview

Introduction

This course, "Computer-Based Modeling for Engineers," introduces students to computer-based modeling and programming using Python for engineering applications. The course emphasizes algorithm development, engineering problem solving, and the use of Python for data analysis, decision making, and visualization.

Course Information

Catalog Description

• Focuses on algorithm development and engineering problem solving using Python.

• Covers data types, decision and control flow, functions, debugging, data analysis, and plotting.

• Applies programming to real-world engineering problems.

Prerequisites

• MA 141 (Calculus I)

Credit Hours

• 3 credit hours

Learning Outcomes

What You Will Learn

• Develop computational thinking and problem-solving skills for engineering problems.

• Understand programming basics such as operations, control structures, data types, and functions.

• Identify and debug errors in source code.

• Work with large data files and perform data analysis using PANDAS.

• Create and interpret data visualizations and plots in Python.

• Formulate simple programming models using Python IDEs.

Course Materials

Textbooks and Resources

• Textbook (not required): Introduction to Programming Using Python by David Schneider, Pearson, 2015.

• Many free Python resources are available online and through the NCSU library.

Software Requirements

• Python (latest version recommended)

• Anaconda

• Spyder IDE

• Access to a computer or similar technology is required.

Grading and Assessment

Grading Breakdown

• Homework Assignments: 25%

• Class Participation: 10%

• Projects: 40%

• Final Exam: 25%

Grading Scale

Low	Letter	High
93.00	A	100
90.00	A-	92.99
87.00	B+	89.99
83.00	B	86.99
80.00	B-	82.99
77.00	C+	79.99
73.00	C	76.99
70.00	C-	72.99
67.00	D+	69.99
63.00	D	66.99
60.00	D-	62.99
0.0	F	59.99

Assignment Policies

• Homework is posted weekly and graded on a 10-point scale.

• Late assignments accepted up to one week late at 50% credit if the solution key is not posted.

• Projects are group-based; each group submits their own code.

• Exams are in-class, open laptop, with unlimited printed notes but no electronic notes.

• Attendance is tracked via Kahoot and Moodle quizzes.

Course Schedule (Tentative)

Major Topics by Week

• Introduction to Python and Programming

• Variables, Data Types, and Operators

• Control Structures: Conditionals and Loops

• Functions and Modular Programming

• File Input/Output

• Data Structures: Lists, Tuples, Dictionaries

• Error Handling and Debugging

• Data Analysis with Pandas

• Plotting and Visualization

• Project Work and Review

Additional info: The schedule is subject to change; students are responsible for keeping up with updates.

Academic Integrity and Use of AI

Policy on Generative AI

• AI tools may be used as learning assistants, not as homework assistants.

• Do NOT use AI for tests or to generate code for assignments.

• Cite all AI use as you would other resources.

• Use AI responsibly to enhance your work, not to bypass critical thinking.

Support and Resources

Student Support

• Counseling Center, Suicide & Crisis Lifeline, and Pack Essentials Program are available for support.

• Technology lending is available for students needing devices.

Communication and Conduct

Guidelines

• Use respectful tone in all communications.

• Maintain professionalism and respect cultural differences.

• Do not record others without consent.

Key Python Concepts Introduced in the Course

Programming Basics

• Variables: Named storage for data values. Example: x = 5

• Data Types: Common types include int, float, str, bool, list, tuple, dict.

• Operators: Arithmetic (+, -, *, /), comparison (==, !=, <, >), logical (and, or, not).

Control Structures

• Conditionals: if, elif, else statements for decision making.

• Loops: for and while loops for iteration.

Functions

• Reusable blocks of code defined with def. Example: def add(a, b): return a + b

Data Structures

• List: Ordered, mutable collection. Example: my_list = [1, 2, 3]

• Tuple: Ordered, immutable collection. Example: my_tuple = (1, 2, 3)

• Dictionary: Key-value pairs. Example: my_dict = {'a': 1, 'b': 2}

File Input/Output

• Reading and writing files using open(), read(), write() functions.

Data Analysis and Visualization

• Using Pandas for data manipulation and analysis.

• Creating plots using libraries such as matplotlib.

Debugging and Error Handling

• Identifying and fixing errors using try, except blocks.

• Using debugging tools in IDEs like Spyder.

Example: Simple Python Function

def square(x): return x * x

Example: Reading a File in Python

with open('data.txt', 'r') as file: data = file.read() print(data)

Example: Data Analysis with Pandas

import pandas as pd df = pd.read_csv('data.csv') print(df.head())

Example: Plotting with Matplotlib

import matplotlib.pyplot as plt plt.plot([1, 2, 3], [4, 5, 6]) plt.xlabel('x') plt.ylabel('y') plt.show()

Summary Table: Key Python Concepts

Concept	Description	Example
Variable	Stores data value	x = 10
List	Ordered, mutable collection	my_list = [1, 2, 3]
Function	Reusable code block	def add(a, b): return a + b
Conditional	Decision making	if x > 0: print('Positive')
Loop	Iteration	for i in range(5): print(i)
Pandas	Data analysis library	pd.read_csv('file.csv')
Plotting	Data visualization	plt.plot(x, y)

Conclusion

This course provides a comprehensive introduction to Python programming for engineering modeling, focusing on practical problem-solving, data analysis, and visualization. Mastery of these foundational skills will prepare students for advanced coursework and professional applications in engineering and data science.