Course Syllabus

Below is a syllabus template that includes WSU's required syllabus elements. Please complete all items highlighted in yellow.

Title of Course Computational Calculus I

Prefix and Number DATA 121

Semester and Year Fall 2025

Number of Credit Hours 3

Prerequisites: MATH 106 and MATH 108 with a C or better OR a minimum ALEKS math placement score of 83%; CPTS 111 with a B+ or better or by permission with an AP Exam in CPTS Principles or Cpt Sci A with a 4 or better.

Course Details

Day and Time: [tbd]

Meeting Location: [tbd]

Instructor Contact Information

Instructor Name: [tbd]

Instructor Contact Information: [office location, phone, email] [tbd]

Instructor Office Hours: [click here for best practices] [tbd]

TA Name: [tbd]

TA Contact Information: [office location, phone, email]: [tbd]

TA Office Hours: [click here for best practices] [tbd]

Course Description

This course introduces the fundamental concepts of calculus, including limits, derivatives, integrals, and their applications, with a strong emphasis on computational methods using Python. The course is tailored for students majoring in Data Analytics, focusing on the practical implementation of algorithms to solve complex problems. Hands-on projects utilizing Python libraries will enhance understanding and application of calculus in real-world data analytics scenarios.

Course Materials

Books: N/A

Reference Materials: ‘Calculus: A Modeling and Computational Thinking Approach’ by E. Stade and E. Stade, Springer, 1^st Edition, 2023. E-ISBN: 978-3-031-24681-4. URL: https://link.springer.com/book/10.1007/978-3-031-24681-4

‘Computational Calculus: A Numerical Companion to Elementary Calculus’ by W. C. Bauldry, Springer, 1^st Edition, 2023. E-ISBN: 978-3-031-29658-1. URL: https://link.springer.com/book/10.1007/978-3-031-29658-1

Fees: N/A

**Student Learning Outcomes (SLOs) [add more lines if necessary]**
Course Learning Outcomes (students will be able to:)	Activities Supporting the Learning Outcomes	Assessment of the Learning Outcomes
Understand and apply the concepts of limits, derivatives, and integrals.	[insert]	All four project assignments listed in the Grading section assess a mixture of all the 5 SLOs.
Implement numerical methods for differentiation and integration using Python.	[insert]	All four project assignments listed in the Grading section assess a mixture of all the 5 SLOs.
Utilize Python libraries to solve calculus problems, with a focus on data analytics applications	[insert]	All four project assignments listed in the Grading section assess a mixture of all the 5 SLOs.
Analyze and interpret the results of computational methods in the context of data modeling.		All four project assignments listed in the Grading section assess a mixture of all the 5 SLOs.
Apply calculus concepts to real-world scenarios through project-based learning.		All four project assignments listed in the Grading section assess a mixture of all the 5 SLOs.

**Course Schedule**

**[Please note that a WSU semester is 15 weeks + Thanksgiving/Spring Break. The schedule below does not include the break.]**
Dates	Lesson Topic	Assessment
Week 1 [dates]	Introduction to Calculus and Python Tools	Overview of Calculus: Limits, Derivatives, and Integrals Introduction to Python for Calculus: Introduction to Algorithms and software development process Variables, Data Types, and Operators Overview of relevant Python packages (NumPy, SciPy, Matplotlib, SymPy)
Week 2 [dates]	Taylor’s Theorem and Numerical Differentiation	Taylor’s Theorem and its importance in numerical methods Numeric expressions, functions, testing, and debugging Numerical Differentiation Techniques: Forward, Backward, and Centered Difference Approximations using NumPy and SymPy
Week 3 [dates]	Numerical Differentiation Continued	Taylor Expansions and Error Analysis File processing, Selection Structures Implementing Numerical Differentiation Algorithms in Python using NumPy and SciPy
Week 4 [dates]	Numerical Differentiation Continued	Taylor Expansions and Error Analysis File processing, Selection Structures Implementing Numerical Differentiation Algorithms in Python using NumPy and SciPy
Week 5 [dates]	Numerical Integration	Basic Integration Techniques: Rectangle Methods, Trapezoidal Rule, Simpson’s Rule using SciPy Loops and loop patterns Gaussian Quadrature and Gauss-Kronrod Quadrature with SciPy Comparing Numerical Integration Methods using Python
Week 6 [dates]	Sequences and Series	Introduction to Sequences and Series Power Series, Taylor Series, and their applications in Data Analytics Approximating functions using Taylor Series in Python
Week 7 [dates]	Sequences and Series	Introduction to Sequences and Series Power Series, Taylor Series, and their applications in Data Analytics Approximating functions using Taylor Series in Python

Expectations for Student Effort

[Describe how much time students should expect to invest in the course each week. Graduate courses should state: "For each hour of lecture equivalent, students should expect to have a minimum of two hours of work outside of class." Note that Global campus courses will automatically include credit hour equivalents in the syllabus.]

Students should expect to spend a minimum of 3 hours per week for each online 1-credit course, engaged in the following types of activities: reading, listening to/viewing media, discussion, or conversation in the LMS or other academic technology, conducting research, completing assignments, and reviewing instructor feedback, studying for and completing assessments, etc.

Grading [add more lines if necessary]

Assignment Breakdown
Type of Assignment (tests, papers, etc)	Points	Percent of Overall Grade
Project 1: Variable Data	[insert]	10%
Project 2: Differentiation Algorithms	[insert]	30%
Project 3: Integration Methods	[insert]	30%
Project 4: Taylor Series		30%

Performance metrics for assignments vary from one assignment to another. These metrics are listed in the assignment descriptions available on the LMS.

Course assignments and their due dates will be made available on the LMS.

Grading Schema
Grade	Percent	Grade	Percent
A	94 — 100	C	74 — 76.9
A-	90 — 93.9	C-	70 — 73.9
B+	87 — 89.9	D+	67 — 69.9
B	84 — 86.9	D	61 — 66.9
B-	80 — 83.9	F	0 — 60.9
C+	77 — 79.9

Letter grades will be assigned based on the scale shown below. The assignment and exam scores will be adjusted (curved) according to the class averages. The below scale assumes class average is 80%.

Attendance and Make-Up Policy

[Provide details on how attendance affects final course grades. Indicate whether and how missed exams, laboratory sessions, etc. can be made up. Sample attendance statement: “Students should make all reasonable efforts to attend all class meetings. However, in the event a student is unable to attend a class, it is the responsibility of the student to inform the instructor as soon as possible, explain the reason for the absence (and provide documentation, if appropriate), and make up class work missed within a reasonable amount of time, if allowed. Missing class meetings may result in reducing the overall grade in the class.” ] The course will be delivered in a hybrid fashion. As such, attendance is not required. The instructor expects the students to be mature enough to be able to manage the course items as per the course schedule listed in the LMS.

Academic Integrity Statement

You are responsible for reading WSU's Academic Integrity Policy, which is based on Washington State law. If you cheat in your work in this class you will:

-[insert your sanction here]

-Be reported to the Center for Community Standards

-Have the right to appeal my decision

-Not be able to drop the course of withdraw from the course until the appeals process is finished

If you have any questions about what you can and cannot do in this course, ask me.

If you want to ask for a change in my decision about academic integrity, use the form at the Center for Community Standards website. You must submit this request within 21 calendar days of the decision.