COMP 521 Syllabus

Course Information¶

Course Description¶

Introduction to computational methods and data science using Python. Numerical algorithms, optimization, Monte Carlo methods, data wrangling with pandas, exploratory data analysis, regression and classification. Emphasizes software engineering practices including object-oriented programming, modular design, version control, testing, and documentation.

Units: 3 Prerequisites: MATH 252; MATH 254 or 342A; or equivalent with instructor permission, or graduate standing.

Course Materials & Technology¶

There is no textbook to buy. All required readings are free on the course website. Readings will be added as the semester progresses and posted at least one week before they are due.

You need a laptop you can bring to every class. Class sessions interleave instruction with hands-on work, so a laptop is not optional equipment here. Any machine running macOS, Windows, or Linux that can install Python will do. This course is not computationally demanding, and nothing we do requires a GPU or a large amount of memory.

Software (Python, git, and the scientific stack) is free, open-source, and installed in Week 1. See Getting Started.

Class Format¶

This course does not separate lecture days from lab days. Each 75-minute session combines short instruction with hands-on work, and the balance shifts depending on where we are in a project: heavier on instruction when a new method is introduced and heavier on working time as a deadline approaches.

Bring your laptop. Much of the instruction happens through hands-on work and discussion that responds to the class’s current questions.

Student Learning Outcomes¶

By the end of this course, students will be able to:

1. Build modular, testable, and reproducible scientific software in Python that implements core numerical algorithms from first principles, using functions, modules, version control, and classes when state and invariants require them.

2. Analyze and validate numerical methods by quantifying error, convergence, stability, and failure modes; using analytic limits, reference solutions, and library comparisons to establish reliability.

3. Acquire, clean, analyze, and visualize real scientific data with defensible treatment of quality flags, missing values, units, assumptions, and limitations.

4. Apply statistical inference, model fitting, uncertainty quantification, and introductory machine-learning methods to evaluate scientific models and hypotheses.

5. Formulate, scope, and carry out a computational investigation by selecting appropriate models, data, methods, validation evidence, and tradeoffs among correctness, performance, and clarity.

6. Communicate reproducible computational work professionally through code, tests, documentation, figures, notebooks, and presentations; clearly stating methods, evidence, limitations, and meaningful collaboration or AI use.

Where these outcomes are assessed: Projects 1–5, the two Code Review Presentations, the Final Project (including the proposal pitch), and the Growth Reflections.

Grading & Assessments¶

The three Growth Reflections are spaced across the semester: a baseline Growth Memo in Week 1, a mid-semester Growth Memo, and the Growth Synthesis at the start of finals week. See Growth Reflections.

Important Dates¶

Projects are typically assigned on Tuesdays and due on Wednesdays at 11:59 pm PT. See the Course Schedule for each assignment and submission date, including exceptions, and Projects for deliverables and submission expectations.

See Code Review Presentations for the individual presentation and submission requirements.

End-of-semester sequence:

University dates that affect you:

Week-by-week topics and readings are on the Course Schedule. Topics are listed by week rather than by day, so that class time can follow where the class actually is.

Grading Scale¶

Work in this course is graded with letters, not points. Each assignment receives a single letter for the work as a whole. There are no itemized rubrics and no point breakdowns. See Grading for what each letter means for each kind of work.

Your grade in each component is the average of the letters earned in it; your course grade is the weighted average of those, using the weights above. Letters average as you would expect: an A+ and an A− come out to an A. Grades are recorded as these numbers in Canvas.

SDSU does not award A+ as a final grade, so the highest course grade is an A. An A+ on an assignment still counts as 4.3, which lifts your average.

See Grading for the table converting your weighted average into a final letter.

Click here for SDSU’s explanation of grades.

Late Work & Extensions¶

• One free extension per semester, for Projects 1–5 only. Request it at least 24 hours before the deadline and you get a 2-day grace period with no penalty and no reason required.

• No late submissions beyond that. If you miss a deadline without an advance arrangement, the submission is not accepted unless there is a documented emergency. If you cannot give a code review presentation on its scheduled date, contact me promptly through Canvas Inbox to arrange an equivalent individual presentation and a deadline for its materials. The project extension does not apply to presentations.

• For projects, your submission is the latest commit pushed to GitHub by the deadline. Work that remains only on your computer has not been submitted. Push early and often. Code review materials are submitted through Canvas by the start of class on the presentation date.

Regrade Requests¶

• Submit a request within 7 calendar days of the grade release.

• Provide a $\leq$200-word written justification that argues against the grade description in Grading and the assignment expectations. Make the case that the work meets a higher description than the one I applied.

• I will re-evaluate the entire submission; the grade may go up, down, or remain unchanged.

Course Components¶

Per SDSU policy, students are expected to spend at least 6 hours per week (a minimum of 2 hours per course credit hour/unit) on coursework outside of class, including reading, project work, and preparation for class. Click here for SDSU’s explanation of Credit Hour or Unit.

Projects¶

COMP 521 includes a required repository check at the start of Project 1, five two-week projects, two individual code review presentations, and a four-week solo final project. See Projects for the checkpoint, project sequence, submissions, and collaboration expectations.

Code Review Presentations (10% total)¶

Choose one open source computational science/scientific computing package and, later in the semester, a different data science or machine-learning package. Present a brief map of each package’s architecture, then examine one code path, teach a small example, and explain what a focused check supports. Each individual presentation counts for 5%. See the assessment brief for the dates, submission route, grading, and assignment-specific AI rules.

Scholarly Engagement (10%)¶

Scholarly engagement means prepared participation, productive collaboration, and visible reasoning: questions, tests, debugging, and the ability to explain your code. You can demonstrate it through discussion, written reasoning, pair work, and project check-ins; frequent speaking is not required. You receive one engagement grade for the term as a whole.

Classroom norms. We are all members of a professional academic community. Treat classmates and the instructor with respect. Spirited, courteous debate about ideas is welcome; disrespectful behavior is not. Because active reasoning is the point of being in the room, off-task device use during class is distracting and will negatively affect your engagement grade.

Attendance itself is not graded. However, excessive absences will affect Scholarly Engagement because they limit prepared participation, collaboration, substantive check-ins, and other visible engagement with the course.

Final Project (30% total)¶

The final project counts for 30% of your grade: 5% for the proposal pitch and 25% for the finished project.

The presentation, report, and repository receive one holistic final-project grade.

For the proposal pitch, give a five-minute presentation using a short slide deck. State your scientific question, data or source, computational approach, validation plan, bounded minimum result, and immediate next step. Commit the slides to your repository before class. The pitch is graded for clarity, feasibility, and a credible plan to test the result.

The final project is a four-week, solo computational investigation. Choose a bounded scientific question and use the course toolkit to produce and defend a result. You may extend a course project, analyze a curated scientific data set, or propose a small project from your own field. Custom projects require approval at the proposal pitch.

Your project must:

• state a focused scientific question;

• make its data, model, methods, and assumptions explicit;

• include reproducible code and evidence that the result is trustworthy; and

• communicate the result, its limitations, and what you would investigate next.

The final-project brief will define the available project pathways and submission format. This project is solo. See Project Pairs & Collaboration.

The final presentation is eight minutes, followed by two minutes for questions. Explain what you built, the evidence that it works, and its limits. The report is 5–7 pages and covers the background, methods, results, and conclusions.

The final project is graded on the same description as the other projects (see Grading), applied to a larger piece of work. You should expect to defend your design and verification choices.

Growth Reflections (10% total)¶

Growth Reflections build metacognition, or thinking about how you learn so you can improve your workflow, debugging habits, and verification discipline over time. Write them in an informal, memo-like voice (not an essay). Honesty and specific evidence matter more than polished prose.

There are three:

Growth Memos 1 and 2 should each be 1–2 pages, with a minimum of one full page. The Growth Synthesis should be 2–3 pages, with a minimum of two full pages. All three are graded on specificity, reflection, and evidence rather than polished prose; see Growth Reflections for the prompts.

Submit each reflection through the corresponding Canvas assignment as a single-spaced PDF using a readable 12-point font and 1-inch margins.

Generative AI: Student and Instructor Use¶

Student use¶

All submitted work must reflect your own understanding and reasoning. AI is optional; you are responsible for the code and written answers you submit and must be able to explain both. Core solution code remains yours in every phase. Disclose meaningful use. Read the full AI Use & Growth Mindset Policy before Project 1; its staged permissions and documentation requirements are enforced. The Code Review Presentations have additional rules for package discovery and student-authored explanations.

How I use generative AI¶

I use generative AI as a drafting, technical, and pedagogical assistant when developing readings, projects, examples, code, and website text. I direct and review the process, verify claims and code, and personally vet all course materials.

Use in grading: I use my SDSU ChatGPT Edu account to assist with holistic review of submitted code. I independently verify its output and determine every final grade. It does not make academic-integrity decisions. You may challenge a grade through Regrade Requests.

Course Materials (Sharing Policy)¶

Lecture notes, slides, projects, and course handouts are the intellectual property of the instructor. You may use course materials for your own educational purposes in this course, but you may not reproduce, distribute, share, or post them on any public platform (e.g., Chegg, Course Hero, Discord servers, public GitHub repos) without written permission.

Unauthorized recording or redistribution of class sessions or office hours is prohibited.

Communication & Getting Help¶

Use Canvas Inbox for all routine course-related messages, including questions about assignments, deadlines, grades, extensions, missed work, and appointments. Use the Canvas discussion board when your question may help others. I aim to respond to Canvas messages within 24–48 hours on weekdays.

Do not send routine course questions to my SDSU email. If you do, I will reply only to direct you to Canvas; I will not provide a separate substantive response by email. Email is reserved for a genuine emergency or a confidential matter that cannot appropriately be handled through Canvas.

If you encounter difficulties with the assignments or find yourself falling behind, seek help immediately by:

• posting your questions on the course Canvas discussion page,

• speaking with me during class or office hours (preferred),

• collaborating with classmates (while ensuring your submission reflects your own understanding).

Office hours are for learning. Bring what you have tried (notes, attempts, questions) and we will work from there. I will often guide you by asking questions so you build your own problem-solving skills. You are welcome to come even if you are not sure what to ask, or have non-course-related questions.

Study Tools (Build Your Own)¶

I will show you how to build a private, grounded study notebook from course materials. It remains subject to the AI Use & Growth Mindset Policy: keep it private and do not use it to generate solution code.

Fostering a Growth Mindset¶

A growth mindset is the belief that intelligence, abilities, and talents are malleable and can be developed through effort and persistence, not fixed traits you are born with. This mindset is key to succeeding in this course. In COMP 521 you will encounter challenging concepts that may initially seem overwhelming. This is normal and expected: it means you are learning.

Diversity and Inclusivity Statement¶

I consider this classroom to be a place where you will be treated with respect, and I welcome individuals of all ages, backgrounds, beliefs, ethnicities, genders, gender identities, gender expressions, national origins, religious affiliations, sexual orientations, ability, and other visible and non-visible differences. All members of this class are expected to contribute to a respectful, welcoming and inclusive environment for every other member of the class.

Essential Student Information¶

For essential information about student academic success, please see the SDSU Student Academic Success Handbook.

SDSU provides disability-related accommodations through the Student Ability Success Center (SASC). Contact sascinfo@sdsu.edu as early as possible. Accommodations are not retroactive and begin after I receive an authorized accommodation letter. Approval may take 10–14 business days.

Class rosters are provided to the instructor with the student’s legal name. Please let me know if you prefer an alternate name and/or gender pronoun.

If you need to be absent from class for a religious observance, please notify me in writing during the first two weeks of the semester so that we can make any necessary arrangements.

Land Acknowledgement¶

San Diego State University sits on Kumeyaay land. The Kumeyaay people have lived in this region for over 10,000 years and continue to live here today.