Data Science for Human-Centered Systems (IAT 461 / IAT 882, 4 units) – Summer 2026

Welcome! This course follows the spirit of SFU SIAT's IAT 461 data science pipeline — carrying analyses from questions and data through cleaning, modeling, and interpretation for human-centered and interactive systems. Teaching happens online, over zoom. Videos will be recorded and posted on Yotube, with downloadable slides on this site and course operations

This page summarizes structure, outcomes, policies, grading, and the weekly schedule. When in doubt, the official SFU outline and Canvas announcements win if they ever differ from a draft here.

Format (Summer 2026)

  • Lectures — Live zoom sessions and asynchronous video on YouTube; Slides (and sometimes links/references) are published on this website. CHECK THIS WEBSITE REGULARLY.
  • Labs — online support / exercises aligned with each week.
  • Expectations — plan roughly 6–8 hours/week beyond lecture viewing for readings, tutorials, and assignments (aligns with prior offerings of this course).

Learning outcomes

On successful completion, you should be able to:

  • Carry out the data analytics process for human-centered systems end to end, using appropriate terminology.
  • Understand types of data and common pitfalls in analyzing each.
  • Identify which techniques fit each stage of the pipeline and when they apply.
  • Execute cleaning, feature engineering, method selection, and interpretation of results.
  • Apply core models — including linear and logistic regression, k-means and hierarchical clustering, and methods such as decision trees, random forests, NLP, Topic Modeling, and Agentic Systems.
  • Reason about integrating data into system design from needs analysis.
  • Use the Python ecosystem (e.g. pandas, scikit-learn, stats-focused libraries) to implement analyses in notebooks.

Textbook & readings

  • Required text: Steven S. Skiena, The Data Science Design Manual (2017). Access via SFU Library (Skiena e-book permalink from prior syllabus)
  • Readings are reuqired.
  • Environment: we standardize on VS Code + Python; you may use Google Colab if you prefer, but support may be limited — export .ipynb and PDF as required for hand-ins

Lecture videos & slides

Each week's YouTube link will appear in the syllabus table in the Video column as recordings go public. The Slides are hosted on this site; links appear in the same table when available.

Teaching Team

Instructor

Dr. Alireza Karduni

Email: akarduni@sfu.ca

TA

Mehdi Jahani

Email: mehdi_jahani@sfu.ca

Office hours:

Course Policies

Contacting us

We use Discord for community Q&A; use SFU email for anything official (concessions, grading disputes with the instructor, etc.). Please allow up to about two business days for email replies — we'll often be faster.

To speed up replies, include: your full name; a subject line starting with "IAT461:"; and a clear question. For logistics specific to labs/tutorials, start with TA Mehdi; escalate to the instructor if needed.

Conduct

Please treat our online interactions the same way you would in-person interactions. As a teaching team we are dedicated to providing a harassment-free experience for everyone in this class, regardless of gender, sexual orientation, disability, physical appearance, body size, race, or religion. Harassment of any form is not tolerated. Sexual language and imagery is not appropriate in this class.

If you have concerns with anyone's conduct either in-person or online, Email your instructor. If you do not feel comfortable reaching out to your instructor, please contact SIAT's advisors

SFU's complete student conduct policy is available online.

Illness

If you are feeling ill, you should stay home and get better. Let your instructor or TA know that this is the case, and make sure to catch up with course materials to stay up-to-date.

Grading and evaluation

Assignments, quizzes, and exams

This offering is online: you need a reliable computer, Python, and a notebook environment. Lecture ideas are introduced in the YouTube videos and readings; labs and assignments are where you practice the pipeline end to end.

You will submit work thgough GITHUB. Instructions, rubrics, and due dates for each item are posted there — this site holds the week-by-week plan and slide decks.

There will be at least one midterm-style quiz

The final project applies the full data-science workflow — implementation, communication, and often a short presentation or recording

Your grades are based on your participation in all of these activities:

  • Assignment: 38%
  • Midterm Quiz: 15%
  • Final Project Submission: 30%
  • Final Presentation: 12%
  • In-class Quiz and Lab Attendance: 5%
  • Lecture engagement: 5% (extra)

Note: For due dates and details, refer to the syllabus table below; we will keep Canvas aligned with this schedule.

Late assignments

Late penalties (10% a day for 2 days, 20% after). If you have issues and can't submit on time, please let us know in advance, we are happy to work figure out a way to get you up to speed.

Use of AI-assisted tools

Tools like ChatGPT, Copilot, and similar can help with syntax, debugging, or small code patterns — and learning to use them well is part of modern practice. For this course, disclosure and academic honesty matter.

In general: do not paste whole assignment solutions or full interpretation paragraphs from a model without doing your own analytical work. When AI informs a block of code, mark it clearly in the notebook (tool, link, prompt, and the adapted code) so we can see your judgment — a pattern like the#BEGIN/#END blocks used in prior offerings works well

For written reports and presentations, your analysis should be your own words and reasoning; do not submit synthetic prose as if it were unchanged human insight.

Acknowledgements

The weekly structure, outcomes, and many pedagogical ideas trace to SIAT colleagues who developed IAT 461 before this offering — including Marek Hatala and Dilky Felsinger

Course Syllabus

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