Data Analysis for Game Development

IMGD 2905

This course will cover basic concepts of probability and data analysis as they apply to the design and analysis of interactive media and games. Students will study appropriate use of probability distributions in the design of interactive experiences, and the use of data analysis methods to understand user behavior in games and other interactive experiences. Topics will include discrete and continuous probability distributions, programming techniques to produce samples from different distributions, descriptive statistics, exploratory data analysis and using existing tools to collect and analyze data from gameplay. This course counts toward the Quantitative Science component of the university-wide Mathematics and Science Requirement for IMGD majors only.

Recommended background: High school algebra



Latest Agenda

Groupwork Sessions


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Course Information

Time and Place

Days: Mo, Tu, Th, Fr
Time: 10:00pm-10:50am
Place: IS 105

Discord Server: IMGD 2905 (invite)


Professor: Mark Claypool
Office hours: Mo 12-1pm Tu 12-1pm Th 4-5pm Fr 12-1pm
Place: FL B24 (or Zoom, or Discord, by appt.)

Student Assistant: Jenna Tripoli
Email: jmtripoli at
Office hours: Tu 1:30-3pm Fr 1:30-3pm
Place: Discord

Student Assistant: Ryan Darcey
Email: rtdarcey at
Office hours: Tu 3-4pm We 11am-12pm
Place: Discord


David M. Levine and David F. Stephan. “Even You Can Learn Statistics and Analytics”, Third Edition, Pearson, 2015. ISBN: 978-0133382662.

The book is unfortunately named since it suggests a degree of ineptitude in the student, but the content is well-presented with nice examples. The technical depth is sufficient to provide a decent foundation for game analytics.


List of topics covered in this course (not exactly in order of appearance):

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Grading Policy


Final grades will be computed as follows:

Projects: Each student will complete four projects that require engaging in data analysis for game development. This includes hands-on work with appropriate data collection, statistics and visualization tools for analysis. Work on each project will be demonstrated through a written report.

Although content is a significant part of any project writeup, content means little if the reader cannot easily extract the information and main messages. Thus, written reports are to be graded on presentation clarity as well as content. Writing should suitably concise, follow directions, and provide the needed information precisely. While it is not the intent to play “English teacher” while grading, errors in grammar, organization, and/or style may affect the grade.

Homework: There will be three written homework assignments, spaced roughly 1/3 apart each, and a final “homework” in class as a quiz during the final week. The homework will have you work statistics and probability problems and exercises from the book and outside the book, sometimes using tools (e.g., Excel) in coming up with the answer.

Participation: Showing up to class is worth a large part of the class participation grade, but so is being engaged in the class material through asking and answering questions.

Playtesting: 2% of your final grade is based on participation in playtesting (and user studies). You volunteer for playtesting sessions arranged by developers (typically IMGD majors) outside of class. For each session you participate in, provide your name to the developers and they give you a certification document (e.g., an email, photo or document). Upload the certification to the Canvas assignment. Note, playtesting sessions are not graded - you get credit just for participating. All playtesting must be done by the end of the term.

Final Grades: The final grades earned will reflect the extent to which a student has demonstrated understanding of the material and completed the assigned projects. The base level grade will be a “B” which indicates that the basic objectives on projects and exams have been met. A grade of an “A” will indicate significant achievement beyond the basic objectives. A grade of a “C” will indicate not all basic objectives have been met, but work was satisfactory for credit. No incomplete grades will be assigned unless there are exceptional, extenuating circumstances. Similarly, no makeup projects or exams will be given unless there are exceptional, extenuating circumstances.

A letter “S” grade will be awarded to students with superlative performance, a recognition above that of an “A” grade. Only the top 10% of the class will receive such a grade. While the “S” grade will not show up on an official WPI transcript (it will show up as an “A”), students that receive an “S” are more than welcome to claim the grade on a resume, blog or any other forum they wish - I’ll stand behind it.

Late Policy

Assignments (Homework and Projects) are due online (via Canvas) at 11:59pm on the due date, unless otherwise noted. Late assignments will be accepted with a 10% penalty of the total earned value for each day (24 hours) late, with the weekend counting as one day. Assignments will not be accepted more than one week beyond the due date.

Class Conduct

All work is expected to be done individually (unless otherwise noted). As such, students are encouraged to discuss their work with each other, but are also expected to do the work by themselves. A guideline is that code, data, charts and analysis can all be viewed by students at the same place and time (e.g., by looking over each other’s shoulder and explaining and discussing or sharing a screen by Zoom). But the line is drawn at digitally copying over such materials - e.g., there is to be no emailing, cutting-and-pasting or transferring code, data, graphs or tables from others. Any breach of professional ethics as evidenced, for example, by copying material (e.g., data and/or charts) for the projects or homework, from other students or the Internet, or using outside help of any kind, is considered adequate reason for an NR in the course and a report to the Dean of Students. Refer to the official WPI statements on Academic Integrity for details. Remember this warning - any breach of ethics will earn you an NR and an official report. When in doubt, ask!

Bullying or harrassing classmates or IMGD 2905 staff in person or online will not be tolerated. Nor will homophobic, racist, sexist, or otherwise offensive comments. If you have questions about the above policy, or wish to report/discuss what you have seen/read online, please contact the professor.

Research has shown that students who are “active” and take notes during class retain more information, even if they do not use those notes later. So, take notes! Do this on paper or with the computer.

Also, those who multi-task (e.g., checking texts or reading non-related material during class) often learn less. In fact, those who believe that they are expert multi-taskers often do the worst! Productivity can be reduced by as much as 40 percent when switching tasks.

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Personal Health

Students may experience stressors that can impact both their academic experience and their personal well-being. These may include academic pressure and challenges associated with relationships, mental health, alcohol or other drugs, identities, finances, and more. If you are experiencing concerns, seeking help is a courageous thing to do for yourself and those who care about you. If the source of your stressors is academic, especially if that source is related to IMGD 2905, please contact me so that we can find solutions together. I care and I’d like to help. Please, just ask.

If you have a friend or classmate that you think might need help, please reach out to them and talk, perhaps encourage them to seek help. Even just simply reaching out can be a boon to someone in need.

Help - it’s OK to need it.  It’s important to ask for it.  Here’s some other ways to find it: Be Well Together

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Slides from class lectures and other in-class materials are available shortly before or after they are presented. Both powerpoint and pdf are provided, along with an indication of the relevant chapter in the course textbook.

Administrative pdf pptx
Introduction pdf pptx
Fundamentals of Statistics pdf pptx (Chapter 1)
Presenting Data pdf pptx (Chapter 2)
Descriptive Statistics pdf pptx (Chapter 3)
Probability pdf pptx (Chapters 4 and 5)
Inferential Statistics pdf pptx (Chapters 6 and 7)
Simple Linear Regression pdf pptx (Chapter 10)
Review pptx

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Homework will be turned in online (canvas) in written form, saved as a PDF.

Homework and due-dates will be placed here as they are defined. Here is what we have so far:


Projects and due-dates will be placed here as they are defined. Here is what we have so far:

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This section has any samples discussed in class, exam preparation material, tutorials or any other demonstration-type class materials.

For finding some rich data sets (including some on games) ready for analysis, you might check out:

A visual introduction to probability and statistics:

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