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

- Gain proficiency in modern tools for data acquisition and analysis.
- Understand basic probability and statistics as it applies to data analysis.
- Develop skills for presenting game data analysis both orally and in written form.

- Use a spreadsheet (e.g., Microsoft Excel) to analyze and visualize game data.
- Use a scripting language (e.g., Python) to extract and clean data recorded from a game.
- Apply summary statistics to game data.
- Compute probability distributions for game data.
- Write reports with graphs/charts and tables illustrating detailed analysis of game data.

info | grading | slides | homework | projects | samples | timeline

Professor: Mark Claypool

email: claypool@cs.wpi.edu

office hours: `Mo 4-5pm Tu 4-5pm We 4-5pm Th 4-5pm`

place: `https://wpi.zoom.us/j/542948614`

Days: `Mo`

, `Tu`

, `Th`

, `Fr`

Time: `10:00pm-10:50am`

Place: `https://wpi.zoom.us/j/460051336`

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 necessarily in order of appearance):

- Data analysis tools and pipeline
- Visualizing and presenting data
- Statistics and statistical inference
- Probability
- Hypothesis testing
- Regression

info | grading | slides | homework | projects | samples | timeline

Final grades will be computed as follows:

- Projects:
*60%* - Homework:
*30%* - Participation:
*10%*

**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. 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.

**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.

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.

This course is intended for serious students. Participants will be expected to adhere to all rules of professional behavior. It is to be emphasized that knowledge of material and professional behavior are tied together; failure in one of them negates any excellence in the other.

All work is expected to be done individually. 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 s 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*!

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. The “multi-tasking” state of mind has been labeled continuous partial attention and is a problem when trying to focus. During class, try to limit multi-tasking with any device you have - stay with the task on hand that is paying attention to the lecture. Maintaining focus on a difficult skill, but like most skills, one that gets better with practice.

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

info | grading | slides | homework | projects | samples | timeline

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:

Homework 1 (due:

*April 8*)Homework 2 (due:

*April 23*)Homework 3 (due:

*May 8*)

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

Project 1: League of Legends Analytics (due:

*April 2*) (slides) (grading guide)Project 2: Mazetool Analytics (due:

*April 14*) (slides) (grading guide)Project 3: Hearthstone Analytics (due:

*April 27*) (slides) (grading guide)Project 4: You Pick Analytics (proposal:

*May 4*, report:*May 13*) (slides) (highlights)

info | grading | slides | homework | projects | samples | timeline

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:

info | grading | slides | homework | projects | samples | timeline

Mark Claypool (`claypool`

at `cs.wpi.edu`

)