Instructor: Dr. Chelsea Parlett-Pelleriti ([email protected])
- Monday 10am-11:30am (ZOOM)
- Tuesday 2:15-4:15pm (IN-PERSON)
- Wednesday 6-7pm (ZOOM)
- Thursday 10-11:30am (IN-PERSON)
- Monday 12:30pm-2:30pm and 4:00pm - 6:30pm
- Tuesday 4:00pm - 6:30pm
- Thursday 3:00pm - 5:30pm
- Friday 12:00pm - 3:30pm
- Tuesday 9:30am-10:30am
- Friday 10am-12pm
- Install Python
- Other Python packaged we will use: jupyterlabs, sklearn, plotnine, pandas, numpy, keras, tensorflow.
By the end of the semester the students should
- Understand computational issues related to data mining on the large scale.
- Enumerate standard algorithms for supervised and unsupervised learning
- Perform exploratory data analysis and visualization
- Formulate techniques for outlier detection and data cleaning for domain-specific data sets
- Carry out statistical analysis of data
- Analyze data for patterns
- Build statistical models of large data sets
The Chapman experience creates outcomes which are consistent with our identity. Similar to the General Education program, each degree program, or major, at Chapman has a unique set of learning outcomes, or student abilities that are not only related to Chapman's institutional mission and goals, but also unique to the student's discipline or field of study. For more information, Fowler School of Engineering Program Learning Outcomes.
Students create sophisticated arguments supported by quantitative evidence and can clearly communicate those arguments in a variety of formats (using words, tables, graphs, mathematical equations, etc., as appropriate).
This course provides a survey of algorithms, tools, and techniques for computing with Big Data. Students will be exposed to fundamental concepts in data mining, machine learning, and information retrieval systems, with special emphasis on techniques for data visualization and analysis. Recent advances in high performance computing, such as map-reduce, will be presented in the context of Big Data. Students will apply data mining algorithms to data sets from biology, chemistry, social media, and industry using several popular machine learning packages.
CPSC 230, and MATH 203 OR MGSC 209.
CPSC 392 is a 3 unit course.
NONE. Optional Reading is linked on the Front Page of the Course GitHub.
All course materials will be made available via Canvas and/or Github
Homework will consist of assignments to reinforce material covered in class, and must be submitted electronically. They will typically be due at 11:59pm (unless otherwise specified). Homeworks and Final Project must be turned in with a README file as either a .txt or a .md file (see Admin Folder on GitHub for a template). Homework files must be turned in in the format specified (see Collaboration Policy below for more information).
Stuff happens. So, you have 8 "extra days" that you can use, no questions asked, throughout the semester for any homework or project (NOT Quizzes and Tests). You must use 1 whole extra day at a time (i.e. you cannot use 3 hours of an extra day for one assignment, and 21 on another). If an assignment was due Monday at 11:59pm and you use 1 late day, the assignment would then be due Tuesday at 11:59pm. It is your responsbility to keep track of the number of late days you have, and how many you have used.
To qualify for an extra day, you must fill out the Late Days Google Form linked on Canvas under the Syllabus Tab BEFORE the assignment is due. Fill the form out ONCE per late day used (e.g. if using two late days, fill out the form twice). Because of these "extra days", late work will automatically be penalized a 0.15% per hour (or part of an hour) late. You must ALSO put a comment on the assignment (in CANVAS) indicating how many late days you used (e.g. "I used 2 late days") Because of grading deadlines, extra days CANNOT be used during finals week and may NOT prevent peer feedback during the final project.
Again, to use a Late Day:
- Fill out the Google Form
- Leave a Comment on your Canvas Submission
Failure to follow these instructions may result in the late day not being applied.
All programs must be written in Python unless otherwise specified, and should use the packages taught in this course. Grading will be based on correctness, clarity, elegance of solution, and style (comments, naming conventions, etc.). Assignments must reflect your own work, and any outside resources (including--but not limited to--tutors, websites, books, LLMs...etc.) must be cited in your README file with specific links, specific prompts (if using an LLM), or specific citations.
There will be weekly quizzes on the lecture material and classwork, which will count toward the Quizzes grade. You MUST be in person to take the quiz. Missed quizzes cannot be made up at a different time. However, I will drop your lowest 2 quiz scores, so use these 2 dropped quizzes to account for sick days or missed classes. In addition to the two dropped quiz scores, ONE missed quiz during the semester (with a score of 0, due to student being absent) can be replaced with a small, extra take-home assignment. In the case of documented conflicts (such as sports) that are known in advance, the student may schedule to take the quiz at a different time in advance, these re-schedules quizzes must be taken the week of the quiz.
There will also be two in-class exams. In-class exams must be taken in-person on the dates specified, however I don't believe taking tests while sick demonstrates your true ability, so in the case of a well-documented, unavoidable conflict or illness, I will do my best to accommodate you. You must notify me prior to missing the exam.
Finally there will NOT be a final exam, rather there will be a final project and presentation (this will take place during the final exam time, it is expected that all students will be present, in-person during the final time).
Information/Announcements for the course will mainly be disseminated through our Course Slack. YOU MUST join the course slack channel and check for updates. Materials will also be available through the course GitHub (https://github.com/cmparlettpelleriti/CPSC392ParlettPelleriti).
20%
30%
15%
15%
20%
| % Range | Letter Grade |
|---|---|
| 93-100 | A |
| 90-92.99 | A- |
| 87-89.99 | B+ |
| 83-86.99 | B |
| 80-82.99 | B- |
| 77-79.99 | C+ |
| 73-76.99 | C |
| 70-72.99 | C- |
| 67-69.99 | D+ |
| 63-66.99 | D |
| 60-62.99 | D- |
| < 60 | F |
You must score a 70 or above to receive a P when taking the course P/NP.**
You have much to learn from your classmates, and so I encourage you to discuss and study course material together. However, all work you submit for this course must be your own, and must be completed individually unless otherwise specified. More specifically, you may not present source code or programs copied from the Internet, Large Language Models (or other AI), other texts, other students, etc as your own work. Of course, you are free to use whatever reference materials you like, but please cite them in a README turned in with your assignments. I assume you are familiar with Chapman's policy on academic misconduct is presented below and any incidents of academic misconduct or dishonesty will be dealt with severely in accordance with this policy.
AI (such as ChatGPT, and other Large Language Models) should not be used to do assignments for you (they are completely prohibited on Exams and Quizzes), however these tools are often useful for debugging code. If you use output from an AI model you must cite it in your readme in the following form:
- The exact prompt(s) you put into the model
- Any clarifications or changes you asked for from the model
- Which part(s) of the assignment/assessment this output was used in
If AI is used to the extent that the assignment is deemed to not be your own work, the assignment will not be accepted and a 0 will be given for any portion(s) deemed not your work.
I expect that everyone will maintain a classroom conducive to learning. Thus, everyone is expected to behave with basic politeness, civility, and respect for others. In particular, talking in class is okay if it’s part of a class discussion or with me. Private communications are not permitted, especially during exams. Neither are reading extraneous materials, using electronic equipment off task, or sleeping. As this is a Computer Science class, technology is allowed to aid in learning and understanding material. However, please do not use a personal device for any purpose unrelated to our class. All devices should be silenced. Cell phones should be put away. Suggestions for improvement are welcome at any time. Any concern about the course should be brought first to my attention.
Chapman University is a community of scholars that emphasizes the mutual responsibility of all members to seek knowledge honestly and in good faith. Students are responsible for doing their own work and academic dishonesty of any kind will be subject to sanction by the instructor/administrator and referral to the university Academic Integrity Committee, which may impose additional sanctions including expulsion. Please see the full description of Chapman University's policy on Academic Integrity.
In compliance with ADA guidelines, students who have any condition, either permanent or temporary, that might affect their ability to perform in this class are encouraged to contact the Office of Disability Services. If you will need to utilize your approved accommodations in this class, please follow the proper notification procedure for informing your professor(s). This notification process must occur more than a week before any accommodation can be utilized. Please contact Disability Services at (714) 516–4520 if you have questions regarding this procedure or for information or to make an appointment to discuss and/or request potential accommodations based on documentation of your disability. Once formal approval of your need for an accommodation has been granted, you are encouraged to talk with your professor(s) about your accommodation options. The granting of any accommodation will not be retroactive and cannot jeopardize the academic standards or integrity of the course.
Chapman University is committed to ensuring equality and valuing diversity. To access information part of Chapman's DEI (Diversity, Equity, and Inclusion) initiative, including on-campus resources, student-driven clubs, faculty and staff advocates, and how to report a concern or incident, please view the Diversity and Inclusion Resources. Students and professors are reminded to show respect at all times as outlined in Chapman’s Discrimination, Harassment, and Retaliation Prevention Policy. Any violations of this policy should be discussed with the professor, the Dean of Students and/or otherwise reported in accordance with this policy.
Over the course of the semester, you may experience a range of challenges that interfere with your learning, such as problems with friend, family, and or significant other relationships; substance use; concerns about personal adequacy; feeling overwhelmed; or feeling sad or anxious without knowing why. These mental health concerns or stressful events may diminish your academic performance and/or reduce your ability to participate in daily activities. You can learn more about the resources available through Chapman University’s Student Psychological Counseling Services.
Fostering a community of care that supports the success of students is essential to the values of Chapman University. Occasionally, you may come across a student whose personal behavior concerns or worries you, either for the student’s well-being or yours. In these instances, you are encouraged to contact the Chapman University Student Concern Intervention Team who can respond to these concerns and offer assistance. While it is preferred that you include your contact information so this team can follow up with you, you can submit a report anonymously. 24-hour emergency help is also available through Public Safety at 714-997-6763.
Religious Accommodation at Chapman University Consistent with our commitment of creating an academic community that is respectful of and welcoming to persons of differing backgrounds, we believe that every reasonable effort should be made to allow members of the university community to fulfill their obligations to the university without jeopardizing the fulfillment of their sincerely held religious obligations. Please review the syllabus early in the semester and consult with your faculty member promptly regarding any possible conflicts with major religious holidays, being as specific as possible regarding when those holidays are scheduled in advance and where those holidays constitute the fulfillment of your sincerely held religious beliefs.
This syllabus is subject to change only under extenuating circumstances. Updates will be posted on the course website or through Slack.