Course Syllabus – Big Data
CS 677 ⋅ Fall 2020 ⋅ 4 Credits
Lecture: Tuesday & Thursday ⋅ 9:55am – 11:40am ⋅ Zoom Live Stream
Instructor: Matthew Malensek
Office: HR 406
Hours: T, Th 3:00pm – 4:00pm & 9:30pm – 10:30pm ⋅ W 1:30pm – 2:30pm
Appointments: Sign Up ⋅ Personal Zoom Link
Programming experience, preferably in Java or Python.
There is no textbook for this course. Instead, we will read and discuss research papers.
Nevertheless, here are some good resources:
- Data-Intensive Systems: Designing Data-Intensive Applications. Martin Kleppmann.
- Lambda Architecture: Big Data: Principles and best practices of scalable realtime data systems. Nathan Marz and James Warren.
- Distributed Systems: Distributed Systems: Principles and Paradigms. Andrew S. Tanenbaum and Maarten van Steen.
This course examines the algorithmic and systems challenges associated with big data. Topics include storage frameworks (key-value, in-memory, wide-column), scalable computing paradigms (MapReduce, Spark, stream processing), and analysis techniques (sentiment analysis, predictive modeling).
After completing the course, students will be able to:
- Explain the theory behind the I/O, memory, computational, and algorithmic trade-offs associated with big data
- Leverage big data frameworks and distributed systems to carry out analysis and gain insight
- Design and implement big data frameworks and algorithms
- Preprocess and prepare data for machine learning and visualization
- Summarize and critique research papers from the field
These outcomes will be assessed via programming assignments, scientific paper reviews, and quizzes.
The course will be graded on a A-F basis. The grade distribution is:
- Projects: 40%
- Research Papers: 25%
- Quizzes: 25%
- Participation & Labs: 10%
Grades will be assigned as follows:
|100 – 93.0||A|
|92.9 – 90.0||A-|
|89.9 – 87.0||B+|
|86.9 – 83.0||B|
|82.9 – 80.0||B-|
|79.9 – 77.0||C+|
|76.9 – 73.0||C|
|72.9 – 70.0||C-|
|69.9 – 67.0||D+|
|66.9 – 63.0||D|
|62.9 – 60.0||D-|
|59.9 – 0||F|
This scale is subject to change; scoring in the ranges above guarantees you will receive at least the grade listed.
Projects: The best way to learn is by putting theory into practice. This course features large projects that count for the majority of your grade. Remember to start early, ask questions, and go to office hours if necessary.
Scientific Research Papers: we will read several research papers throughout the semester. These assignments involve reading the paper and producing a written presentation and report outlining your analysis of the work, along with in-class discussion.
Quizzes: Your knowledge of the concepts covered in class will be evaluated via quizzes.
Participation & Labs: Beyond the research paper group discussions, we will also have small lab assignments or discussions in class to help reinforce content from the lecture. While attendance is not required in this class, you are encouraged to participate. This includes asking/answering questions during lecture or on the discussion board. To earn points during class, ask or answer a question (ideally write your question/answer in the Zoom chat). Participation on the discussion board is based on the number of “likes” your posts get (instructors will “like” helpful posts, and your peers can as well).
- Do not cheat. Review the Honor Code, and if in doubt about whether or not something is cheating, ask the professor.
- The course staff will run cheat detection software that includes past assignments.
- “Collaboration” that involves sharing code/solutions is considered cheating.
- If you cheat, you will get a 0 on the assignment or an F in the class.
- Submit code via GitHub. Commit your changes frequently as you work on the assignments.
- Due dates are posted on the course schedule page. Assignments are due at 11:59pm on the due date.
- Makeup quizzes will not be administered unless arranged at least one week in advance.
- Late lab/discussion/participation assignments are not accepted.
- Late projects are deducted 10% per day for a maximum of three days. Afterward, no credit will be given.
You are here to learn. Be professional and courteous toward your peers, and help create a learning environment that supports diverse thinking, experiences, perspectives, and identities. If you need to use an electronic device during a lecture, do so in a way that doesn’t distract others. And most importantly, be excellent to each other.
- August 18: Classes/late registration begin
- August 24: Last day to add a class
- September 4: Census date
- September 7: Labor day holiday, no classes
- October 30: Last day to drop courses or withdraw
- November 23–27: Thanksgiving recess
- December 3: Last day of classes
Students with Disabilities
The University of San Francisco is committed to providing equal access to students with disabilities. If you are a student with a disability, or if you think you may have a disability, please contact Student Disability Services (SDS) at email@example.com or 415 422-2613, to speak with a disability specialist. (All communication with SDS is private and confidential.) If you are eligible for accommodations, please request that your accommodation letter be sent to me as soon as possible; students are encouraged to contact SDS at the beginning of the semester, as accommodations are not retroactive. Once I have been notified by SDS of your accommodations we can discuss your accommodations and ensure your access to this class or clinical setting. For more information please visit the SDS website: https://www.usfca.edu/student-disability-services.
All students are expected to behave in accordance with the Student Conduct Code and other University policies (see http://www.usfca.edu/fogcutter/). Students whose behavior is disruptive or who fail to comply with the instructor may be dismissed from the class for the remainder of the class period and may need to meet with the instructor or Dean prior to returning to the next class period. If necessary, referrals may also be made to the Student Conduct process for violations of the Student Conduct Code.
As a Jesuit institution committed to cura personalis – the care and education of the whole person – USF has an obligation to embody and foster the values of honesty and integrity. USF upholds the standards of honesty and integrity from all members of the academic community. All students are expected to know and adhere to the University’s Honor Code. You can find the full text of the code online at http://myusf.usfca.edu/academic-integrity/. The policy covers:
- Plagiarism – intentionally or unintentionally representing the words or ideas of another person as your own; failure to properly cite references; manufacturing references.
- Working with another person when independent work is required.
- Submission of the same paper in more than one course without the specific permission of each instructor.
- Submitting a paper written by another person or obtained from the Internet.
Counseling and Psychological Services (CAPS)
CAPS’ diverse staff offers brief individual, couple, and group counseling to student members of our community. CAPS services are confidential and free of charge. Call (415) 422-6352 for an initial consultation appointment. Telephone consultation through CAPS After Hours is available Monday - Friday from 5:00 p.m. to 8:30 a.m., 24 hours during weekends and holidays; call the above number and press 2. Further information can be found at https://myusf.usfca.edu/student-health-safety/caps.
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- To report any sexual misconduct, students may visit the Title IX coordinator (UC 5th floor) or see many other options by visiting usfca.edu/student_life/safer.
- Students may speak to someone confidentially or report a sexual assault confidentially by contacting Counseling and Psychological Services at (415) 422-6352.
- To find out more about reporting a sexual assault at USF, visit USFs Callisto website at: usfca.callistocampus.org.
- For an off-campus resource, contact San Francisco Women Against Rape (SFWAR) (415) 647-7273 (sfwar.org).