Course Syllabus – Big Data

CS 677 ⋅ Spring 2023 ⋅ 4 Credits

Course Information

Lecture: Tuesday & Thursday ⋅ 9:55am – 11:40am ⋅ LM 152

Instructor: Matthew Malensek
Office Hours:


Programming experience, preferably in Go, Java, or Python.

Required Texts/Materials

There is no textbook for this course. Instead, we will read and discuss research papers.

Nevertheless, here are some good resources:

Course Overview

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).

Learning Outcomes

After completing the course, students will be able to:

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:

Grades will be assigned as follows:

Score Range Grade
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.

Research Papers: we will read several research papers throughout the semester. There are two parts to these assignments:

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. One of the major components of the class will be participating in system design discussions to weigh the trade-offs associated with the different types of distributed systems and data processing platforms.

Grading Policy:

Late Policy:

Classroom Conduct

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.

Important Dates

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 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:

Behavioral Expectations

All students are expected to behave in accordance with the Student Conduct Code and other University policies (see 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.

Academic Integrity

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 The policy covers:

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

Confidentiality, Mandatory Reporting, and Sexual Assault

As instructors, one of our responsibilities is to help create a safe learning environment on our campus. We also have a mandatory reporting responsibility related to our role as faculty. We are required to share information regarding sexual misconduct or information about a crime that may have occurred on USF’s campus with the University. Here are some useful resources related to sexual misconduct: