Course Syllabus – Big Data
CS 677-01 ⋅ Fall 2018 ⋅ 4 Credits
Lecture: Tuesday & Thursday ⋅ 4:35 – 6:20pm ⋅ LS G12
Instructor: Matthew Malensek
Office: HR 412A
Hours: M, T, Th 3:30 – 4:30pm or by appointment
Programming experience, preferably in Java or Python.
There is no textbook for this course. Instead, we will read and discuss research papers.
If you feel you’d like to get more background on distributed systems, Distributed Systems: Principles and Paradigms by Andrew S. Tanenbaum and Maarten van Steen (2nd Edition) is a good choice.
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: 55%
- Quizzes: 15%
- In-class discussion/labs: 15%
- Research Paper Reports: 15%
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.
Scientific Research Papers: we will read several research papers throughout the semester. These assignments involve reading the paper and producing a written report outlining your analysis of the work, along with in-class discussion.
In-Class Discussion/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.
- All assignments are due at 11:59pm on the date posted on Canvas.
- No late discussion assignments or written reports will be accepted.
- Submit code via GitHub. Commit your changes frequently as you work on the assignments.
- Each student is allocated 4 ‘slip days’ that can be used to turn in projects late. Use your slip days wisely; once they are gone, you must submit your project on time to receive credit.
- If you’d like to make small corrections or bug fixes on a graded project, you may use slip days to do so. Check with the instructor first.
- In the event of an excused absence, you may make up a quiz if arranged at least one week in advance.
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.
- Code must be submitted via GitHub.
- If you cannot attend an in-class discussion, you may arrange to submit a report instead if you provide notice 24 hours in advance.
- August 21 – Classes/Late Registration Begin
- August 27 – Last day to add classes
- September 3 – Labor day holiday (no classes)
- September 7 – Census Date
- October 15–16 – Fall Break (No Classes)
- November 2 – Last Day to Drop Courses or Withdraw
- November 22–23 – Thanksgiving Recess
- December 5 – Last Day of Classes
- December 6 – Study/Review Day
- Dember 7–13 – Final Examination Period
- January 2 – Last Day to Submit Grades
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.
- The penalties for violation of the policy may include a failing grade on the assignment, a failing grade in the course, and/or a referral to the Academic Integrity Committee.
Students with Disabilities
If you are a student with a disability or disabling condition, or if you think you may have a disability, please contact USF Student Disability Services (SDS) at 415 422-2613 within the first week of class, or immediately upon onset of disability, to speak with a disability specialist.
If you are determined eligible for reasonable accommodations, please meet with your disability specialist so they can arrange to have your accommodation letter sent to me, and we will discuss your needs for this course. For more information, please visit: http://www.usfca.edu/sds or call (415) 422-2613.
All students are expected to behave in accordance with the Student Conduct Code and other University policies (see http://www.usfca.edu/fogcutter/). Open discussion and disagreement is encouraged when done respectfully and in the spirit of academic discourse. There are also a variety of behaviors that, while not against a specific University policy, may create disruption in this course. 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.
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