EEL 6935 Big Data Ecosystems
1. Catalog Description – (3 credits) Data mining, statistics, conventional software tools, big
data analytics software stack, big system software stack, large- scale machine learning
algorithms, recommendation systems, and applications in science, engineering, business, and
health.
2. Pre-requisites – EEL 3834 or equivalent
3. Course Objectives – the student will have an understanding of big data generated from
natural systems, engineered systems, and human activities and the challenges they present.
The student will learn a holistic methodology on the design of big data ecosystems and
compare that to real world case studies in the areas of science, engineering, business, and
health.
4. Contribution of course to meeting the professional component (ABET only – undergraduate
courses) 5. Relationship of course to program outcomes: Skills student will develop in this course
(ABET only undergraduate courses) 6. Instructor – Dr. Xiaolin (Andy) Li
a. Office location: 433 NEB
b. Telephone: 352-392-2651
c. E-mail address: andyli@ece.ufl.edu
d. Class Web site: http://www.andyli.ece.ufl.edu/
e. Office hours: TBD
7. Teaching Assistant - TBD
a. Office location:
b. Telephone:
c. E-mail address:
d. Office hours:
8. Meeting Times and Location - TBD
9. Class/laboratory schedule - 3 class periods consisting of 50 minutes each
10. Material and Supply Fees - None
11. Textbooks and Software Required a. Title: Mining of Massive Datasets
b. Author: Jure Leskovec, Anand Rajaraman, and Jeffrey David Ullman
c. Publication date and edition: Cambridge University Press, 2014 (Free PDF book is
available at http://i.stanford.edu/~ullman/mmds.html)
d. ISBN number:
12. Recommended Reading a. Recent conference papers and online resources/documents
b. Hadoop: The Definitive Guide, Tom White, O'Reilly Media, 3rd Edition, 2012. c.
Data-Intensive Text Processing with MapReduce, Jimmy Lin and Chris Dyer,
c. 2010.
d. The Fourth Paradigm: Data-Intensive Scientific Discovery, Tony Hey, Stewart
e. Tansley, and Kristine Tolle, Microsoft Research, 2009.
f. Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig,
Prentice Hall, 3rd Edition, 2009.
g. Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer,
h. 2007.
i. Data Mining: Concepts and Techniques, Jiawei Han, Micheline Kamber,
Morgan Kaufmann, 3rd Edition, 2011.
j. Machine Learning, Tom M. Mitchell, McGraw-Hill, 1997.
k. Programming in Scala: A Comprehensive Step-by-Step Guide, Martin
l. Odersky, Lex Spoon, and Bill Venners, 2nd Edition, 2011.
m. The Way To Go: A Thorough Introduction To The Go Programming
n. Language, Ivo Balbaert, iUniverse, 2012
13. Course Outline (provide topics covered by week or by class period) –
 Introduction
o Data Mining and Statistics
o Algorithms
o Tools: R, Weka, RapidMiner, Julia
 Big Data Stack
o Data Path: Messaging, Online Processing/Query, Nearline/Stream Processing,
Offline Processing
o Data Store: Databases, Distributed File Systems, Storage
o Analytics: Machine Learning, Graph, Search
o Control Plane: Coordination and Management
o Tools: Kafka, Spark, Storm, GraphLab, MLbase, Hadoop, Cassandra
 Large-scale Machine Learning
o Dimension Reduction
o Recommendation Systems
o Clustering and Classification
o Deep Learning
o Mining Data Streams
o Mining the Web
 Data-driven Software-defined Ecosystems
o Mesos, YARN, SuperStack
o Software-defined Networking
 Case Studies: Science, Engineering, Business and Health
14. Attendance and Expectations - Attendance is expected from students in order to properly
follow class progress. There are no explicit penalties for absence. Cell phones and other
electronic devices are to be silenced. No text messaging during class or exams. Additional
class policy guidelines are provided in a separate class policies document.
Homework and programming assignments are due by 11:55pm of the due date (unless
announced in class otherwise). Late homework will NOT be accepted. Late program penalty
is 10% per day, according to the timestamp of your online submission.
Requirements for class attendance and make-up exams, assignments, and other work in this
course are consistent with university policies that can be found in the online catalog at:
https://catalog.ufl.edu/ugrad/current/regulations/info/attendance.aspx
15. Grading – homework, reports, and projects = 80%, exams = 20%
16. Grading Scale (e.g., 90-100 A, 85-89 B+, 80-84 B, etc.) If grades are to be curved, so state.
Values should not overlap and the full grade to percentage/points map must be included. –
A
90-100
A-
B+
B
B-
C+
C
C-
D+
D
D-
E
85-89 80-84 75-79 xx-xx 70-74 xx-xx 65-69 xx-xx 60-64 xx-xx 0-59
This statement must be included in every grade scale for undergraduate level 1000-4000
syllabi:
“A C- will not be a qualifying grade for critical tracking courses. In order to graduate,
students must have an overall GPA and an upper-division GPA of 2.0 or better (C or better).”
Note: a C- average is equivalent to a GPA of 1.67, and therefore, it does not satisfy this
graduation requirement. For more information on grades and grading policies, please visit:
https://catalog.ufl.edu/ugrad/current/regulations/info/grades.aspx
This statement must be included in every grade scale for 5000 level graduate syllabi:
“Undergraduate students, in order to graduate, must have an overall GPA and an upperdivision GPA of 2.0 or better (C or better). Note: a C- average is equivalent to a GPA of
1.67, and therefore, it does not satisfy this graduation requirement. Graduate students, in
order to graduate, must have an overall GPA of 3.0 or better (B or better).” Note: a Baverage is equivalent to a GPA of 2.67, and therefore, it does not satisfy this graduation
requirement. For more information on grades and grading policies, please visit:
https://catalog.ufl.edu/ugrad/current/regulations/info/grades.aspx
This statement must be included in every grade scale for 6000 level graduate syllabi:
“In order to graduate, graduate students must have an overall GPA and an upper-division
GPA of 3.0 or better (B or better).” Note: a B- average is equivalent to a GPA of 2.67, and
therefore, it does not satisfy this graduation requirement. For more information on grades and
grading policies, please visit: http://gradschool.ufl.edu/catalog/current-catalog/cataloggeneral-regulations.html#grades
17. Make-Up Exam Policy – Only when verifiable extenuating circumstances can be
demonstrated will make-up exams or extended assignment due dates be considered.
Verifiable extenuating circumstances must be reasons beyond control of the students, such as
illness or accidental injury. Poor performance in class is not an extenuating circumstance.
Advise your instructor of the verifiable extenuating circumstances in advance or as soon as
possible. In such situations, the date and nature of the make-up exams and the extended due
dates for the assignments will be decided by the instructor.
If you have a University-approved excuse and arrange for it in advance, or in case of
documented emergency, a make-up exam will be allowed and arrangements can be made for
making up missed work. University attendance policies can be found at:
https://catalog.ufl.edu/ugrad/current/regulations/info/attendance.aspx
Otherwise, make-up exams will be considered only in extraordinary cases, and must be taken
before the scheduled exam. The student must submit a written petition to the instructor two
weeks prior to the scheduled exam and the instructor must approve the petition.
18. Honesty Policy – Discussion of techniques and ideas covered in class is encouraged.
However, every line of all assignments must be your own. A statement required by the
university: "Care must be taken that exam answers are not seen by others, that term papers or
projects are not plagiarized by others or otherwise misused by others, etc. Even passive
cooperation in a dishonest enterprise is unacceptable." In programming assignments,
discussion of techniques in a natural language (such as English) is allowed, but a discussion
in a computer or algorithmic language is not allowed. (Computer language discussions and
questions are to be limited to the language and should not concern the assignment.) Stealing,
giving or receiving any code, drawings, diagrams, texts or designs (from others or Internet) is
not allowed. Project reports should be written in your own words; apparent copy (ONE
sentence) is assumed as plagiarism, if not quoted. In examinations, no discussion of any kind
(except with the instructor) is allowed. No access to any type of written material is allowed.
Students who do not comply with the above described collaboration policy will receive a
grade of F in the course. Furthermore, the case will be reported to the University Officials.
UF students are bound by The Honor Pledge which states, “We, the members of the University of
Florida community, pledge to hold ourselves and our peers to the highest standards of honor and
integrity by abiding by the Honor Code. On all work submitted for credit by students at the
University of Florida, the following pledge is either required or implied: “On my honor, I have
neither given nor received unauthorized aid in doing this assignment.” The Honor Code
(http://www.dso.ufl.edu/sccr/process/student-conduct-honor-code/) specifies a number of
behaviors that are in violation of this code and the possible sanctions. Furthermore, you are
obligated to report any condition that facilitates academic misconduct to appropriate personnel. If
you have any questions or concerns, please consult with the instructor or TAs in this class.
19. Accommodation for Students with Disabilities – Students requesting classroom
accommodation must first register with the Dean of Students Office. That office will provide
documentation to the student who must then provide this documentation to the course
instructor when requesting accommodation.
20. UF Counseling Services – Resources are available on-campus for students having personal
problems or lacking clear career and academic goals. The resources include:
·
·
·
UF Counseling & Wellness Center, psychological and psychiatric services, 3190
Radio Rd, 392-1575, online: http://www.counseling.ufl.edu/cwc/Default.aspx,
Career Resource Center, Reitz Union, career and job search services, 392-1601.
University Police Department, 392-1111 or 911 for emergencies
21. Software Use – All faculty, staff and student of the University are required and expected to
obey the laws and legal agreements governing software use. Failure to do so can lead to
monetary damages and/or criminal penalties for the individual violator. Because such
violations are also against University policies and rules, disciplinary action will be taken as
appropriate. We, the members of the University of Florida community, pledge to uphold
ourselves and our peers to the highest standards of honesty and integrity.
22. Course Evaluation – Students are expected to provide feedback on the quality of instruction
in this course based on 10 criteria. These evaluations are conducted online at:
https://evaluations.ufl.edu. Evaluations are typically open during the last two or three weeks
of the semester, but students will be given specific times when they are open. Summary
results of these assessments are available to students at: https://evaluations.ufl.edu/results.