Instructor:
Dr. Angelina A Tzacheva, Department of Computer Science, College of
Computing and Informatics,
EMail:
aatzache@uncc.edu, OfficeHours: Tuesday 3:00 pm - 5:00 pm
via WebEx link :
Join Zoom Meeting
https://us02web.zoom.us/j/87995946159?pwd=NUdna25EMU5uL1FkTVdwek14MFZUZz09
Meeting ID: 879 9594 6159
Passcode: 059885
SkypeID: angelina.tzacheva
Teaching Assistants:
1. Shruthi Vasalamarri, EMail: svasalam@uncc.edu ,
Office Hours: Monday 11am - 12:30pm & Tuesday 11am - 12:30pm via Zoom links :
OfficeHoursDay1: Monday 11am - 12:30pm
ZoomLink_OfficeHoursDay1:
https://uncc.zoom.us/j/95732338075
OfficeHoursDay2: Tuesday 11am - 12:30pm
ZoomLink_OfficeHoursDay2:
https://uncc.zoom.us/j/95037785250
SkypeID: svasalam
2. Bhargava Ram Bonala , EMail: bbonala@uncc.edu ,
Office Hours: Wednesday 10am - 11:30am & Thursday 10am - 11:30am via Zoom links :
OfficeHoursDay1: Wednesday 10am - 11:30am
ZoomLink_OfficeHoursDay1:
https://uncc.zoom.us/j/3513895509
OfficeHoursDay2: Thursday 10am - 11:30am
ZoomLink_OfficeHoursDay2:
https://uncc.zoom.us/j/3513895509
SkypeID: live:.cid.73dad19e7fdca4bc
3. Amulya Cheyala , EMail: achepya1@uncc.edu ,
Office Hours: Friday 10am - 11:30am & Saturday 10am - 11:30am via Zoom links :
OfficeHoursDay1: Friday 10am - 11:30am
ZoomLink_OfficeHoursDay1:
https://uncc.zoom.us/j/98265587833
OfficeHoursDay2: Saturday 10am - 11:30am
ZoomLink_OfficeHoursDay2:
https://uncc.zoom.us/j/98265587833
SkypeID: chepyala024
Prerequisites:
ITCS 2114 Algorithms & Data Structures. Familiarity with Java (or
Python / Scala), Unix, Data Structures and Algorithms, Linear Algebra,
Probability and Statistics. Good
programming skills, and solid mathematical background.
Review Documents
-
Probability
Reminders
-
Introduction
to Proof Techniques
-
Linear
Algebra
Textbooks:
1.
Mining
of Massive Datasets, 2nd Edition, by Jure Leskovec, Anand
Rajaraman, and Jeff Ullman, Cambridge University Press, 2014, ISBN:
9781107077232
2.
Hadoop:
The Definitive Guide, 4th Edition, by Tom White, O’Reilly Media,
2015, ISBN: 9781491901632
3.
Learning
Spark Lightning-Fast Big Data Analysis, by Holden Karau, Andy
Konwinski, Patrick Wendell, Matei Zaharia, O’Reilly Media, 2015, ISBN:
9781449358624
4.
Introduction
to Information Retrieval, by Christopher D. Manning, Prabhakar
Raghavan, and Hinrich Schutze, Cambridge University Press, 2008, ISBN:
9780521865715
Course Outline:
- Distributed Computing and Cloud
- Hadoop, MapReduce
- Pig, Hive, Spark
- Information Retrieval, Indexes, Scores
- Web Search, Page Rank
- Data Mining Algorithms
- Rules, Clustering, Classification
- Social Network Analysis
Student Learning Outcomes:
1. Recognize and Define Cloud Platforms and Tools
2. Deploy and Analyze Datat using Cloud Tools
3. Demonstrate Programming Skills for Cloud Platforms
Instructional
Method:
This
course takes case and project approach, complemented by lectures,
and group activities. Activle Learning Activites and Flipped Classroom
approach will be used once per week.
Lectures Notes, Videos, and Reading Assignments are posted in the
syllabus table below, as well as on Canvas.
Please
download and read each lecture material, and view each Video on the
specified day.
All
material by date is listed, including preparation for the exams with
sample questions. The exams are open-book / open-notes. The textbooks
are necessary, as exam
questions are based on lecture notes AND on the text.
Credit Hours:
This is a 3 credit hour course.
This
course is designed to require about 10 hours per week - for readings,
exams, exercises, video cases, and group project work.
The
material is technical, and requires dedication of time to
comprehend. To complete course
successfully, Please do not plan on
cramming all
lectures the day before the exam. Designate 3 hours every lecture day
for reading
the given lecture, and book chapter. Designate additional 4 hours per
week for Exercises, VideoCase assignments, and Group meetings /
activites.
Exercises are assigned after each
chapter. The Exercises are due on Canvas
on the dates they are assigned. Exercises are *not accepted*
through e-mail. Late Exercises are not accepted.
Grading:
The final course grade is determined on the following weights:
Exercises 20%
VideoCases 10%
GroupActivites 14%
Midterm Exam 15%
Group Project 16%
Final Exam 15%
Attendance 10%
Grading scale:
A 90% - 100%
B 80% - 89%
C 70% - 79%
D 60% - 69%
F less than 60%
X academic dishonesty
Gradig Enquiries:
Grades to all Exercises, Exams, and Project are posted on Canvas
shortly after the assignments are due. Students are expected to observe
their grades on Canvas, and e-mail TA and Instructor immediately if
they notice any issues . Students who have questions or concerns about
their final CourseTotal grade are expected to e-mail the TA and
Instructor at least 1 week prior to letter Grades being assigned on
registration system . The letter grades Due date
is found on the University Calenadar at the end . Once the letter grades are
assigned and rolled on registration system , we are unable to
change the grades anymore .
Academic
Integrity
and Honesty:
Students are required to read and abide by the Code of Student
Academic Integrity
availbe from Dean of Students Office. This code forbids cheating,
fabrication or falsification of information, multiple submissions of
academic work, plagiarism (including viewing others work without
instructor permission), abuse of academic materials, and complicity of
academic dishonesty. Violations of the Code of Student Academic
Integrity, including plagiarism, result in disciplinary action as
provided by the Code.
Civility:
We
are
concerned with a positive learning experience. This course strives to
create an inclusive academic climate in which the dignity of all
individuals is respected and maintained. We value diversity that is
beneficial to both employers and societey at large. Students are
encouraged to actively and appropriately share their views in class
discussions.
Inclement Weather:
University
Policy states the University is open unless the
Chancellor announces that the University is closed. In the event of
inclement weather, check your e-mail. The instructor will
post a message through e-mail. The instructor will use
their best judgment as to whether class should be held.
Disability:We are committed to access to education. If you have a disability
and need academic accommodations, please provide a letter of
accommodation from Disability Services early in the semester. For more
information on accommodations, contact the Office of Disability Services or visit their office.
Withdrawal:
The University policy on Course
Withdrawal
allows students a limited number of opportunities available to withdraw
from courses. There are financial and academic consequences that may
result from course withdrawal. If a student is concerned about his /
her ability to succeed in this course it is imporant to make an
appointment to speak with the instructor as soon as possible.
Syllabus Revision:
The
instructor may modify the class schedule and syllabus during the course
of the semester. For example - additional educational vidoes will be
posted every week. Same changed will appear on Canvas. Students are responsible
for refreshing their syllabus once per week.
E-Mail
Communication:
Students
are responsible for *all* announcements made in class and on the class
online resources. Students should check the online class resources
throughout the semester. The Instructor and Teaching Assistants send
occasional e-mails with important information. We send this information
to the student's university email address.
Class Expectation:
By
attending class beyond the first week, students agree to follow the
framework and rules related to this course as described above.