Instructor : Dr. Sampath Jayarathna, Web: http://www.cs.odu.edu/~sampath/
Contact : Office: 3109, Email: sampath@cs.odu.edu , Phone: (757) 683-7787
Office Hours : Tuesday, 3.00 PM – 4.00 PM, or email me for an appointment
Schedule : Tuesday and Thursday, Room: BAL 2069, Time: 5.45 PM – 7.00 PM
Website : http://www.cs.odu.edu/~sampath/courses/f18/cs795/
Piazza : https://piazza.com/odu/fall2018/cs795/home
Blackboard : https://www.blackboard.odu.edu/
Prerequisites : There are no specific course prerequisites for this course. But, I expect you
to be comfortable
learning new programming
languages/tools/APIs, and knowledge in linear algebra and
statistics.
This
course will introduce students to this rapidly growing field of Data
Science
and equip them with some of its basic principles and tools as well as
its
general mindset. Students will learn concepts, techniques and tools
they need
to deal with various facets of data science practices
Required textbook. No textbook is required. All the key course content will be documented in slides, which will be available in the course website after each lecture.
List of optional but recommended materials. You may find some of these optional textbooks helpful, though none are required:
o Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, By William McKinney, O'Reilly; 2 edition (October 20, 2017)
o Data
Science from Scratch: First Principles with Python By Joel Grus,
O'Reilly 1st
edition, 2015
I have an
open-door policy i.e.,
office visits. My posted office hours are times when I will make
concerted
effort to be available. Occasionally administrative meetings or
emergencies may
interfere with these posted times. The open-door policy is: if my door
is open,
I am in and welcome walk-in visitations. I am committed to
supporting students with
disabilities. If you have challenges related to these issues or others
I want
to work with you to help you succeed. Please come and talk to me, since
only
you can properly communicate your situation to me.
Topics: The
tentative topics are as follows. Topics and specific course
activities may
change as needed. PowerPoint slides will
be available on the course web page after each lecture.
Week 1: Syllabus and Introductions
Week 2: Python Workshop
Week 3: Pandas
Week 4: NumPy
Week 5: Data Cleaning
Week 6: Unstructured and Semi-Structured Data
Week 7: NoSQL
Week 8: Data Wrangling
Week 9: Text Data Analysis and Inference
Week 10: Evaluations
Week 11: Machine Learning on Data
Week 12: Delivering Results
Week 13: Weka Workshop
Week 14: Recommender Systems
Week 15: Project Presentations/Demo
It is extremely important for you to be engaged in the course. Otherwise, you will fall asleep and wonder what happened to your tuition dollars. So, I encourage you to ask questions during lecture and actively participate at the piazza forum. For the first few weeks, when asking a question at the class, state your name so that I know who you are.
Cell
phones and Tardiness:
You may have cell phones in class, but they must be on mute, or
airplane mode
and not answered until the end of class. You are expected to arrive on
time so
that you do not cause a disruption in the middle of class. I would like
to
start the class at the scheduled time. If you cannot make it on time or
want to
leave early for some reason, please let
me know. Persistent tardiness will
be noted.
Piazza: All questions will be fielded through Piazza. The primary benefit is that for many questions everyone can see the answer and other students can answer as well. I will endorse good student responses. Additionally, I expect you to actively participate in online discussions at Piazza. You can post public or private messages that can only be seen by the instructor. You will be signed up with your odu email, but you may switch to another email.
Blackboard: Blackboard will be used primarily for grade dissemination.
Email: If you send
email to me, please be
sure to include your name and the course number in the body of the
e-mail. You
should also use an appropriate subject line that looks like “C795-HW1”
etc.
Failure to follow these guidelines may result in delayed
response. Again, email should only be used in rare
instances, I will probably point you back to Piazza if you have a
question
related to course materials and/or relevant to other students in the
class.
| 40% | Project: Proposal(5pts), Progress Reports (5pts), Presentation & Demo (15pts), Final Report (15pts) |
| 30% | Final Exam (Final is comprehensive), Thursday, December 13 from 4.00 pm to 6.00 PM |
| 20% | Homework Assignments (4) |
| 5% | Research Paper Presentation + Extra Credit for report |
| 5% | in-class activities |
| 101% | Your Total Score for the class |
Project: The project is an opportunity to tackle a more challenging data science activity. Details, requirements and submission information will be on the course website. For the project, you will work in teams of either 2, or 3 students on a problem of your choosing (or instructor provided topics) that is interesting, significant, and relevant to Data Science. The ultimate goal of your course project is to develop a new tool to tackle some interesting real-world problem. At the end of the semester, we will hold a competition during our regular class time for your project demonstration. All members of a group will receive the same grade on group work. Therefore, it is in your interest to choose other group members (ideally, first day of the class if possible) who have the same goal in the class as you do. It is also in your interest to work together and ensure that all tasks are completed effectively. Your scores on group work may be adjusted based on your contribution (peer-evaluation).
Final
Exam: The final
exam is comprehensive, closed books and will be held on Thursday, December 13 from 4.00 pm to 6.00
PM. You may bring one standard 8.5" by 11" piece of paper with
any notes you deem appropriate or significant (front and back).
In Class Activities: Attendance in class and participation in the discussion are both important to your success in the course. As one crude measure of your participation and course preparation, we will have in class activities related to lecture topics to supplement the learning. I will ask you to bring a computing device (laptop, tablet).
Homework: We will have 4 homework assignments, each worth 5% of your overall grade.
Research Paper Presentation: Technical presentations by students
on emerging trends in data science or tools. Each
student will be responsible for planning a 10-minutes PowerPoint presentation (8
minutes talk, 2 minutes Q&A). Timing strictly enforced, penalty (2 pts out
of 5pts) will be applied for any presentation longer than 10 minutes. The rules
and formats of presentation will be discussed in the lectures. The presenter
must post presentation slides publically at Piazza to the entire class after
the presentation (no points will be given if the slides are not posted within 3
days). Topics: After the first
meeting, you must select a research paper for your presentation. Papers should be
technically rigorous (10 pages or more) for the field Computer Science appear
in IEEE or ACM journals or conferences.
You can ask the professor about other papers if you wish to check their
suitability. No two students may select the same paper, and topics
will be assigned on a first-come first-served basis. Students will volunteer
each week for presentation slots available at the start of each class
time. To lock in a paper, post it in
Piazza or let me know by the time you volunteer for the project. Students
should consult the previous student presentations (available in Piazza) to
avoid duplicate research papers. Extra
Credit: You can
get up to one point added to your final grade through preparing the research
paper summary. Use the same paper to summarize, and provide your thoughts about
this article. Note: Borderline grades
will not be boosted if extra credit is not submitted.
Final course grades are based on the overall average. You are guaranteed a grade based on a 10% window (e.g., 90-100% is an A). Overall class grade (not the individual grade) windows may be increased in size if the instructor finds it appropriate.
Notes: Final score in % will be rounded to the nearest whole number. Assigning + or – grades may be made at instructor’s discretion.)
A: 90-100, B: 80-89, C: 70-79, D: 60-69, Fail (Grade F): 0-59
Grading corrections: Bring any assignment or exam grading correction requests to the instructor within 1 week of receiving the grade, or before the end of the quarter, whichever comes first. After that, your grade will not be adjusted. If you find a mistake in grading, please let the instructor know. Your grade will not be lowered.
There
is no separate grading scale for PhD students, but PhD students will typically
be held to a higher standard.
All project reports, homework assignments, are due at the beginning of class in all required forms (e.g., paper and/or submit on blackboard) on the due date. Changes to a submission’s due dates will be avoided because they are unfair to those students who have organized their time to complete the assigned work. Individual accommodations will be discussed if you have a valid medical excuse.
Project due dates will be set to give ample time for completion of the project and will not be extended save for the unexpected and unlikely major, long-lived catastrophe. Start projects early--last minute computer malfunctions will not be accepted as a reason for delaying a project due date.
Unless otherwise specified by the instructor, only the final exam will be comprehensive, covering material from the entire course. There are no makeups or rescheduling of exams unless you have a plausible reason with appropriate document or verification for absence. Rescheduling of exams must be arranged at least one week in advance. An exam missed without an acceptable excuse will be recorded as a grade of zero (0). Please also be aware that no electronic devices are allowed during the exam.
For Homework assignments, each late submission will incur a 5 points penalty per day. A missed submission without an acceptable excuse will be recorded as a grade of zero (0). No submission will be accepted after 3rd day and will be recorded as a grade of zero (0). There will be no makeup for homework assignments or class activities.
By attending Old Dominion University you have accepted the responsibility to abide by the honor code. If you are uncertain about how the honor code applies to any course activity, you should request clarification from the instructor. The honor pledge is as follows:
"I pledge to support the Honor System of Old Dominion University. I will refrain from any form of academic dishonesty or deception, such as cheating or plagiarism. I am aware that as a member of the academic community, it is my responsibility to turn in all suspected violators of the Honor Code. I will report to a hearing if summoned."
Scholarly
dishonesty, especially plagiarism, will not be tolerated. Plagiarism is defined
as "Failing to credit sources used in a work product to pass off the work
as one's own. Attempting to receive credit for work performed by another,
including papers obtained in whole or in part from individuals or other
sources." Students found to have
engaged in plagiarism will be punished severely, typically earning an automatic
F in the course and being reported to the Office of Student Conduct and Academic
Integrity.
Homework
Assignments Collaboration Clarification: To clarify, your
homework assignment is yours alone
and you are expected to complete each independently. Your solution
should be
written by you without the direct aid or help of anyone else. However,
I
believe that collaboration and team work are important for facilitating
learning, so I encourage you to discuss problems and general problem
approaches
(but not actual solutions) with your classmates. If you do have a chat
with
another student about a problem, you must inform me by writing a note
on your
submission (e.g., Bob pointed me to the relevant section for problem
3). The
basic rule is that no student should explicitly share a solution with
another
student (and thereby circumvent the basic learning process), but it is
okay to
share general approaches, directions, and so on. If you feel like you
have an
issue that needs clarification, feel free to contact me.
In compliance with PL94-142 and more recent federal legislation affirming the rights of disabled individuals, provisions will be made for students with special needs on an individual basis. The student must have been identified as special needs by the university and an appropriate letter must be provided to the course instructor. Provision will be made based upon written guidelines from the University's https://www.odu.edu/educationalaccessibility . All students are expected to fulfill all course requirements.
Students
are encouraged to self-disclose disabilities that have been verified by the Office
of Educational Accessibility by providing Accommodation Letters to their
instructors early in the semester in order to start receiving accommodations.
Accommodations will not be made until the Accommodation Letters are provided to
instructors each semester.