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, 2.00 PM – 3.00 PM, or email me for an appointment
Schedule : Tuesday, Room: DRGS 1117, Time: 4.20 PM – 7.00 PM
Website : http://www.cs.odu.edu/~sampath/courses/f19/cs620
Piazza : https://piazza.com/odu/fall2019/cs620/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. Cross-listed with DASC
600.
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 (Aug 27): Syllabus and
Introductions, Python Workshop
Week 2 (Sep 3): Pandas
Week 3 (Sep 10): NumPy
Week 4 (Sep 17): Data Wrangling
Week 5 (Sep 24): Unstructured and
Semi-Structured Data
Week 6 (Oct 1): NoSQL
Week 7 (Oct 8): Mid-Term Exam
Week 8 (Oct 15): No Class – Fall
Break
Week 9 (Oct 22): Text Data Analysis and
Inference
Week 10 (Oct 29): Machine Learning on Data
Week 11 (Nov 5): Machine Learning on Data
Week 12 (Nov 12): Evaluations
Week 13 (Nov 19): Delivering Results
Week 14 (Nov 26): Recommender Systems +
Final Revision
Week 15 (Dec 3): Final Exam
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 “C620-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.
25% |
Final Exam (Final is comprehensive), Tuesday, December 03
from 5.00 pm to 7.00 PM |
25% |
Mid-term Exam, Tuesday, October 08, from
5.00 pm to 6.30 PM |
25% |
Homework Assignments (5) |
15% |
Data project |
10% |
in-class activities + Attendance |
100% |
Your Total Score for the class |
Final
Exam: The final
exam is comprehensive, closed books and will be held on Tuesday, December 03 from 5.00 pm to 7.00
PM. You may bring one standard 8.5" by 11" piece of paper with
any notes you deem appropriate or significant (front and back).
Midterm Exam: The midterm exam will be held on Tuesday during class time. For both exams, no iPads, iPhones, Blackberries, Android phones/tablets are allowed. Standard calculators are allowed.
Homework: We will have 5 homework assignments, each worth 5% of your overall grade.
Data Project: The data 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 are required to individually work on a dataset of your choosing that is interesting, significant, and relevant to Data Science. The ultimate goal of your data project is to apply the techniques learn in each week of the class towards your dataset (exploration, wrangling, machine learning, visualization). We are going to use Google Colab (Colaboratory) (https://colab.research.google.com/), a free Jupyter notebook environment that requires no setup and runs entirely in the cloud. With Colaboratory you can write and execute code, save and share your analyses, and access powerful computing resources, all for free from your browser.
In Class Activities/Attendance: Attendance in class and participation in the discussion are both important to your success in the course. As one 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).
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.
Due dates will be set to give ample time for completion of the assignments and will not be extended save for the unexpected and unlikely major, long-lived catastrophe. Start projects and homework early--last minute computer malfunctions will not be accepted as a reason for delaying a 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. Having another exam on the same day is not an acceptable excuse for requesting to reschedule 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.