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, 4.00 PM – 5.00 PM (Via Zoom), or email me for an appointment
PLE : http://ple.odu.edu/courses/202110/cs620
(Note
that the course content is offered via ODU PLE)
Piazza : https://piazza.com/odu/fall2021/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
coding in Python and its associated
libraries, and knowledge in linear algebra and
statistics.
Data
science is an interdisciplinary blend of the analytical, computational, and
statistical skills necessary to extract knowledge from large and complex sets
of data. The proliferation of such data has led to an acute shortage of
students with data science skills in the local, national, and global economy.
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 the PLE modules.
List of optional but recommended materials. You may find some of these optional textbooks helpful, though none are required:
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 contact me, since only you
can properly communicate your situation to me.
Structure: Course is divided into two units with each unit having several modules. Unit-I is the programming unit that covers python, and its associated libraries required for data science activities. Unit-II is the core unit that covers data science concepts and techniques.
This is a hands-on course with a number of programming activities, assignments, and a final data project. Participation in discussion forum (piazza) is mandatory and will also be graded. See discussion participation section for more details. The course project is a major component of this course, which will enable students to apply their knowledge acquired in the course to develop and implement a data science application on one or more of the technologies covered in Unit-I and II.
Duration: Unit-1 is covered in 7 weeks, Unit-II is covered in 7 weeks. The course project overlaps with Unit-I and II and is spread over 14 weeks. See the topics below for more information.
Topics: The tentative topics are as follows.
Week 1: Syllabus and Introductions,
Week 2: Python
Week 3: Pandas
Week 4: NumPy
Week 5: Data Wrangling
Week 6: Unstructured and Semi-Structured Data
Week 7: NoSQL
Week 8: Weka
Week 9: Text Data Analysis and Inference
Week 10: Pattern Mining
Week 11: Machine Learning Classification
Week 12: Machine Learning Clustering
Week 13: Evaluations
Week 14: Delivering Results
Week 15: Written Exam
Module Activity:
Online meeting with instructor:
It is extremely important for you
to be engaged in the course; otherwise, you will wonder what happened to your
tuition dollars. So, I encourage you to setup online meetings, ask questions and
actively participate at the piazza discussion forum.
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.
|
40% |
Data Project |
|
20% |
Final Exam |
|
25% |
Homework Assignments |
|
15% |
Activities (10%) + Discussion Forum Interactions (5%) |
|
100% |
Your total score fo the course |
Final Exam: Final examination will be a comprehensive (covering all the modules), online (delivered via Blackboard) exam and will be scheduled during the last week of the class. On the week before written exam, I will post a study guide that will help students to prepare for the written examination.
Homework: We will have several homework assignments, worth 25% 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 project section. For the project, you are required to (individually or team of 2-3 members) 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). (see Project tab for details)
Class Activities/Discussion
Forum interactions: Class
activities 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.
Final course grades are based on the overall average. Overall class grade (not the individual grade) windows may be increased in size if the instructor finds it appropriate.
The grading scale is as follows:
| % | letter grade | |
| 94-100 | A | |
| 90-93 | A- | |
| 87-89 | B+ | |
| 84-86 | B | |
| 80-83 | B- | |
| 78-79 | C+ | |
| 74-77 | C | |
| 70-73 | C- | |
| 0-69 | F |
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.
Attendance: Since this is an on-line course, there is no mandatory attendance policy. However, students are expected to actively participate in the piazza discussions, class activities, homework submissions, and Google Colab project writing. Each of these components is graded and counted towards the final grade.
Online Classroom Conduct (Netiquette): Students are expected to follow good Netiquette rules. Netiquette is the accepted behavior for online participation. The following is a list of general guidelines for this course:Select the link to find more information on Netiquette.
Tests, Make-ups, and Late Policies: 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. 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.
Unless otherwise specified by the instructor, only the written exam will be comprehensive, covering material from the entire course. There are no makeups or rescheduling of exam unless you have a plausible reason with appropriate document or verification. Rescheduling of exams must be arranged at least 3 days in advance. An exam missed without an acceptable excuse will be recorded as a grade of zero (0).
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.
Withdrawal: A syllabus
constitutes an agreement between the student and the course instructor about
course requirements. Participation in this course indicates your acceptance of
its teaching focus, requirements, and policies. Please review the syllabus and
the course requirements as soon as possible. If you believe that the nature of
this course does not meet your interests, needs or expectations, if you are not
prepared for the amount of work involved - or if you anticipate that the class
meetings, assignment deadlines or abiding by the course policies will
constitute an unacceptable hardship for you - you should drop the class by the
drop/add deadline, which is located in the ODU Schedule of Classes. For more
information, please visit the Office of the Registrar.
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. For additional information visit the Office of Educational
Accessibility online or at 1525 Webb Center.