Programming and Database Fundamentals for Data Scientists (EAS 503)

School of Engineering and Applied Sciences, University at Buffalo

Spring Semester 2020

Instructors

Meeting times and locations

  • Zia: Wednesdays, 7.00 PM - 9.50 PM, Furnas 805.

Office Hours

  • Zia Office Hours: Tuesdays, 6:45-8PM, Furnas 805
  • McCall Office Hours: Thursdays 1-3 and 5-7 in Furnas 805.

Prerequisites

None

Course materials

Jupyter Notebooks

Online forum

Piazza Page - https://piazza.com/class/k5yka76g8se3y2

Note

We will use Piazza as our primary mode of communication with the students. Students are strongly encouraged to use Piazza to communicate with the instructors (through private messages) and with rest of the class (through public posts)

Classroom management

UBLearns

Note

We will use UBLearns to distribute grades.

Course Texts (Optional)

  • Python Programming: An Introduction to Computer Science, Zelle [ISBN-10: 1590280288]
  • Hector Garcia-Molina, Jeffrey D. Ullman, Jennifer D. Widom, Database Systems: The Complete Book

Grading

  • Programming Labs (3) - 30%
  • Homeworks (10) - 40%
  • Final Project - 30%

Final Exam

  • NONE

Class schedule

Class Schedule

How to submit homeworks

We will use the Jupyter nbgrader for administering the labs and homeworks.

The server for this class is - https://eas503.ccr.buffalo.edu. More details about the submission will be provided in the class.

Expectations

  • Students are expected to act in a professional manner. A student’s grade may be reduced due to unprofessional or disruptive behavior. Examples include coming to class late, texting (or otherwise using your cell phone) during class, your cell phone ringing during class and/or exams, etc.
  • Homework assignments will be assigned for each module. Homework assignments are due at the beginning of class.
  • Homework and programming assignments will be graded and returned to students.
  • Late submission of assignments will receive a grade of zero.
  • Students are encouraged to discuss assignments and share ideas, but each student must independently write and submit their own solution.
  • Makeup exams will be given in the following circumstances only:
    1. You have a valid and documented reason to miss the exam, and
    2. You contact the instructor prior to the exam.

Accessibility Services and Special Needs

If you have any disability which requires reasonable accommodations to enable you to participate in this course, please contact the Office of Accessibility Resources in 60 Capen Hall, 716-645-2608 and also the instructor of this course during the first week of class. The office will provide you with information and review appropriate arrangements for reasonable accommodations, which can be found on the web at: http://www.buffalo.edu/studentlife/who-we-are/departments/accessibility.html.

Academic Integrity

Academic integrity is a fundamental university value. Through the honest completion of academic work, students sustain the integrity of the university and of themselves while facilitating the university’s imperative for the transmission of knowledge and culture based upon the generation of new and innovative ideas. For more information, please refer to the Graduate Academic Integrity policy.

This course will operate with a zero-tolerance policy regarding cheating and other forms of academic dishonesty. Any act of academic dishonesty will subject the student to penalty, including the high probability of failure of the course (i.e., assignment of a grade of “F”). It is expected that you will behave in an honorable and respectful way as you learn and share ideas. Therefore, recycled papers, work submitted to other courses, and major assistance in preparation of assignments without identifying and acknowledging such assistance are not acceptable. All work for this course must be original for this course. Additionally, you are not allowed to post course homeworks, exams, solutions, etc., on a public forum. Please be familiar with the University and the School policies regarding plagiarism.

Data Science Honor Code

By enrolling into this class, you are implicitly bound to the Data Science Honor Code. It is against the DS honor code to:

  1. Collaborate on homeworks and programming labs
  2. Collaborate or cheat during exams
  3. Submit someone else’s work, including from the internet, as one’s own for any submission
  4. Misuse Piazza forum

You are allowed to:

  1. Have discussions about homeworks and programming labs. Every student should submit own homework with names of students in the discussion group explicitly mentioned.
  2. Collaborate in groups of 2 or 3 for the projects. One submission is required for each group.

Warning

  • Violation of DS honor code and departmental policy will result in an automatic F for the concerned submission
  • Two violations ⇒ fail grade in the course