Attendance Policy

Lectures are in-person and attendance is expected. We don't want or expect anyone to come to class when they're sick, though, so if you're not able to attend in-person you can participate in Prof. Felix's lecture remotely, following the guidelines in the syllabus. Fill out the Remote Attendance Form and access lecture via Canvas > Zoom Meetings.

Lecture recordings will not be available; if you must completely miss lecture, access lecture materials on the course website and attend office hours to ensure you are caught up.

This policy applies to students who have to miss a class or two due to illness, travel, wellness days, etc. If you need to miss more than that due to travel restrictions, long-term illness, etc., please connect with your academic advisor to discuss your options.

Textbook and Resources

Both textbooks below are freely available online. You do not need to read the textbooks ahead of lecture; they're most useful as reference materials or for looking up new examples. Keep them handy when working on the homework or reviewing your lecture notes.

Recommended Textbook	Intro to Python for Computer Science and Data Science. Deitel & Deitel. Pearson, 2019. ISBN: 0135404673. Available free online or purchase (Click on sign in, then choose "Not listed" if prompted to pick a school, and enter your NEU login details).
Python Reference	Think Python: How to Think Like a Computer Scientist. Allen B. Downey. O’Reilly Media, 2015. ISBN: 1491939362. Available free online or purchase.
DS2000 Rubric and Style Guide	Download (PDF)

Software

We'll be using Python 3 in this class. PyCharm Community Edition (https://www.jetbrains.com/pycharm/download) is our official editor for DS2000, which we’ll use to write and run Python code. If you like and use another editor that's totally fine, but we'll use PyCharm in lectures and office hours, and we'll be able to help you out if something goes wrong. Check out the PyCharm Intro video linked on the course calendar!

Gradescope

You'll be added to our Gradescope page: https://www.gradescope.com/courses/801405. If you have any trouble accessing or submitting work on Gradescope, let us know on Piazza and we'll sort it out!

We'll use Gradescope for all homeworks in DS2000.

Piazza

You'll also be added to our Piazza page: https://piazza.com/northeastern/fall2024/ds2000.

Piazza is an extension of our classroom discussion, and we expect everyone to behave accordingly. No disrespect, rudeness, or abuse will be tolerated -- towards fellow students or towards the course staff. Piazza will be disabled if we feel it is being misused.

You may not post your code on Piazza, but you can ask, answer, and discuss different things you've tried, what worked and didn't work, and resources you've found.

We'll also use Piazza to post course announcements, so make sure your email settings are turned on!

Communication

The simplest way to get feedback and help from course staff and from your classmates is via Piazza. All students and course staff are on the DS2000 Piazza, so we hope that everyone joins in the discussion.

Email (f.muzny@northeastern.edu, laneys@northeastern.edu) is the best tool for specific questions or concerns about your experience in class or anything sensitive in nature. During the week, we'll respond within 24 hours, but don't expect a response after 6pm. On the weekends we'll be slower to respond, but if you reach out over a weekend you can expect to hear back Monday morning.

Office hours are the best place for talking through your approach to a homework problem. We're not here to give you answers, of course, but to be your fellow data scientists thinking through a tough problem with you. Expect us to ask more questions than we answer.

You can also ask lecture-related questions directly to Profs. Felix and Laney so we can wrap them into the next lecture if appropriate. Please use this form to do so: https://forms.gle/8nwhqqzotmtjws2i6.

Academic Integrity

You are free to discuss homeworks and ideas with your classmates. You may not share code with classmates, and you may not post code on Piazza.

Searching online and looking for ideas is acceptable, as long as (1) you cite any outside sources that you referenced in a comment in your code, and (2) you do not ask TAs or instructors to help you fix code you found online. We’ll help you work out problems with your code, not someone else’s.

Copying solutions from a classmate or online source is a violation of our academic integrity policy and will result in a 0 on the assignment/exam and a report filed with OSCCR. The university's academic integrity policy discusses actions regarded as violations and consequences for students: https://osccr.sites.northeastern.edu/. Note that “copying” includes any medium that transfers one student’s code to another student, whether it be by showing it to them, telling to them, or by other means.

This includes the code from large language models (ChatGPT, etc.).

A further note on this course's policy to not allow usage of large language models and AI chatbots like ChatGPT: you are here to learn a new skill. This is a difficult thing to do and it can be very enticing to use tools like these. These tools will be far more useful to you in the future if you do everything in your power to learn and practice the fundamentals on your own. You should always be able to explain every part of the code you use and write

Student Services

If you require support during the course due to a disability please ensure that you are already registered with the University’s Disability Access Services, and contact Profs. Laney and Felix to coordinate any support needed during the course.

Title IX makes it clear that violence and harassment based on sex and gender are Civil Rights offenses subject to the same kinds of accountability and the same kinds of support applied to offenses against other protected categories such as race, national origin, etc. If you or someone you know has been harassed or assaulted, you can find the appropriate resources here: Title IX.