Syllabus

DS2000 (Lecture)

Laney Strange (she/her) - Sections 1, 2, 3

E-mail	laneys@northeastern.edu
Web	https://northeastern.edu/home/laney
OH Link	https://northeastern.zoom.us/my/laney
Office Hours	M 2-4pm, R 9-11am Reserve OH time or just drop in (appointments take priority though!). If the Mon and Thurs office hours get booked quickly, Laney will add an extra hour on Friday afternoon. There are no office hours on university holidays. Click the calendly link above for the most-updated schedule.

Kayla McLaughlin (she/her) - Academic Coordinator

E-mail

k.mclaughlin@northeastern.edu

Class Schedule

DS2000 lectures introduce key concepts and dive into examples and applications. 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 we'll post videos each week that you can use to catch up on any missed material. The DS2000 Piazza is a great place to ask follow-up questions after lecture or when you’re working on homework. You can also ask lecture-related questions directly to Laney so I can wrap them into the next lecture if appropriate. Please use this form to do so: https://forms.gle/8i5vkS5dnVKi3PiF9

About DS2000

Introduces programming for data and information science through case studies in business, sports, education, social science, economics, and the natural world. Presents key concepts in programming, data structures, and data analysis through Python. Integrates the use of data analytics libraries and tools. Surveys techniques for acquiring and programmatically integrating data from different sources. Explains the data analytics pipeline and how to apply programming at each stage. Discusses the programmatic retrieval of data from application programming interfaces (APIs) and from databases. Applies data visualization techniques to summarize and communicate the analysis of data.

DS2000 is designed for beginning programmers. No coding experience, no problem!

The practicum that accompanies the lecture is DS2001. It's an interdisciplinary structure where you gain hands-on experience applying data science techniques and knowledge to specific topical areas. Each DS2001 section is taught by an instructor from Khoury, Bouve, D'Amore-McKim, or CSSH.

DS2000 and DS2001 grades are separate. Your DS2001 instructor will have their own syllabus, assignments, and grading structure. DS2001 assignments typically contextualize what we learn in lecture to a specific topic, but are separate from, and not necessarily related to, the DS2000 homework assignments.

Classroom Environment

Please ask questions, and answer questions! In programming, we seldom get anything right on the first try. We see how an attempt turned out, and we try again. I like our classroom to reflect that approach as well; so please answer a question that's been posed, even if you're not sure of the answer.

To create and preserve a classroom atmosphere that optimizes teaching and learning, all participants share a responsibility in creating a civil and non-disruptive forum for the discussion of ideas.

Students are expected to conduct themselves at all times in a manner that does not disrupt teaching or learning. This class is designed for beginners. If you happen to have some experience with Python, we expect you to be supportive and respectful of your classmates who don't.