|Lectures||Sec 1: TF 9:50-10:55am. WVG 102.
Sec 2: TF 1:35-2:40pm. WVG 102.
Sec 3: TF 3:25-4:30pm. WVG 104.
Sec 4: T 11:45am-1:25pm, R 2:50-4:30pm. Online only.
DS2001-11. T 1:35-3:15pm
DS2001-12. T 3:25-5:05pm
DS2001-13. W 11:45am-1:25pm
DS2001-17. W 11:45am-1:25pm
DS2001-14. W 2:50-4:30pm
DS2001-15. W 2:50-4:30pm
DS2001-16. W 4:40-6:20pm.
DS2001-18. R 9:50-11:30am
DS2001-19. R 11:45am-1:25pm
DS2001-10. R 11:45am-1:25pm.
|DS2000 Syllabus:||Download (PDF)|
|DS2000 Grading Rubric:||Download (PDF)|
|DS2000 Style Guide:||Download (PDF)|
Lectures will be live and will not be recorded. But we'll post short videos to watch before each lecture. It works like this:
|Name||Office Hours||Name||Office Hours|
|Emma Sommers, TA Lead (she/her)||Dachuan Zhang, TA Lead (he/him)||Fri 6-8pm|
|David Pogrebitskiy (he/him)||Weds 6-8pm||Jacob Kulik (he/him)||Weds 6-8pm|
|Maddy Hill (she/her)||Weds 6-8pm||Gio Ong||Mon 6-8pm, Weds 5-7pm|
|Isha Arora (she/her)||Wed 5-7pm, Fri 4-6pm||Melek Abubaker||Mon 6-8pm, Tue 6-8pm|
|Sarah Costa (she/her)||Thu 6-8pm||Devarsh Hemantbhai Patel (he/him)||Thu 6-8pm, Fri 6-8pm|
|Daniel Xu (he/him)||Tue 6-8pm||Will Hanvey (he/him)||Mon 6-8pm|
|Joann Rachel Jacob (she/her)||Weds 6-8pm, Fri 4-6pm||Ian Beer (he/him)||Tue 6-8pm|
|Saachi Chandrashekhar (she/her)||Thu 4-8pm||Arian Gokhale (he/him)||Thu 4-6pm|
|Hamsini Malli (she/her)||Tue 4-6pm||Max Rizzuto (he/him)||Weds 5-7pm|
Michael Devine (he/him)
|Fri 4-8pm||Claudia Levi||Thu 4-6pm|
Rachel Cassway (she/her)
|Thu 6-8pm||Steven Boehm (he/him)||Fri 4-6pm|
Meghaana Tummapudi (she/her)
|Thu 6-8pm, Fri 4-8pm||Siddarth Sathyanarayanan||Tue 6-8pm|
Maya Zeldin (she/her)
|Thu 4-6pm||Shruti Kedharnath (she/her)||Wed 6-8pm|
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.
Beginning programmers are welcome; we don't assume any previous knowledge and we'll start from the very beginning.
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.
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.