Sec 1: TF 11:45am - 12:50pm. Online only. Rachlin.
Sec 2: TF 9:50am - 10:55am. RI 236. Muzny.
Sec 3: TF 1:35pm - 2:40pm. RI 236. Muzny.
Sec 4: TF 3:25pm - 4:30pm. CH 103. Muzny.
|DS2000 Syllabus:||Download (PDF)|
|DS2000 Grading Rubric:||Download (PDF)|
|DS2000 Style Guide:||Download (PDF)|
|Office Hours||Some Mondays, all Thursdays AM and early PM|
|OH Link||Reserve OH time|
All information about TAs and their office hours can be found on the office hours/TAs page.
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.
New 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 read the syllabus for complete information on classroom environment and expectations.
In summary, we design our classes to be interactive. 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. We like our classrooms 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 respectful 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.
This is the website to find everything on. Please bookmark it. We will always link assignments from the schedule page to where you'll need to turn them in.
We use Canvas for its grade-book functionality. About a week after your grades have been posted on Gradescope, we'll post them to Canvas. On Canvas, you'll find links to this website and turn-in links to your assignments on Canvas.