For the remainder of the semester, we'll post a couple of short videos each week, along with sample code and a handout or two (everything will be on the schedule page). Watch the videos, read the handout, and then pop into the remote meetings linked below if you have questions. Same process for the remaining practicum sessions, too!
|Lecture||Sec 1: TF 9:50-10:55am. https://northeastern.zoom.us/j/146566076
Sec 2: TF 1:35-2:40pm. https://northeastern.zoom.us/j/734130129
CS Practicum 1. R 9:50-11:30am. https://zoom.us/j/2428717050
Science Practicum 1. W 11:45am-1:25pm. https://zoom.us/j/9990797060
Health Practicum. R 2:50-4:30pm. https://zoom.us/j/9488971230
Business Practicum 1. W 11:45am-1:25pm. https://zoom.us/j/5297485321
|Office Hours||R 5-6pm|
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.
|Lectures||will focus on developing a conceptual understanding of programming and algorithmic thinking.|
|Homework||will apply conceptual knowledge via problems and Python implementation.|
|Practicum||will apply programming through case studies.|
|Project||will analyze & visualize a dataset programmatically.|
The final grade for this course will be weighted as follows.
At the conclusion of the class you will submit your Python code along with any datasets you used in the project. Additionally, you will present your work during the last week of class.
In my classroom, please ask questions, and answer questions! In computer science, 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. Your comments to others should be constructive and free from harassing statements.
When you come to class, I ask that you be fully present. No phones are permitted in the classroom. If you use a laptop, use it only to take notes. Please be respectful of your fellow students and me by participating attentively and non-disruptively.