Programming for data scientists (Python)

Admissions Requirement: Introduction to computing/programming. Students can fulfill the requirement by taking any one of the following or an equivalent course (instructor's approval is needed): CSCI 1041 - Digital Literacy in a Global Society, CSCI 1611 - A Gentle Introduction to Programming, CSCI 1911 - Foundations of Programming, CSCI 2651 - Python for the Sciences.

Course Restrictions: Restricted to Graduate Students.

Building on students’ programing background, this course delves into Python-specific programming. Basic language constructs are summarized, and then the focus moves to Python-specific sequence types: lists, tuples, strings, dictionaries, and arrays. NumPy, Pandas, Seaborn, and Scikit-learn libraries are used to tackle fundamental tasks of data science: cleaning, munging, aggregating, and visualizing data. With these Python tools, students will analyze time series data and create both linear and multiple linear regression models. Students’ learning will culminate in a final case study project, and if applicable, students are encouraged to use datasets relevant to their workplaces.

Credit: 3