Course Detail
Units:
3.0
Course Components:
Lecture
Enrollment Information
Enrollment Requirement:
Prerequisites: 'B-' or better in (IS 6482 OR IS 6489) AND Masters status in the School of Business
Description
Successful corporations can utilize data science techniques to help drive business decision making by analyzing datasets of varying sizes. In this course, a hands-on practitioner's approach is taken to learning the fundamental knowledge, techniques and tools required for leading data science teams and analyzing big data. This course will utilize popular open source technologies and libraries in use today to learn how to collect, pre-process and visualize data, as well as build and test models for inference and prediction. We will examine the unique challenges posed by big data and complex models, and learn how to address them using distributed computing frameworks such as Dask, Hadoop and Spark. The course is taught in Python, and will offer a bootcamp in the first few weeks to help everyone get comfortable with the language.