Applied Data Science (ADS) Tooling: Data Management, Open Source Packages and Infrastructure

Catalog description

This is an introductory course to provide practical knowledge and resources in Applied Data Sciences(ADS) Tools that can be applied to different areas where code is developed for data analysis and modeling. This course focuses on practical aspects of Applied Data Science to complement the traditional ADS curriculum. When new code, pipelines, algorithms and models are developed, they need to be implemented, scalable, reusable, understandable and efficient. Most of those aspects are not traditionally covered in core ADS classes. This course proposes to fill the gap in some important areas which includes creating and publishing code packages and R and Python, agile software development, coding good practices and documentation, version control (git), Data Management, foundations of infrastructure and LLMs as code assistants. This course represents an opportunity for students to learn useful applied data science tools to boost their careers and provide practical experience in developing data science solutions and applications. Graduate students will work on a hands-on open source project which could cover different aspects of the course depending on their interest. One example is making a R/Python package based on their research or developing an automated data analysis pipeline with efficient and well documented code. ADS Tooling is an introductory course that provides foundational and practical concepts and implementations of data science technologies useful to any domain, and therefore the course is open to students in any school. Students should have experience in R or Python to enroll in this course. Offered as DSCI 355 and DSCI 455 . Prereq: ( DSCI 351 or DSCI 351M or DSCI 451 ) and ( DSCI 353 or DSCI 353M or DSCI 453 ) or Requisites Not Met permission.