7 courses with the subject DSC, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
DSC 130Introduction to Data Science3.00
This course is an introduction to data science and the practice of working through the entire data life cycle to inform decisions across various domains. Using real-world data, students will learn the basics of commonly deployed analytical methods and tools including data wrangling, cleaning, sampling, analysis, and visualization. Students will apply strategies to generate knowledge and use data to explore social problems and ethical implications. Upon completion, students will have gained a foundational understanding and related competencies in many facets of effective use of data. LSP: IG
DSC 131Introduction to Programming for Data Science3.00
Th course is an introduction to basic programming, computational thinking and extraction for data analysis. Using beginner-friendly methods and programs that manipulate different types of data, students will leverage tools and libraries for data programming. Students will also learn to efficiently extract and curate relevant datasets from multiple data storage sources.
This course introduces key concepts in data exploration, data visualization, and visual storytelling. Topics include concepts and methods used in graphical representation of data such as exploration and ethical reporting of data, visual reasoning, and basic interactive design methods. Upon completion, students should be able to effectively use graphical tools and presentation skills to communicate insights about data with demonstrated understanding of social implications and context.
This course provides an introduction to responsible artificial intelligence (AI) and explains where bias in AI systems comes from, who it impacts, and how to mitigate it. This course will explore fundamental global challenges and concerns, including accountability, equity, fairness, and transparency in AI systems. Upon completion, students will know the fundamentals and principles of responsible artificial intelligence.
This course explores advanced data science methods, machine learning technologies, and design thinking practices. Students will learn to apply these techniques to address key industry problems. Several open- source technologies including python and Jupyter notebooks, and the artificial intelligence platform IBM Watson will be used. Students will learn about different roles in data science teams and the course will prepare those who want to start a career in Data Science. Students that successfully complete course readings, labs and the final exam can earn the IBM Data Science Practitioner digital badge. or BUS 233 or MTH 438. 108
DSC 130 or CSC 235 or MTH 136 or MTH 231 or PSY 233
DSC 435Data Science Practicum3.00
In this course, students will engage directly in the use of knowledge from data science and intradisciplinary courses in an industrial, community or research setting. Faculty sponsored practicums require 40 hours on-site or in the classroom per credit hour. Accordingly, the student must have 120 hours for this 3-credit-hour course.
In this course, special topics covering new advances in data science are selected and offered by the Department. The topic covered by the course may change from one semester to another. May be repeated when the topic varies.
