Data Science (DATA)
DATA-113 Introduction to Data Science
Fall and Spring. Credits: 4
Data scientists answer questions with scientific and social relevance using statistical theory and computation. We will discuss elementary topics in statistics and learn how to write code (in Python) to visualize data and perform simulations. We will use these tools to answer questions about real data sets. We will also explore ethical issues faced by data scientists today.
Applies to requirement(s): Math Sciences
K. Mulder, L. Tupper
DATA-225 Topics in Data Science:
DATA-225AR Topics in Data Science 'Ethics and Artificial Intelligence'
Spring. Credits: 4
Artificially intelligent technologies are prominent features of modern life -- as are ethical concerns about their programming and use. In this class we will use the tools of philosophy to explore and critically evaluate ethical issues raised by current and future AI technologies. Topics may include issues of privacy and transparency in online data collection, concerns about social justice in the use of algorithms in areas like hiring and criminal justice, and the goals of developing general versus special purpose AI. We will also look at ethics for AI: the nature of AI 'minds,' the possibility of creating more ethical AI systems, and when and if AIs themselves might deserve moral rights.
Crosslisted as: PHIL-260AR, EOS-299AR
Applies to requirement(s): Humanities
L. Sizer
DATA-295 Independent Study
Fall and Spring. Credits: 1 - 4
The department
Instructor permission required.
DATA-390 Data Science Capstone
Fall and Spring. Credits: 4
The Capstone is a research seminar that brings together the three pillars of the Data Science curriculum. The course will start with common readings about research projects across a range of disciplines, including readings that address issues of ethics involved with the collection, treatment, and analysis of data. Concurrently, each student will develop an individual research topic and identify relevant data resources. The remainder of the term will be dedicated to exploring these topics through extensive data analysis, visualization, and interpretation, leading to a final report with complete results and a presentation.
Applies to requirement(s): Math Sciences
K. Mulder
Prereq: COMSC-205 and STAT-340. STAT-340 may be taken concurrently (contact instructor for permission).
DATA-395 Independent Study
Fall and Spring. Credits: 1 - 8
The department
Instructor permission required.
DATA-395P Independent Study w/Practicum
Fall and Spring. Credits: 1 - 8
The department
Instructor permission required.
Related Courses
Code | Title | Credits |
---|---|---|
Chemistry | ||
CHEM-348 | Using Data Science to Find Hidden Chemical Rules | 4 |
Computer Science | ||
COMSC-151CP | Introduction to Computational Problem Solving: 'Computing Principles' | 4 |
COMSC-151DS | Introduction to Computational Problem Solving: 'Big Data' | 4 |
COMSC-151HC | Introduction to Computational Problem Solving: 'Humanities Computing' | 4 |
COMSC-151SG | Introduction to Computational Problem Solving: 'Computing for Social Good' | 4 |
COMSC-205 | Data Structures | 4 |
COMSC-335 | Machine Learning | 4 |
Mathematics | ||
MATH-211 | Linear Algebra | 4 |
Statistics | ||
STAT-140 | Introduction to the Ideas and Applications of Statistics | 4 |
STAT-242 | Intermediate Statistics | 4 |
STAT-340 | Applied Regression Methods | 4 |