Course Advice
Students Considering a Major in Data Science
Data science is new and evolving; there are many important combinations of theoretical, applied, and field-specific knowledge that may provide a foundation for future work. If you are interested in a data science major, we recommend that you work with your advisor to choose a set of related courses that reflect your interests and priorities from the list of electives. Course combinations that focus on individual topics, disciplines, or domains are strongly recommended. We also strongly recommend substantial engagement with issues of ethics, which could be in one focused course or across multiple courses.
Students Considering Graduate School or a Career as a Data Scientist:
While there are many fields for which the combination of data analysis and computational tools may be valuable, we have particular recommendations for students seeking a future as a data scientist. We strongly recommend that you take both COMSC-335 Machine Learning and STAT-340 Applied Regression Methods. Ideally, at least one course should involve an extended project requiring the analysis of data. We also recommend that you contextualize your data science preparation in the content of a domain or area of study that is theoretically and empirically cohesive.
Course Offerings
DATA-113 Introduction to Data Science
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.
DATA-225 Topics in Data Science:
DATA-225AR Topics in Data Science 'Ethics and Artificial Intelligence'
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.
DATA-295 Independent Study
DATA-390 Data Science Capstone
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.
DATA-395 Independent Study
DATA-395P Independent Study w/Practicum
Required Core Courses for the Data Science Major
Course List
Code |
Title |
Credits |
---|
CHEM-348 | Using Data Science to Find Hidden Chemical Rules | 4 |
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 |
MATH-211 | Linear Algebra | 4 |
STAT-140 | Introduction to the Ideas and Applications of Statistics | 4 |
STAT-242 | Intermediate Statistics | 4 |
STAT-340 | Applied Regression Methods | 4 |
Elective Courses for the Data Science Major
Course List
Code |
Title |
Credits |
---|
BIOL-223 | Ecology | 4 |
BIOL-234 | Biostatistics | 4 |
CHEM-291 | Scientific Illustration and Data Visualization | 4 |
CHEM-328 | From Lilliput to Brobdingnag: Bridging the Scales Between Science and Engineering | 4 |
CHEM-348 | Using Data Science to Find Hidden Chemical Rules | 4 |
COMSC-133DV | Data Visualization: Design and Perception | 4 |
COMSC-312 | Algorithms | 4 |
COMSC-334 | Artificial Intelligence | 4 |
COMSC-335 | Machine Learning | 4 |
COMSC-341NL | Topics: 'Natural Language Processing' | 4 |
COMSC-341TE | Topics: 'Text Technologies for Data Science' | 4 |
DATA-113 | Introduction to Data Science | 4 |
DATA-225AR | Topics in Data Science 'Ethics and Artificial Intelligence' | 4 |
DATA-390 | Data Science Capstone | 4 |
ECON-320 | Econometrics | 4 |
EOS-299AR | Topic: 'Ethics and Artificial Intelligence' | 4 |
GEOG-205 | Mapping and Spatial Analysis | 4 |
GEOG-210 | GIS for the Social Sciences and Humanities | 4 |
GEOG-320 | Research with Geospatial Technologies | 4 |
MATH-339PT | Topics in Applied Mathematics: 'Optimization' | 4 |
MATH-339SP | Topics in Applied Mathematics: 'Stochastic Processes' | 4 |
MATH-342 | Probability | 4 |
PHIL-180DE | Topics in Applied Philosophy: 'Data Ethics' | 4 |
PHIL-260AR | Topics in Applied Philosophy: 'Ethics and Artificial Intelligence' | 4 |
SOCI-216TX | Special Topics in Sociology: 'Text as Data I: From Qualitative to Quantitative Text Analysis' | 4 |
SOCI-316TX | Special Topics in Sociology: 'Text as Data II: Computational Text Analysis for the Social Sciences' | 4 |
STAT-244MP | Intermediate Topics in Statistics: 'Survey Sampling' | 4 |
STAT-244NF | Intermediate Topics in Statistics: 'Infectious Disease Modeling' | 4 |
STAT-331 | Design of Experiments | 4 |
STAT-340 | Applied Regression Methods | 4 |
STAT-343 | Mathematical Statistics | 4 |