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Data Science

Undergraduate

Data science extracts meaningful knowledge from large amounts of complex data by using scientific methods, statistics and computing tools.

Program Overview

The data science major lives at the intersection of many fields. Cross-disciplinary by definition, this major is an exercise in integration, one that brings together technical skills and domain-specific knowledge to create new pathways for learning. The data science major will prepare you to face emerging questions and challenges in the communication of data concepts and outcomes. Offering rigorous training in statistics and computer science, the major emphasizes how to be an effective, ethical and judicious consumer of data.

The major offers a foundational understanding of the data generating process, the appropriate and efficient translation of analytic strategies to specific data settings, the potential biases arising from missing data or data collection, the means for drawing accurate conclusions, and the techniques and principles of integrity in data visualization and communication. As part of your data science education, you will develop excellent communication skills and the ability to make clear and persuasive arguments framed by logic and supported by data.

The curriculum is flexible and innovative, serving a diverse backgrounds and disciplinary interests, and deep enough to accommodate anyone who wants ultimately to pursue advanced study in statistics and computer science. The Data Science curriculum reflects the increasingly collaborative and interdisciplinary academic landscape.

Courses and Requirements

Culminating in an integrative capstone and data ethics course, the data science curriculum includes required coursework in mathematics and statistics with a choice of domain area, such as English, economics or chemistry.

Learning Goals

  • Apply core concepts of statistics, computing, and domain knowledge to extract insight from data sets.
  • Understand the social and ethical issues surrounding data collection, analysis and use.
  • Be able to communicate in multiple modalities the results of large scale data analysis.

Requirements for the Major

A minimum of 40 credits:

STAT-140Introduction to the Ideas and Applications of Statistics4
STAT-242Intermediate Statistics4
As a prerequisite for MATH-211:
MATH-102
Calculus II (or above)
MATH-211Linear Algebra4
COMSC-151Introduction to Computational Problem Solving 14
COMSC-205Data Structures4
12 credits at the 300 level from at least two departments or programs and chosen from the approved list of elective courses for Data Science. One course must be either: 2, 3, 4, 512
COMSC-335
Machine Learning 6, 7
or STAT-340
 Applied Regression Methods
8 additional credits chosen from the approved list of elective courses for Data Science 2, 3, 4, 58
Total Credits40
1

Any COMSC-151 offering, for example, COMSC-151CP, COMSC-151DS, or COMSC-151HC.

2

Students who do not elect both COMSC-335 and STAT-340 will need to choose two other 300-level courses from this list, one of which is from a department other than their COMSC-335/STAT-340 choice.

3

Many elective courses require prerequisites. Students are encouraged to plan their elective courses early in order to ensure that they meet the requirements to access chosen courses.

4

Students are strongly encouraged to take an elective course in ethics.

5

Other courses that focus on ethics, cover data analytic methods, or involve an independent project with data can be substituted with approval of the Data Science Program Committee.

6

Students intending to attend graduate school in data science are advised to take both of these courses.

7

COMSC-335 Machine Learning requires MATH-232 as a prerequisite.

Additional Specifications

  • Students who declare a Data Science major automatically fulfill the College's "outside the major" requirement.

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

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
Applies to requirement(s): Humanities
Other Attribute(s): Writing-Intensive
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.

Required Core Courses for the Data Science Major

Chemistry
CHEM-348Using Data Science to Find Hidden Chemical Rules4
Computer Science
COMSC-151CPIntroduction to Computational Problem Solving: 'Computing Principles'4
COMSC-151DSIntroduction to Computational Problem Solving: 'Big Data'4
COMSC-151HCIntroduction to Computational Problem Solving: 'Humanities Computing'4
COMSC-151SGIntroduction to Computational Problem Solving: 'Computing for Social Good'4
COMSC-205Data Structures4
COMSC-335Machine Learning4
Mathematics
MATH-211Linear Algebra4
Statistics
STAT-140Introduction to the Ideas and Applications of Statistics4
STAT-242Intermediate Statistics4
STAT-340Applied Regression Methods4

Elective Courses for the Data Science Major

Biological Sciences
BIOL-223Ecology4
BIOL-234Biostatistics4
Chemistry
CHEM-291Scientific Illustration and Data Visualization4
CHEM-328From Lilliput to Brobdingnag: Bridging the Scales Between Science and Engineering4
CHEM-348Using Data Science to Find Hidden Chemical Rules4
Computer Science
COMSC-133DVData Visualization: Design and Perception4
COMSC-312Algorithms4
COMSC-334Artificial Intelligence4
COMSC-335Machine Learning4
COMSC-341NLTopics: 'Natural Language Processing'4
COMSC-341TETopics: 'Text Technologies for Data Science'4
Data Science
DATA-113Introduction to Data Science4
DATA-225ARTopics in Data Science 'Ethics and Artificial Intelligence'4
DATA-390Data Science Capstone4
Economics
ECON-320Econometrics4
Geography
GEOG-205Mapping and Spatial Analysis4
GEOG-210GIS for the Social Sciences and Humanities4
GEOG-320Research with Geospatial Technologies4
Mathematics
MATH-339PTTopics in Applied Mathematics: 'Optimization'4
MATH-339SPTopics in Applied Mathematics: 'Stochastic Processes'4
MATH-342Probability4
Philosophy
PHIL-180DETopics in Applied Philosophy: 'Data Ethics'4
PHIL-260ARTopics in Applied Philosophy: 'Ethics and Artificial Intelligence'4
Sociology
SOCI-216TXSpecial Topics in Sociology: 'Text as Data I: From Qualitative to Quantitative Text Analysis'4
SOCI-316TXSpecial Topics in Sociology: 'Text as Data II: Computational Text Analysis for the Social Sciences'4
Statistics
STAT-244MPIntermediate Topics in Statistics: 'Survey Sampling'4
STAT-244NFIntermediate Topics in Statistics: 'Infectious Disease Modeling'4
STAT-331Design of Experiments4
STAT-340Applied Regression Methods4
STAT-343Mathematical Statistics4

Contact Us

The Data Science Committee

Cross-disciplinary by definition, Data Science prepares students to face emerging questions and challenges in the communication of data concepts and outcomes.

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