Data Science

Undergraduate

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

The Data Science Committee

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:

Course List
Code	Title	Credits
STAT-140	Introduction to the Ideas and Applications of Statistics	4
STAT-242	Intermediate Statistics	4
As a prerequisite for MATH-211:
MATH-102	Calculus II (or above)
MATH-211	Linear Algebra	4
COMSC-151	Introduction to Computational Problem Solving ¹	4
COMSC-205	Data Structures	4
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, 5}		12
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, 5}		8
Total Credits		40

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

On This Page

Required Core Courses for the Data Science Major
Elective Courses for the Data Science Major

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.

Required Core Courses for the Data Science Major

Course List
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

Elective Courses for the Data Science Major

Course List
Code	Title	Credits
Biological Sciences
BIOL-223	Ecology	4
BIOL-234	Biostatistics	4
Chemistry
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
Computer Science
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 Science
DATA-113	Introduction to Data Science	4
DATA-225AR	Topics in Data Science 'Ethics and Artificial Intelligence'	4
DATA-390	Data Science Capstone	4
Economics
ECON-320	Econometrics	4
Entrepreneurship, Orgs & Soc
EOS-299AR	Topic: 'Ethics and Artificial Intelligence'	4
Geography
GEOG-205	Mapping and Spatial Analysis	4
GEOG-210	GIS for the Social Sciences and Humanities	4
GEOG-320	Research with Geospatial Technologies	4
Mathematics
MATH-339PT	Topics in Applied Mathematics: 'Optimization'	4
MATH-339SP	Topics in Applied Mathematics: 'Stochastic Processes'	4
MATH-342	Probability	4
Philosophy
PHIL-180DE	Topics in Applied Philosophy: 'Data Ethics'	4
PHIL-260AR	Topics in Applied Philosophy: 'Ethics and Artificial Intelligence'	4
Sociology
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
Statistics
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

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.

413-538-2162
415A Clapp Laboratory

The Data Science Committee

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