Data Science
Martha Hoopes, Chair
Connell Heady, Academic Department Coordinator
415A Clapp Laboratory
413-538-2162
https://www.mtholyoke.edu/academics/find-your-program/data-science
Overview and Contact Information
The major in data science aims to guide students to be effective, ethical, and judicious users, interpreters, analyzers, and communicators of data and data-related concepts. The major offers students 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 their data science education, students will develop excellent communication skills and the ability to make clear and persuasive arguments framed by logic and supported by data. The data science curriculum reflects the increasingly collaborative and interdisciplinary academic landscape.
See Also
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.
Faculty
This area of study is administered by the Data Science Program Committee:
Maria Gomez, Elizabeth Page Greenawalt Professor of Chemistry
Dylan Shepardson, Robert L. Rooke Associate Professor of Mathematics, On Leave 2023-2024
Benjamin Gebre-Medhin, Assistant Professor of Sociology, On Leave 2023-2024
Marie Ozanne, Clare Boothe Luce Assistant Professorship in Statistics, Teaching Spring Only
Requirements for the Major
A minimum of 40 credits:
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: | ||
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 | |
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
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
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
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 |