Jul 17, 2024  
2023-2024 Scripps Catalog 

Data Science

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Associate Professor Winston Ou
Associate Professor Michael Spezio

Data science (DS) is a growing interdisciplinary set of analytical and inferential approaches that exerts influence in government, industry, law, medicine, the academy, and non-governmental organizations. DS seeks to construct or extract information and knowledge from large repositories of data and then to communicate that knowledge and any discoveries made using that knowledge for the purposes of implementing policies in scientific, economic, environmental, educational, legal, or medical domains. Most of the time, the reasons advanced to motivate the development of data science technologies include benefits to human and more than- human life within the broad structure of liberal democracy. Examples of data science technologies include applications in large-scale analysis of textual corpora; spatial information analysis using Geographic Information System (GIS) mapping; deep learning; chemical identification; biological systems modeling; bioinformatics; prediction of financial markets; shared personal e-vehicles such as scooters, bikes, and cars; political advocacy and online coalitions; public transportation monitoring; solar energy network monitoring; ecosystem and climate monitoring and prediction; personal location monitoring; autonomous vehicles; social media and social media monitoring; online retail and targeted advertisement; autonomous weapons and the war cloud; direct-to consumer genetic testing; digital identification via facial, postural, or movement data; medical discovery; policing; sentencing and probation decisions; and private and governmental security.

DS consists of five core areas: 1) domain knowledge/expertise; 2) domain-defined data and domain-informed data management and handling and cleaning; 3) inferential probabilistic models; 4) coding and algorithms; and 5) applied ethics and justice reasoning.

The DS Minor at Scripps College is designed to support students in existing Majors who have interests in developing computational capability, data skills, inferential reasoning from large repositories of data, and ethical reasoning in assessing the design, implementation, and auditing of data science technologies. The DS Minor opens up an increased range of research resources that serves students’ interests and inquiries. The DS Minor also aids students’ future applications for postgraduate education and/or employment in various sectors in society.

Learning Outcomes

Data Science Minor Goals and Objectives

Goals are broad statements what the program wants to accomplish.

1. Students gain in computational capability.
2. Students obtain competency in data science sufficient for them to incorporate data science methodologies into their undergraduate work and Senior Thesis.
3. Students learn to assess the quality of data.
4. Students gain awareness of and confidence and competency in the applied use of data science tools, including two or more of the following: logistic regression, principal component analysis, independent component analysis, pattern recognition and clustering, supervised machine learning and deep learning, autoencoders, and reinforcement learning.
5. Students learn to understand and interpret different measures of bias in existing data.
6. Students gain awareness of and experience in ethical reasoning about data science technologies.
7. Students learn and practice effective and transparent communication of data science in written, verbal, and graphical formats accessible both to specialists and to the general public.

Data Science Minor Student Learning Outcomes

Outcomes describe specific knowledge, abilities, values, and attitudes students should demonstrate.

SLO1: Students will demonstrate intermediate to advanced competency in Python, R, Matlab, Maple, C++, or other advanced computer programming language used in data science.
SLO2: Students will demonstrate a working knowledge of Structured Query Language (SQL). SLO3: Students will demonstrate competence in probabilistic reasoning about data and its relationships to goal-directed, domain-specific inquiry.
SLO4: Students will demonstrate competence in at least two of the following data science methods: logistic regression, principal component analysis, independent component analysis, pattern recognition and clustering, supervised machine learning and deep learning, autoencoders, and reinforcement learning.
SLO5: Students will demonstrate understanding of how to apply at least two ethical systems to address challenges to ethics and justice in data science, such as racial/ethnic bias, gender bias, threats to privacy, threats to autonomy, and threats from mass misinformation/propaganda.
SLO6: Students will demonstrate the ability to read, understand, summarize, and critique written work about data science in the media, in research publications, and in popularized texts.
SLO7: Students will demonstrate written, verbal, and graphical communication competency in data science.



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