From the traditional STEM discipline to broader pathways like esports, healthcare, English, political, and environmental sciences, the data science program offers students access to endless opportunities.
Data science is a fundamentally interdisciplinary field that leverages the large quantities of data and computational resources that have become available over the last few decades and allows questions to be answered in a broad range of fields.
The Data Science Program is intended to reach beyond the STEM disciplines and cross our institution’s and program boundaries. If you are pre-med, data science can be used in projects involving medical imaging, epidemics and disease spread, pharmacology, and healthcare analytics. If your passions lie in political or environmental science, you can leverage data to help make a difference with data-backed policies, research, and decision-making. In the arts, data science and AI can be used to discover fraud or to investigate and generate new artistic products. If you are a video gamer or part of our Esports program, you can learn how AI is used to improve the gaming experience.
Although data science uses tools from mathematics, statistics, and computer science, the knowledge gained can also be used in areas that are not traditionally data-focused. If you are late in deciding on a STEM career, have never taken pre-calculus or trigonometry, or have no fluency with computer programs, you have an opportunity for success in the Data Science Program where you will be challenged and supported.
While in the program, you will acquire skills such as data cleaning, visualization, statistical modeling, and machine learning. Such skills are essential in the data science field. These technical skills must be complemented by non-technical skills, such as critical thinking, effective communication, and creative problem-solving. You will learn to ask meaningful questions and find solutions that will impact your career and your community. You will participate in a data science capstone project that is tailored to both an area of interest and a specific job opportunity. Completing the project will provide you with a start-to-finish data science project that can be shown to employers in your field of interest.
Data science is a steadily growing field, with industries seeking qualified individuals with strong data, communication, problem-solving, and leadership skills. The program will also provide you with experience in Python, R, and SQL; three of the top programming skills required by many employers.
Career pathways are plentiful, including accounting, financial technology, business and marketing analytics, healthcare analytics, environmental analytics, data engineering, data visualization, and more. Your knowledge and experience gained in the Data Science Program will help your resume stand out when applying for jobs in your field.
Joanna Bieri (Economics – full-time – program director)
Nathaniel Cline (Economics – full-time)
Nicholas Reksten (Economics – full-time)
Cheyne Murray (ITS – part-time instructor)
The Bachelor of Arts in Data Science will consist of 40 credits in which students will explore courses to satisfy the key competencies as outlined by the American Statistical Association's Curriculum Guidelines for Programs in Data Science.
We note that courses below with the DATA alpha are courses that we propose to develop as we teach into the major. Any course beyond the DATA alpha already exist in our current curriculum.
Introduction to Statistics (4 Credits) – choose one (CST/MF)
MATH 111 Elementary Statistics with Applications (QRF) - Descriptive and inferential statistics for students from diverse fields. Distribution, correlation, probability, hypothesis testing, use of tables, and examination of the misuse of statistics and relation of statistics to vital aspects of life. Computer packages used as tools throughout the course. Prerequisite: MATHEMATICS PLACEMENT AT MATH-100 OR MATH-101
POLI 202 Statistical Analysis and Mapping of Social Science Data (QRF) - Principles of hypothesis development and testing, strategies for making controlled comparisons, principles of statistical inference, and tests of statistical significance. Development and testing of important research questions using such prominent data sets as the General Social Survey and the National Election Series. Numeric and Evaluation grading only. Prerequisite: None
PSYC 250 Statistical Methods (QRF) - Introduction to the use of descriptive and inferential statistics in the collection of data and the interpretation of research in psychology and education. Prerequisite: PSYC 100
* We also accept AP Statics
GIS/DATA 167 Introduction to Programming in Python (QRE) – Introduction to programming using the Python language. Data structures, conditionals, loops, variables, boolean expressions, conditional statements, functions and input/output processing. Emphasis on developing algorithms for solving real world problems. Exploration of useful Python packages for mathematics, visualization, statistics, and data science. Prerequisite: None
CS 110 Introduction to Programming (QRE) - Introduction to problem-solving methods and algorithm development through the use of computer programming in the Java language. Emphasis on data and algorithm representation. Topics include declarations, arrays, strings, expressions and statements, control structures, functions, and input/output processing. Prerequisite: PLACEMENT INTO MATH-118 OR HIGHER
* We also accept AP Computer Science
DATA 100 Mathematics for Data Science – Introduction to applied mathematical techniques that form the foundation of the study of data science. Topics include probability, linear algebra, differential calculus, and optimization techniques. Prerequisite: None
** Students who take Calculus plus a course in Probability and Linear algebra do not also need to take Mathematics for Data Science. For example, Math, Physics, or Computer Science majors who have already taken Linear Algebra would be waived from this requirement.
DATA 101 – Introduction to Data Science (IMLI) – Introduction to of the full data science life cycle, using either R or Python with a focus on question formulation, data collection and cleaning, exploratory data analysis, data visualization, prediction and communication of results. This will include basic regression, classification, dimensionality reduction, and clustering techniques. Emphasis on reproducible results and being a good data custodian, eg. CARE and FAIR data principles. Prerequisite: None
* This course is the first in a sequence in which students develop an application area to build into a capstone project. By the end of this course students should have an area of inquiry defined along with a list of several appropriate data sources and an outline for their data science approach.
** This course can be replaced with Data Storytelling or CS 211 Data science by permission only.
Introduction to Data Science II (4 Credits) (MB/ASF/DC/KT)
DATA 201 – Introduction to Data Science II (IMLA), using either R or Python. Expanding upon the introductory course with a continued focus on the full data life cycle reproducible results and being a good data custodian. Intermediate topics include data management, collaboration, prediction techniques, and data privacy and ethics. Techniques from frequentest and Bayesian decision-making, multiple and logistic regression, Q-learning, decision tress, and recommendation systems. Prerequisite: Introduction to Programming and Introduction to Data Science, math for Data Science is highly recommended.
* This course is the second in a sequence in which students develop an application area to build into a capstone project. By the end of this course students should have refined their question, cleaned and visualized data for their application, build a collaborative location (like GIT hub) for the sharing final project, and outlined appropriate next steps given the data they found. Additionally, students should identify methods and assumptions used in creating the data set along with limitations, data privacy, or ethics restrictions on the use or publication of their data.
PHIL 110 Contemporary Moral Issues (CER) - Examination of competing ethical and social political theories in the context of current ethical controversies. Prerequisite: None
PHIL 211 Environmental Ethics (APW,ESS) - Examination of ethical issues about the environment: foundational questions about moral status, public policy issues, and questions of personal morality. Traditional perspectives such as anthropocentrism and individualism are contrasted with alternatives such as the Land Ethic and ecofeminism. Prerequisite: None
PHIL 212 Humans and Other Animals (ESS,H) - Study of relations between humans and other animals, both empirical and ethical. Topics include the nature of animal minds, theories of animal ethics, animals as food, animal experimentation, hunting and fishing, zoos and aquariums. Films, guest speakers, and readings from classical and contemporary sources. Prerequisite: None
PHIL 213 Animal Ethics and Policy - Study of animal ethics and the evolution of animal welfare policy since the Animal Welfare Act of 1966. Examines in substantial depth competing moral arguments and related policy developments in the arenas of companion animal treatment, farmed animal welfare, animal research, and broader theories. Numeric and Evaluation grade only. Prerequisite: None
PHIL 215 Bioethics: Doctors and Patients (CPI,H) - Examination of the ethical issues that arise within the relationship between doctors and patients. Topics include paternalism, autonomy, confidentiality, informed consent, and the conflicts that can arise in medical research. Prerequisite: None
PHIL 216 Bioethics: Technology and Justice - Examination of the ethical issues that arise from the distribution of health resources and the nature of particular procedures and technologies. Topics include fairness in rationing health resources, genetic screening, abortion, and end of life care. Prerequisite: None
PHIL 221 Ethical Theory - Examination of the nature and status of ethical value through historical and contemporary writings. Addresses philosophical arguments defending the objectivity and rationality of ethical principles in light of the challenges presented by individual psychology and cultural difference. Maybe better as an elective?
DATA 211 - Introduction to Databases and Data Management. Introduction to the fundamentals of databases, data modeling, and SQL. Database design and SQL programming Query optimization and performance tuning. SQL for data analysis, reporting, and visualization in business contexts. Prerequisite: Introduction to Programming
Capstone (4 credits) (MB/DC/KT)
DS 401 (WD/OC) – Capstone course requiring students to integrate their knowledge of data science including data processing and cleaning, exploratory data analysis, visualization, prediction, privacy, and ethics and to apply this knowledge to their application area. Focus on clear communication, reproducible and professional code, and a clear understanding of the appropriateness of techniques used. Prerequisite: Senior Standing
* This course is the third and final course in a sequence in which students develop an application area to build into a capstone project. By the end of this course students should have a fully published body of work surrounding the application of data science to their application area. This should include clear referencing to the data used and the restrictions and assumptions on that data, exploratory data analysis and visualization to motive decisions made, predictive analysis appropriate to their field of application, and a summary presentation of their results that includes discussion of ethical and community impacts of the work.
Students should work with their advisor to identify an application area. We highly suggest a second major or minor but an approved set of interdisciplinary courses that combine into a area of interest would also satisfy the application area. Students should choose elective courses to enhance their area of application. Students planning to go on to graduate school in Data Science should consider a second major in economics, mathematics or computer science.
