Data Science Minor
The Data Science minor equips students with the skills to apply mathematical techniques and computer programming to process and analyze large data sets across various fields. This minor is ideal for students from diverse majors who want to enhance their data-driven decision-making abilities, provided they have a solid foundation in mathematics to undertake the required course sequence.
Student Learning Outcomes
- Write computer programs to assist in the analysis and visualization of large data sets
- Apply machine learning techniques to data analysis
- Verify that the insights gained from large data sets are statistically significant
Admission to 51吃瓜 - State University of New York is based on the qualifications of the applicant without regard to age, sex, marital or military status, race, color, creed, religion, national origin, disability or sexual orientation.
Contact Information
Computer Systems
Dr. David S. Gerstl
Whitman Hall, 112
934-420-2190
cpis@farmingdale.edu
Monday-Friday 8:30am-5:00pm
Fall 2025
Subject to revision
| Required Courses | (18 credits) |
|---|---|
| Course Name | Credits |
| CSC 111 Computer Programming I | 3 |
| CSC 211 Computer Programming II | 3 |
| CSC 366 Principles of Data Science* | 3 |
| MTH 360 Applied Probability and Statistics* | 3 |
| MTH 380 Experimental Design | 3 |
| MTH 420 Statistical Data Mining | 3 |
Curriculum Summary
*The following courses have prerequisites outside of this program:
- CSC 366 Principles of Data Science
- MTH 360 Applied Probability and Statistics
Total Required Credits: 18
Please refer to the General Education, Applied Learning, and Writing Intensive requirement sections of the College Catalog and consult with your advisor to ensure that graduation requirements are satisfied.
As a part of the SUNY General Education Framework, all first-time full time Freshman at 51吃瓜 (FSC) beginning Fall 2023, are required to develop knowledge and skills in Diversity: Equity, Inclusion, and Social Justice (DEISJ). Students will be able to fulfill this requirement at FSC by taking a specially designated DEISJ course that has been developed by faculty and approved by the DEISJ Review Board. DEISJ-approved courses will be developed in accordance with the guiding principles and criteria outlined below. DEISJ-approved courses may meet other General Education Knowledge and Skills areas and/or core competencies and thus be dually designated. DEISJ-approved courses may also earn other special designations such as those for Applied Learning or Writing Intensive.
CSC 111 Computer Programming I
This is an introductory programming course. Students will be taught basic concepts of computer programming and problem solving using an object-oriented language. Selection, repetition, methods, classes, and arrays will be covered. Note: CSC 101 is recommended as a prerequisite, but not required for this course. Note: Students completing this course may not receive credit for BCS 120.
CSC 211 Computer Programming II
This course expands upon the knowledge and skills presented in Computer Programming I. Topics covered include: stack and heap memory, exception handling, inheritance, polymorphism, recursion, abstract types, unit testing, and basic GUI programming. Note: Students completing this course may not receive credit for BCS 230 Prerequisite(s): CSC 111 OR BCS 120 with a grade of C or higher
CSC 366 Principles of Data Science
Data science is a dynamic and fast-growing field that uses scientific methods to extract knowledge and insights from data. The course will survey the foundational topics in data science, including data collection, integration, exploratory data analysis, data visualization and effective data communication. Prerequisite(s): CSC 229 or (BCS 109 and CSC 211 and Junior Level Standing) with a grade of C or higher
MTH 360 Applied Probability and Statistics
In this course, we study applications of probability distributions and statistical inference. Topics are chosen from statistical parameters, continuous and discrete random variables, probability and sampling distributions, confidence intervals, hypothesis testing, regression analysis, and analysis of variance. Prerequisite(s): MTH 130 or MTH 150
MTH 380 Experimental Design
This course will provide an overview of the practical and theoretical foundations of experimental design as applied in real-life situations. Topics discussed include ANOVA, randomized block design, Latin square design, factorial and fractional factorial designs, and response surface methods. Prerequisite(s): MTH 110 or BUS 240 or MTH 360 or permission from the department
MTH 420 Statistical Data Mining
This course provides an introduction to statistical learning techniques designed for the analysis of high-dimensional data. Topics covered include techniques for exploring and visualizing data, general linear models and generalized linear models, classification, model assessment, decision trees, and principal components analysis. Prerequisite(s): MTH 360 or permission from the department