Data Science

Unleash the vast potential of data with innovation and computation.

Data Science Degree

Discover your path to success in the dynamic field of data science at Susquehanna University. Explore a robust curriculum with a technical foundation in mathematics, statistics and computer science, focusing on innovation and computation to unleash the vast potential of data.

Dive into algorithms, machine learning and advanced computational techniques for an educational experience distinct from traditional business analytics programs. Learn from experienced professionals, as they guide you through real-world projects, ensuring practical skills applicable to the evolving data science landscape.

Tailor your studies to unlock the full potential of a data science degree. Advance your skills with a diverse range of courses in other disciplines that pave the way for success across various industries.

Academics at Susquehanna

Which data degree should I choose?

Three people sit side by side at a row of desks, focused on their computers. They work intently in a classroom setting. The background is plain, and lighting is bright.

Data Science

Data science addresses intricate problems through the utilization of advanced computer skills and analytical techniques. This field involves developing algorithms and models for processing and analyzing datasets, making it a versatile and impactful discipline with applications across various industries.

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Three people in an office setting focus on computer screens displaying stock market data. A man in a light shirt stands, interacting with two seated colleagues. A digital board in the background shows various financial figures and categories.

Business Analytics

Business analytics centers around analyzing data to inform business decisions, utilizing methods such as statistical analysis and reporting tools. The goal is to extract meaningful insights for enhancing processes and achieving business objectives, primarily within the domain of business management.

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95%

of students study abroad

99%

of students receive financial aid

50%

of courses are hands-on or project-based

“I love that Susquehanna is a small, interconnected campus that is big on service and helping the community.”

— Alessia Revelli ’26

Data Science

Mathematics

“Everybody is appreciated for all of their differences and there’s a sense of unity among all the students, even though we’re all different in our own amazing ways.”

— Hannah Foust ’26

Data Science

Political Science

“At Susquehanna University, I received the skills and practical hands-on experiences to excel in my career working with database infrastructures.”

— Kristopher Sauls ’12

Data Science

Computer Science

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Program Resources

A Glance Into Your Degree Pathway

With support from advisors and course planning tools, your time at Susquehanna is carefully designed to help you succeed. This example provides a glimpse into your degree experience, but you’ll have plenty of opportunities to customize your path with electives or study abroad programs that reflect your passions and career goals.

First Year – Fall Semester

CSCI 181

Principles of Computer Science

An introductory course in computer science for majors. Also open to nonmajors. Emphasizes computer problem-solving methods and algorithmic development. Topics include programming in Python or a comparable language, techniques of good programming style, data types, file and screen input and output, control structures, subroutines, recursion, arrays, and pointers. 4 SH. CC: Analytical Thought.

MATH 111

Calculus I

Differentiation and integration of polynomials, exponential, logarithmic and trigonometric functions, rules of differentiation, the Mean Value Theorem, L’Hôpital’s Rule, the Fundamental Theorem of Calculus and applications. 4 SH. CC: Analytical Thought.

First Year – Spring Semester

CSCI 182

Object Oriented Programming & Design

This is the second course in computer programming that builds upon functional programming developed in the prerequisite course. This class introduces object-oriented programming, stressing the importance of planning, design and optimization of object-oriented solutions to programming problems. Topics include objects, classes, constructors, inheritance, polymorphism and design. Prerequisite: CSCI-181 Principles of Computer Science. 4 SH.

MATH 112

Calculus II

Techniques of integration. Also includes improper integrals, further applications of integration and power series. Prerequisite: MATH-111 Calculus I or equivalent. 4 SH. CC: Analytical Thought.

Second Year – Fall Semester

CSCI 281

Data Structures

Second course in computer programming. Stresses the interplay between algorithms, data structures and their implementations. Topics include stacks, queues, linked lists, sorting, searching, binary trees and graphs. Prerequisite: CSCI-181 Principles of Computer Science.

MATH 180

Statistical Methods

This course provides a broad overview of introductory statistical methods and data analysis. Topics include descriptive statistics, probability, probability distributions, statistical inferences on population means and population variances, multiple comparisons, categorical data, data analysis using linear regression and multiple regression, design of experiments, and analysis of variance. 4 SH. CC: Analytical Thought.

N/A

Program Elective

Data Science

Choose from a variety of elective courses within this program to customize your goals.

Second Year – Spring Semester

CSCI 301

Data Mining

This course provides an introduction to the concepts in the automatic extraction of implicit, previously unknown and potentially useful information from large data sets generated in commerce, science and other fields. Topics include preprocessing of the data, application of the fundamental algorithms on the prepared data and interpretation of the patterns discovered by the algorithms. Introduced are the fundamental algorithms for supervised learning, including classification and numerical prediction and unsupervised learning, which includes association rules and clustering. Prerequisites: CSCI-181 and either MATH-180 or both MATH-108 and INFS-233. 4 SH.

MATH 201

Linear Algebra

Systems of linear equations, matrices and matrix algebra, vector spaces, linear transformations, inner product spaces, determinants, eigenvalues and eigenvectors, and selected applications. Prerequisite: MATH-111 Calculus I. 4 SH. CC: Analytical Thought.

N/A

Program Elective

Data Science

Choose from a variety of elective courses within this program to customize your goals.

Third Year – Fall Semester

MATH 221

Discrete Structures

An introduction to the basic logical and set-theoretic framework of mathematics and computer science. Topics include logic, proof techniques, mathematical induction, divisibility and modular arithmetic, sets, relations, mappings, graphs, and counting principles. Prerequisite: MATH-111 Calculus I. 4 SH.

N/A

Program Elective

Data Science

Choose from a variety of elective courses within this program to customize your goals.

Third Year – Spring Semester

CSCI 381

Algorithms

Introduces the design and implementation of algorithms using an object-oriented programming language such as C++ or Java. Covers correctness and efficiency of algorithms for sorting, searching, graph problems and mathematical algorithms. Prerequisites: MATH-221 Discrete Structures and CSCI-281 Data Structures. 4 SH.

MATH 211

Multivariate Calculus

Calculus of several variables, partial derivatives, critical points, multiple integrals, gradient, curl, divergence, Green’s, Stokes’ and Divergence Theorems. Prerequisites: MATH-112 Calculus II and MATH-201 Linear Algebra. 4 SH.

Fourth Year – Fall Semester

CSCI 401

Machine Learning

This course provides an introduction to the systematic study of algorithms and systems that improve their knowledge or performance with experience. A statistical approach that emphasizes concepts and the implementation of the methods is presented to make sense of large and complex data. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines and clustering. Prerequisites: MATH-180 Statistical Methods (or both MATH-108 Intro to Statistics and INFS-233 Data Driven Decision Making), MATH-201 Linear Algebra, and CSCI-181 Principles of Computer Science. 4 SH.

Fourth Year – Spring Semester

CSCI 500

Senior Colloquium

Experience in individual research and presentation of computer related topics. Prerequisites: senior standing and computer science department head’s permission. 4 SH. CC: Writing Intensive.

CSCI 181

Principles of Computer Science

CSCI 182

Object Oriented Programming & Design

CSCI 281

Data Structures

CSCI 301

Data Mining

CSCI 381

Algorithms

CSCI 401

Machine Learning

MATH 111

Calculus I

MATH 112

Calculus II

Techniques of integration. Also includes improper integrals, further applications of integration and power series. Prerequisite: MATH-111 Calculus I or equivalent. 4 SH. CC: Analytical Thought.

MATH 180

Statistical Methods

MATH 201

Linear Algebra

MATH 211

Multivariate Calculus

MATH 221

Discrete Structures

CSCI 500

Senior Colloquium

Experience in individual research and presentation of computer related topics. Prerequisites: senior standing and computer science department head’s permission. 4 SH. CC: Writing Intensive.

When you enroll at Susquehanna, you’ll be paired with an advisor and application tool to guide you in your course planning and scheduling. The following is an excerpt from the complete course catalog. Enrolled students follow the requirements of the course catalog for the academic year in which they declare each major and/or minor, consult with their advisor(s).

Data Science

The data science major is a program that combines mathematics, statistics, and computer science with an area of application to give students the tools necessary to pursue a career in the field. The major is intended for students who desire a strong technical background in the areas that form the discipline of data science.

Learning Goals

Students will develop:

Proficiency in relevant data analysis techniques such as data cleaning and statistical modeling. (Covered in CSCI-301, CSCI-401, MATH-180).
Proficiency in programming languages and tools such as Python, R, and data manipulation libraries. (CSCI-181, CSCI-182, CSCI-281, CSCI-301, CSCI-401).
Proficiency in effective communication both orally and in writing to various stakeholders including non-technical audiences. (CSCI-500 capstone).

Students will be evaluated in the courses covering the various skills and in a capstone course.

Bachelor of Science, Data Science Requirements

To earn the Bachelor of Science degree in data science, a student must complete, with grades of C- or better, the equivalent of the following:

4 CSCI-181 Principles of Computer Science
4 CSCI-182 Object-oriented Programming and Design
4 CSCI-281 Data Structures
4 CSCI-301 Data Mining
4 CSCI-381 Algorithms
4 CSCI-401 Machine Learning
4 MATH-111 Calculus I
4 MATH-112 Calculus II
4 MATH-180 Statistical Methods
4 MATH-201 Linear Algebra
4 MATH-211 Multivariate Calculus
4 MATH-221 Discrete Structures
4 CSCI-500 Senior Colloquium

In addition to the courses above, a student must complete a sequence of three courses outside the department chosen from the list below or another sequence approved by the Mathematics and Computer Science department:

Anthropology: ANTH-162; either ANTH-235 or SOCI-245; either ANTH-341 or ANTH-310
Biology: either BIOL-101 or BIOL-102; BIOL-220; one 300-level or higher BIOL course including lab
Chemistry: CHEM-103; CHEM-242; CHEM-341
Earth and Environmental Sciences: EENV-101; one of EENV-105, EENV-220 or EENV-240; EENV-360
Ecology: ECOL-100; BIOL-220; one 300-level or higher ECOL course including lab
History: One 100-level HIST course; HIST-300; HIST-401
Physics: PHYS-204; PHYS-206; either PHYS-307 or PHYS-401
Political Science: One of POLI-111, POLI-121, POLI-131 or POLI-212; POLI-205; POLI 305
Psychology: PSYC-101; PSYC-223; one 300-level or higher PSYC course
Sociology: SOCI-101; SOCI-235 or SOCI-245; one of SOCI-102, SOCI-255

Double-counting Restriction

Students double majoring in computer science and data science cannot count CSCI-301 and CSCI-401 as computer science electives.

Interdisciplinary Options

Students majoring in computer science can easily complete a minor in another department. Areas such as accounting, business, biology, chemistry, mathematics, physics or a modern language are natural choices. Highly motivated students whose interests cross traditional departmental lines may also wish to consider the self-designed major option, developing an integrated program of study from courses in several departments. For additional information, see the majors and minors section.

Minor in Data Science

The minor in data science requires the completion of 20 semester hours of the following courses with grades of C- or better:

MATH-180 Statistical Methods (or MATH-108 Introduction to Statistics plus INFS-233 Data Driven Decision Making)
MATH-201 Linear Algebra
CSCI-181 Principles of Computer Science
CSCI-301 Data Mining
CSCI-401 Machine Learning