Data Science Expertise

Behind every corporate profit and loss statement, and every new product idea, is a deep set of data that helps companies and engineers make better choices. As a data scientist, you will build the tools that turn big data into real-world insight.

Translate Data into Action

The B.S. in Data Science combines applied mathematics, computer science, statistics, optimization, data mining and machine learning to give you a broad and much desired skill set. From tracking patterns in drug discovery, or changes in ocean temperatures and deforestation, to understanding the habits of Netflix users and predicting stock market fluctuations, Data Scientists inform product design, government policy, and business strategy, turning data into actionable insights.

Program Highlights

Data science draws from math, engineering, business, and computer science to extract knowledge and insights from data, structured and unstructured. The B.S. in Data Science curriculum will teach you computational and analytic approaches to mastering complex data structures. You will gain hands-on experience with tools such as Excel, Python, R, SQL Databases, and Tableau, and be prepared for emerging careers in Data Science and future advanced study.


Big Data Analytics

Students in Big Data Analytics learn to collect, manage and optimize large-scale structured and unstructured data sets to facilitate information and decision-making. Students in Big Data Analytic develop a strong foundation in essential programming skills, quantitative analysis, and hardware and software solutions for facilitating effective use of big data.

Health Informatics

Health Informatics concerns the storage, retrieval, use and delivery of health-related data and information across networks of patients and providers. Whether in support of emergency room optimization, patient health-care affordability, or study and prevention of global pandemics, the practice of health informatics is essential to achieving solutions, better delivery and greater knowledge for the benefit of humankind.

Health Systems Engineering

The Health Systems Engineering (HSE) concentration program prepares the architects of tomorrow’s healthcare system to take on its most urgent and complex challenges. Health systems engineering involves a multidisciplinary approach that applies engineering and analytic principles to all aspects of healthcare. Students in the program gain real-world experience with industry experts and a multidisciplinary faculty to prepare students for a career leading change in healthcare.

Intelligent Mobility

Intelligent Mobility uses data and technology to connect people, places, and goods across all transportation modes. Growth in intelligent mobility will transform the way people travel, interact with their environment, and connect goods and services.

Quantitative Economics and Econometrics

Quantitative Economics and Econometrics use mathematical and statistical methods to develop techniques for measuring a range of systems, including financial, government, social, legal, medical, and so on. Students with a quantitative economics and econometrics background learn to assess and measure trends to understand complex phenomena and to improve long-term positioning.

Bachelor of Science, Data Science (Plan of Study)

Concentrations in Big Data Analytics, Health Informatics, Intelligent Mobility, and Quantitative Economics and Econometrics

Course offering frequency subject to change

Freshman Year
Semester 1

EMAC 2311 - Analytic Geometry and Calculus 1 Credits: 4

ECHM 2045 - Chemistry 1 Credits: 3

ECHM 2045L - Chemistry 1 Laboratory Credits: 1

PENC 1101 - English Composition 1: Expository and Argumentative Writing Credits: 3

FEIDS 1380 - Introduction to STEM Credits: 3

ESLS 1106 - Academic & Professional Skills Credits: 1

Total Semester Credits: 15
Semester 2

EMAC 2312 - Analytic Geometry and Calculus 2 Credits: 4

EPHY 2048 - Physics 1 Credits: 3

EPHY 2048L - Physics 1 Laboratory Credits: 1

PENC 2210 - Technical Writing Credits: 3

FCOP 2271C - Introduction to Computation and Programming Credits: 3

PEGN 1007C - Concepts and Methods for Engineering and Computer Science Credits: 1

Total Semester Credits: 15
Sophomore Year
Semester 1

FCOP 2034 - Introduction to Programming Using Python Credits: 3

EPHY 2049 - Physics 2 Credits: 3

EPHY 2049L - Physics 2 Laboratory Credits: 1

FCOP 3337C - Object Oriented Programming Credits: 3

FCOP 3353C - Introduction to Unix Credits: 2

FSTA 2023 - Statistics 1 Credits: 3

Total Semester Credits: 15
Semester 2

FMAS 3114 - Computational Linear Algebra Credits: 3

FECO 2023 - Principles of Microeconomics Credits: 3

FCTS 2375 - Cloud Implementation Strategies and Cloud Providers Credits: 3

EMAD 2104 - Discrete Mathematics Credits: 3

FCOP 2073 - Introduction to Data Science Credits: 3

Total Semester Credits: 15
Junior Year
Semester 1

ECNT 3004C - Introduction to Computer Networks Credits: 3

FCOP 3530 - Data Structures & Algorithms Credits: 3

PQMB 3200 - Advanced Quantitative Methods Credits: 3

FEGS 3625 - Engineering & Technology Project Management Credits: 3

Arts and Humanities Elective: Credits: 3

FEIDS 4941 - Professional Experience Internship Credits: 0

Total Semester Credits: 15
Semester 2

FCOP 3710 - Database 1 Credits: 3

PEGN 3448 - Operations Research Credits: 3

FSTA 3241 - Statistical Learning Credits: 3

PCAP 4763 - Time Series Modeling and Forecasting Credits: 3

Data Science Concentration course or program elective: Credits: 3

Total Semester Credits: 15
Senior Year
Semester 1

FIDC 4942 - Data Analytics Capstone I Credits: 3

PCAP 4770 - Data Mining & Text Mining Credits: 3

Data Science Concentration Course or program elective - Credits: 3

Data Science Concentration Course or program elective - Credits: 3

Social Science Course - Credits: 3

Total Semester Credits: 15
Semester 2

FIDC 4943 - Data Analytics Capstone II Credits: 3

PCAP 4612 - Machine Learning Credits: 3

Data Science Concentration course or program elective Credits: 3

Data Science Concentration course or program elective Credits: 3

Select any Arts and Humanities or Social Science courses (see note): Credits: 3

Total Semester Credits: 15


Students select one concentration for twelve hours of credit.
Big Data Analytics

FCOP 3729C - Database 2 Credits: 3

PCAP 3774 - Data Warehousing Credits: 3

PCAP 4786 - Topics in Big Data Analytics Credits: 3

Program Elective Credits: 3

Health Informatics

EHIM 2340 - Development and Administration of Health Information Systems Credits: 3

EHIM 4064 - Survey of the US Health Care System Credits: 3

EHIM 4484 - Advanced Topics 1: Consumer and Population Health Informatics Credits: 3

Other concentration course or program elective Credits: 3

Intelligent Mobility

PMAN 4593 - National Transportation Management Credits: 3

FESI 4011 - Data Analytics for Smart City & Transportation Credits:

FESI 4513 - Intelligent Mobility Credits: 3

FESI 3005 - Introduction to Networks and a Connected World Credits: 3

Quantitative Economics & Econometrics

FECP 4044 - Economic Analysis for Technologists Credits: 3

FECO 3930 - Special Topics Credits: 3

Other Concentration course or program elective Credits: 3

FECP 4031 - Benefit Cost Analysis Credits: 3


PCAP 4763 - Time Series Modeling and Forecasting Credits: 3

(students take whichever is not already required in the degree)

Data Analytics Electives

ECNT 3200 - Distributed Information Systems Credits: 3

FCOP 3330C - Computer Programming 2 Credits: 3

ECDA 3100 - Computer Architecture Credits: 3

FCOP 4520 - Introduction to Parallel and Distributed Computing Credits: 3

PCAP 4793 - Advanced Data Science Credits: 3

PENT 2112 - Entrepreneurial Opportunity Analysis Credits: 3

EHIM 4654 - Implementation of EHR/EMR and Clinical Support Methods Credits: 3

EHIM 4016 - Policy Issues in Health Informatics Credits: 3

PCAP 4630 - Artificial Intelligence Credits: 3

PEGN 3466 - Discrete Event Simulation Credits: 3

ECNT 4403 - Data Security Credits: 3

PCEN 4010 - Software Engineering Credits: 3

PCAP 4830 - Modeling and Simulation Credits: 3

PCAP 4410 - Computer Vision Credits: 3

FCIS 3301 - Business Intelligence Credits: 3

FCIS 2005 - Fundamentals of Applied Information Credits: 3

Arts, Humanities, and Social Sciences

Note: Data Science majors select 12 credits from Art and Humanities and Social Sciences. Students may opt to take 9 credits in Social Science and 3 in Arts and Humanities, or divide them evenly. Six credits, as noted below, must be taken in Social Sciences.

Arts and Humanities

Select 3 and up to 6 credits from the following courses. At least 3 credits must come from the required list. If PAMH 2020 is taken to satisfy the Social Science and the Civic Literacy requirement, then students may take an additional 3 credits from Arts and Humanities for a total of 6 credits from this area.

Required, one from the following:

PARH 2000 - Art Appreciation Credits: 3

EHUM 2020 - Introduction to the Humanities Credits: 3

PLIT 2000 - Introduction to Literature Credits: 3

EPHI 2010 - Introduction to Philosophy Credits: 3

Optional, one from the following or one more from Arts & Humanities required or Social Sciences:

EHUM 2022 - Explorations in the Humanities Credits: 3

FEIDS 2144 - Legal, Ethical, and Management Issues in Technology Credits: 3

Social Sciences

Select 6 to 9 credits. FECO 2023 is already in the plan of study, so students must choose at least 3 credits from the required list to fulfill the Social Sciences requirement.

Required, one from the following:

FECO 2013 - Principles of Macroeconomics Credits: 3

EPSY 2012 - General Psychology Credits: 3

Optional, one from the following or one more from Social Science required or Arts & Humanities

PAMH 2020 – Managerial History Credits: 3

PAMH 2930 - Special Topics Credits: 1-3

FECO 2023 - Principles of Microeconomics Credits: 3

Total Degree Credits: 120