Bachelor of Science, Data Science (Program Description)

Total Degree Credits: 120

See the Bachelor of Science, Data Science (Plan of Study)

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured. New computational and analytic approaches to a vast array of forms, scales, and sources of data are now critical to research, decision-making, and action.

Data scientists develop mathematical models, computational methods and tools for exploring, analyzing and making predictions from data. They ask appropriate questions about data and interpret the predictions based on their expertise of the subject domain.

The rigorous curriculum in Data Science program focuses on the fundamentals of applied mathematics, computer science, probability, statistics, optimization, and machine learning while incorporating real-world examples.

In addition to taking Data Science core curriculum subjects, students also complete a concentration in one of four areas: Big Data Analytics, Health Informatics, Intelligent Mobility, or Quantitative Economics & Econometrics.

After completion of the B.S in Data Science, students will be able to:

Apply current data science concepts, techniques, and practices to solve complex problems.
Analyze a given data science problem and formulate a solution in terms of the datasets needed, the techniques required or the technologies to be utilized.
Communicate effectively insights, analysis, conclusions, or solutions to a diverse audience.

**Course offering frequency subject to change**

College Skills (1) & Co-Curricular

All majors are required to complete an approved internship/professional experience prior to graduation.

SLS 1106 - Academic & Professional Skills Credits: 1
IDS 4941 - Professional Experience Internship Credits: 0

General Education (38)

Click here to view the complete list of General Education offerings. The State of Florida requires a minimum of 36 credit hours of general education for the baccalaureate degree. The following are required for the B.S. in Data Analytics.

Communication (6)

ENC 1101 - English Composition 1: Expository and Argumentative Writing Credits: 3
ENC 2210 - Technical Writing Credits: 3

Arts and Humanities (3 to 6)

Required, select one from the following:
ARH 2000 - Art Appreciation Credits: 3
LIT 2000 - Introduction to Literature Credits: 3
HUM 2020 - Introduction to the Humanities Credits: 3
PHI 2010 - Introduction to Philosophy Credits: 3
Optional, select one from the following:
IDS 2144 - Legal, Ethical, and Management Issues in Technology Credits: 3
HUM 2022 - Explorations in the Humanities Credits: 3

Social Sciences (6 to 9)

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.

Required, one from the following:
AMH 2020 - American History Since 1877 Credits: 3
ECO 2013 - Principles of Macroeconomics Credits: 3
PSY 2012 - General Psychology Credits: 3
Required for Data Science majors:
ECO 2023 - Principles of Microeconomics Credits: 3
Optional, select one from the following, OR another History, OR another Arts and Humanities:
AMH 2010 - American History to 1877 Credits: 3
AMH 2930 - Special Topics Credits: 1-3

Mathematics (8)

MAC 2311 - Analytic Geometry and Calculus 1 Credits: 4
MAC 2312 - Analytic Geometry and Calculus 2 Credits: 4

Natural Sciences (12)

CHM 2045 - Chemistry 1 Credits: 3
CHM 2045L - Chemistry 1 Laboratory Credits: 1
PHY 2048 - Physics 1 Credits: 3
PHY 2048L - Physics 1 Laboratory Credits: 1
PHY 2049 - Physics 2 Credits: 3
PHY 2049L - Physics 2 Laboratory Credits: 1

Advanced Math and Analytics (12)

STA 2023 - Statistics 1 Credits: 3
MAD 2104 - Discrete Mathematics Credits: 3
MAS 3114 - Computational Linear Algebra Credits: 3
QMB 3200 - Advanced Quantitative Methods Credits: 3

Data Science Core (48)

These courses provide an essential foundation in Data Science.

IDS 1380 - Introduction to STEM Credits: 3
EGN 1007C - Concepts and Methods for Engineering and Computer Science Credits: 1
COP 2271C - Introduction to Computation and Programming Credits: 3
COP 3337C - Object Oriented Programming Credits: 3
COP 3353C - Introduction to Unix Credits: 2
COP 3710 - Database 1 Credits: 3
CNT 3004C - Introduction to Computer Networks Credits: 3
CTS 2375 - Cloud Implementation Strategies and Cloud Providers Credits: 3
CAP 4770 - Data Mining & Text Mining Credits: 3
CAP 4763 - Time Series Modeling and Forecasting Credits: 3
EGN 3448 - Operations Research Credits: 3
COP 3530 - Data Structures & Algorithms Credits: 3
EGS 3625 - Engineering & Technology Project Management Credits: 3
COP 2073 - Introduction to Data Science Credits: 3
STA 3241 - Statistical Learning Credits: 3
COP 2034 - Introduction to Programming Using Python Credits: 3
CAP 4612 - Machine Learning Credits: 3

Concentrations (12)

Students choose twelve credits from one concentration area.

Big Data Analytics

COP 3729C - Database 2 Credits: 3
CAP 3774 - Data Warehousing Credits: 3
CAP 4786 - Topics in Big Data Analytics Credits: 3
Program Elective Credits: 3

Health Systems Engineering

The Health Systems Engineering (HSE) concentration at Florida Poly prepares students for the dynamic and ever-changing field of healthcare. The concentration supplements traditional degree programs in Computer Science, Data Science and Business Analytics with application examples based on the challenging problems in the healthcare industry. The concentration is grounded in the fundamentals of systems engineering and analytics, and focuses on process improvement, health informatics, healthcaresystems modeling and healthcare decision making.

HIM 3490 - Introduction to Health Systems Engineering Credits: 3
HIM 3514 - Health Systems Modeling and Optimization Credits: 3
HIM 3654 - Health Systems Implementation Credits: 3
Other DSBA concentration course OR program elective

Quantitative Economics & Econometrics

ECP 4044 - Economic Analysis for Technologists Credits: 3
ECO 3930 - Special Topics Credits: 3
Other Concentration course or program elective Credits: 3
ECP 4031 - Benefit Cost Analysis Credits: 3
OR
CAP 4763 - Time Series Modeling and Forecasting Credits: 3
(students take whichever is not already required in the degree)

Intelligent Mobility

ESI 3005 - Introduction to Networks and a Connected World Credits: 3
OR
CNT 3004C - Introduction to Computer Networks Credits: 3
ESI 4011 - Data Analytics for Smart City & Transportation Credits:
ESI 4513 - Intelligent Mobility Credits: 3
Select one course from the following:
MAN 4593 - National Transportation Management Credits: 3
MAN 4594 - Reverse Logistics Credits: 3
AVM 3012 - Air Transportation and Operations Credits: 3

Data Science Electives (3)

Choose three credits from the list below or from available concentration courses.

CNT 3200 - Distributed Information Systems Credits: 3
COP 3330C - Computer Programming 2 Credits: 3
COP 4520 - Introduction to Parallel and Distributed Computing Credits: 3
CAP 4793 - Advanced Data Science Credits: 3
ENT 2112 - Entrepreneurial Opportunity Analysis Credits: 3
HIM 4654 - Implementation of EHR/EMR and Clinical Support Methods Credits: 3
HIM 4016 - Policy Issues in Health Informatics Credits: 3
CAP 4630 - Artificial Intelligence Credits: 3
EGN 3466 - Discrete Event Simulation Credits: 3
CNT 4403 - Data Security Credits: 3
CEN 4010 - Software Engineering Credits: 3
CAP 4410 - Computer Vision Credits: 3

Program Capstone Sequence (6)

IDC 4942 - Data Analytics Capstone I Credits: 3
IDC 4943 - Data Analytics Capstone II Credits: 3

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