بكالوريوس العلوم في علوم البيانات

Core courses include data visualization, data mining, machine learning, statistics, and database systems. Students learn to clean, integrate, and analyze complex data to gain actionable insights and tell data-driven stories. Elective tracks allow students to specialize in fields like Big Data Analytics, Econometrics, or Autonomous Systems. In the final capstone course, students apply their skills to solve a real challenge for a company or nonprofit. Recent projects include optimizing supply chain management for a retailer, identifying risk factors for hospital readmissions, and designing a recommendation system for an online education platform.

College Skills (1) & Co-Curricular

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

• EGN 1006 - Career Design for STEM Disciplines (Credits: 1)
• IDS 4941 - Professional Experience Internship (Credits: 0)

Communication (6)

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

Arts and Humanities (3-6)

Data Science majors select 12 credits from Art and Humanities and Social Sciences. Three to six credits, as noted below, must be taken in Art and Humanities.

• 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)
• MUL 2010 - Music Appreciation (Credits: 3)

Optional to fulfill Arts & Humanities requirement:

• IDS 2144 - Legal, Ethical, and Management Issues in Technology (Credits: 3)
• HUM 2022 - Explorations in the Humanities (Credits: 3)

Social Sciences (6-9)

Six to nine credits, as noted below, must be taken in Social Sciences.

Required state general education core, select 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)
• POS 2041 - American Government (Credits: 3)

Program Required

• ECO 2023 - Principles of Microeconomics (Credits: 3)

Mathematics (8)

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

Natural Sciences (12)

Choose one set from the following:

• 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)
• EVR 1001 - Environmental Science (Credits: 3) + EVR 1001L - Environmental Science Lab (Credits: 1)
• CHM 2046 - Chemistry 2 (Credits: 3) + CHM 2046L - Chemistry 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)
• STA 3036 - Probability and Statistics 2 for Business, Data Science, and Economics (Credits: 3)

Data Science Core (54)

These courses provide an essential foundation in Data Science.

• IDS 1380 - Foundational Lessons in Applications of Mathematics (Credits: 3)
• EGN 1007 - Concepts and Methods for Engineering and Computer Science (Credits: 1)
• COP 2271 - Introduction to Computation and Programming (Credits: 3)
• COP 3337 - Object Oriented Programming (Credits: 3)
• COP 3710 - Database 1 (Credits: 3)

Advanced Courses

• CAP 4770 - Data Mining & Text Mining (Credits: 3)
• EGN 3448 - Operations Research (Credits: 3)
• COP 3530 - Data Structures & Algorithms (Credits: 3)
• COP 2073 - Foundations of Data Analytics (Credits: 3)
• STA 3241 - Statistical Learning (Credits: 3)
• CAP 4612 - Machine Learning (Credits: 3)
• STA 4853 - Time Series Analysis for Business, Data Science, and Economics (Credits: 3)
• CTS 2375 - Cloud Infrastructure and Services (Credits: 3)
• ISC 2310 - Python for Data Analytics (Credits: 3)
• QMB 4690 - Process Design Using Lean Sigma (Credits: 2)
• CAP 4793 - Advanced Data Science (Credits: 3)
• ECO 4422 - Econometrics: Causal Inference, Panel and Survey Data (Credits: 3)
• CAP 4786 - Topics in Big Data Analytics (Credits: 3)
• IDC 3180 - Contemporary Issues and Case Studies in Data Science (Credits: 3)

Application Area (6)

Choose six credits from the list below:

• ECP 3004 - Contemporary Economic Issues (Credits: 3)
• MAR 4705 - Marketing Analytics (Credits: 3)
• ESI 4011 - Data Analytics for Smart City & Transportation (Credits: 3)
• FIN 4501 - Investments, Financial Modeling and Analytics (Credits: 3)

Data Science Electives (3)

Choose three credits from the list below:

• CAI 4304 - Natural Language Processing (Credits: 3)
• CAP 3774 - Data Warehousing (Credits: 3)
• CAP 4410 - Computer Vision (Credits: 3)
• CAP 4630 - Artificial Intelligence (Credits: 3)
• CAP 4613 - Applied Deep Learning (Credits: 3)
• COP 4520 - Introduction to Parallel and Distributed Computing (Credits: 3)
• COP 3729 - Database 2 (Credits: 3)
• CEN 4721 - Human-Computer Interaction (Credits: 3)
• CEN 4033 - Secure Software Engineering (Credits: 3)
• CNT 4403 - Data Security (Credits: 3)
• ECO 4400 - Game Theory and Strategic Decisions (Credits: 3)
• ECP 4031 - Benefit-Cost Analysis (Credits: 3)
• EGS 3625 - Engineering & Technology Project Management (Credits: 3)
• ENT 2112 - Entrepreneurial Opportunity Analysis (Credits: 3)

Program Capstone Sequence (6)

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

Graduates from the Data Science Bachelor of Science program will be prepared to:

1. Provide effective solutions to complex problems, based on technical knowledge, methods, and practices of the dynamic data science field.
2. Serve and work as effective and ethical data science professionals to contribute to their community and society.
3. Assume positions of leadership in industry, academia, public service, and entrepreneurship.

Student outcomes describe what students are expected to know and be able to do by the time of graduation. Upon completion of the Data Science program, graduates will have the ability to

1. Identify, formulate, and solve broadly-defined technical or scientific problems by applying knowledge of mathematics and science and/or technical topics to areas relevant to the discipline.
2. Formulate or design a system, process, procedure, or program to meet desired needs.
3. Develop and conduct experiments or test hypotheses, analyze and interpret data, and use scientific judgment to conclude.
4. Communicate effectively with a range of audiences.
5. Understand ethical and professional responsibilities and the impact of technical and/or scientific solutions in global, economic, environmental, and societal contexts.
6. Function effectively on teams that establish goals, plan tasks, meet deadlines, and analyze risk and uncertainty.

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. 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.

Make Your Passion a Career

We're here to give you the resources to land your dream internship, work alongside faculty in groundbreaking research, and develop leadership skills to stand out in the workplace.

Internship Opportunities

Internships are an important part of setting you up for success after college and are a requirement for you to graduate.

Research Opportunities

Conduct research alongside faculty that is improving lives and changing businesses, with impact ranging from the local Lakeland community to the outer reaches of space.

Career Development

We understand the importance of feeling prepared, and we are committed to your success here and beyond. That's why we have resources to support you in your continued career development.