Objectives

Develop proficient skills in programming, data analysis, and visualization tools.
Gain a comprehensive understanding of machine learning algorithms and applications.

Outline

Program Content

The program curriculum covers a broad range of data science topics, including:

Programming for Data Analysis
Applied Probability and Statistics
Cloud Computing and Virtualization
Big Data Systems
Machine Learning
Mathematical Modeling
Visualizing and Communicating Data Knowledge
Artificial Intelligence

Program Structure

The program offers both Bachelor of Science (B.S.) and Bachelor of Arts (B.A.) options, providing flexibility for students with diverse interests. The B.S. option emphasizes a deeper dive into technical aspects, while the B.A.

Course Schedule

The program follows a typical semester schedule.

Applied Probability and Statistics: Covers foundational statistical concepts like probability distributions, hypothesis testing, and regression analysis.
Cloud Computing and Virtualization: Explores cloud platforms and technologies for data storage and processing.
Big Data Systems: Examines technologies and techniques for handling and analyzing large-scale datasets.
Machine Learning: Introduces various machine learning algorithms, including supervised and unsupervised learning methods.
Mathematical Modeling: Focuses on mathematical modeling techniques for real-world data analysis.
Artificial Intelligence: Explores artificial intelligence concepts, applications, and ethical considerations.

Assessment

Assessment Methods

The program utilizes various assessment methods, including:

Exams and quizzes
Assignments and projects
Presentations
Participation in class discussions

Assessment Criteria

Assessments are evaluated based on:

Accuracy and completeness of answers
Clarity and conciseness of communication
Critical thinking and problem-solving skills
Ability to analyze and interpret data
Effective use of data science tools and methodologies

Teaching

Teaching Methods

The program utilizes diverse teaching methods, including:

Interactive lectures
Hands-on labs and workshops
Group projects and presentations
Case studies and real-world applications

Faculty

The program boasts experienced faculty with expertise in data science and relevant fields.

Unique Approaches

The program emphasizes a hands-on learning approach, encouraging students to apply data science concepts to real-world problems and projects. Students also have access to the Data Science and Artificial Intelligence Laboratory (DataSAIL) for research and project work.

Careers

Career Paths

Graduates of the program can pursue careers in various fields, including:

Data Scientist
Data Analyst
Data Strategist
Data Engineer
Predictive Modeler
Information Research Scientist
Market Research Analyst
Risk Manager

Career Opportunities

The program prepares students for positions in various industries, including:

Technology
Finance
Healthcare
Retail
Marketing
Government
Non-profit organizations

Career Outcomes

The program boasts a strong track record of graduates securing employment in data science-related roles. Many graduates also pursue further education in data science or related fields.

Data Science and Artificial Intelligence Laboratory (DataSAIL)
ECaMS Cyber Defense Club
Upsilon Pi Epsilon National Honors Society
Prometheon Technology Club
Association of Computing Machinery (ACM)
Institute of Electrical and Electronics Engineers (IEEE) Student Chapter
Math Club
e-Sports Club

Data Science, B.S.