![group of male students working in cyber lab](/sites/default/files/styles/hero_home_page_huge/public/2023-10/A48I2832-edit.jpg?itok=SEHxSWzo)
Data Science
Uncover Insights. Drive Innovation.
Clean, manage, analyze, and interpret data. Be prepared for careers in a number of fields, skilled at determining actionable insights about the meaning of data for decision-makers in businesses, public agencies, and nonprofits.
The demand for data-driven decision-making has made data science one of the most sought-after fields in today鈥檚 job market.
Our Data Science degree integrates theoretical knowledge with practical applications, ensuring students are well-versed in the latest tools and technologies in data science.
Graduates from our program can pursue jobs such as data analysts, business analysts, data engineers, machine learning engineers, and data scientists. Data science professionals are needed in various sectors, including technology, finance, healthcare, and government.
Growing Industry
Required Courses
- Databases
- Machine Learning
- Data Visualization
- Big Data Analytics
- Neural Networks
- Special Topics, discussing the latest in the field
- CIS 100: Introduction to Computer Science (3 credits)
- CIS 200: Linear Data Structures (3 credits)
- CIS 210: Non-Linear Data Structures (3 credits)
- Choose at least two other CIS courses (except CIS 201)
听 - CIS 100: Introduction to Computer Science (3 credits)
- CIS 160: Introduction to Cyber Security (3 credits)
- CIS 230: Operating Systems (3 credits)
- CIS 260: Computer Communications & Networks (3 credits)
- CIS 261: Information Security (3 credits)
- CIS 360: Computer Systems Security (3 credits)
- CIS 100: Introduction to Computer Science (3 credits)
- STAT 139: Statistics for Social Sciences (3 credits)
- CIS 150: Introduction to Data Science (3 credits)
- CIS 200: Linear Data Structures (3 credits)
- CIS 280: Data Visualization & Communication (3 credits)
- CIS 350: Relational Database Systems (3 credits)
- CIS 352: Big Data Analytics (3 credits)
- CIS 371: Machine Learning (3 credits)
Core Courses:
Plus one of the following courses:
Learning Outcomes
- Describe all steps of the data cycle
- Elicit client requirements and form a clear empirical question
- Pre-process data, so that it can be stored and analyzed
- Choose and evaluate appropriate mathematical, statistical, and machine-learning models, depending on the specific information needs of the project
- Design and implement efficient computer programs to clean, analyze, and visualize data
- Communicate the results and limitations of data analysis and modeling to a non-technical audience
- Handle all data and communications professionally, ethically, and securely
Christopher Mansour Ph.D.
Chair, Department of Computing and Information Science
Office: Library 402
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Meet the Faculty
Computing and Information Science
![Portrait image of Maria Garase](/sites/default/files/styles/person_detail_small/public/2024-07/GaraseMariaWeb_0.jpg?itok=TM6QHL2X)
Maria Garase
Interim Dean, The School of Intelligence, Computing, and Global Politics; Dean, The School of Social & Behavioral Sciences; Associate Professor, Criminology & Criminal Justice
![Portrait image of Christopher Mansour](/sites/default/files/styles/person_detail_small/public/2024-01/MansourChristopherWeb.jpeg?itok=-GC-ERhJ)
Christopher Mansour, Ph.D.
Chair, Computing and Information Science (Cyber Security and Data Science) Department, Associate Professor