Data Science, Bachelor of Science
College: College of Science and Health
Department: Mathematical and Computational Sciences
Student Type: Traditional Undergraduate
Degree: Bachelor of Science
Campus: Lisle Campus
Progression in the Data Science Program
For progression in the Data Science program students must complete the introductory sequence of CMSC 2200 Computer Programming, CMSC 2205 Data Structures and Algorithms I, and MATH 2210 Calculus I, MATH 2211 Calculus II, MATH 2212 Calculus III with a GPA of 2.500 or above and a grade of “C” or better in each of these courses. A transfer student must meet these requirements through equivalent transfer courses. Additionally, a transfer student must earn a GPA of 2.500 or above in all major classes during the first semester at Benedictine in order to progress in the Data Science program.
If it is determined at any time that a student cannot progress in the Data Science program or cannot graduate with a Data Science degree, the student will be required to change his or her major and seek academic advising outside of that program.
Requirements - Major
The Data Science major must complete a minimum of 52 semester credit hours of mathematics and computer science courses. Required courses are:
|CMSC 2200||Computer Programming||3|
|CMSC 2205||Data Structures and Algorithms I||3|
|CMSC 3270||Data Structures and Algorithms II||3|
|CMSC 3274||Object-Oriented Design and Programming||3|
|CMSC 3330||Database Management Systems||3|
|MATH 2210||Calculus I||4|
|MATH 2211||Calculus II||4|
|MATH 2212||Calculus III||4|
|MATH 2240||Discrete Mathematics||4|
|MATH 3300||Linear Algebra||3|
|MATH 3371||Probability and Statistics I||3|
|MATH 4373||Probability and Statistics II||3|
|MATH 4400||Data Science Capstone||3|
|Select three of the following:||9|
Grades of “C” or better are required to apply computer science and mathematics courses toward the degree.
A student cannot major or minor in both Data Science and Computer Science or Mathematics.
Students who earn a Data Science major will achieve the following student learning outcomes (SLO):
Student Learning Outcome 1: Demonstrate knowledge and understanding of the core content in mathematics.
• University SLO: 1. Disciplinary Competence and Skills
Student Learning Outcome 2: Apply mathematics to other disciplines using mathematical modeling and problem solving.
• University SLO: 5. Analytical Skills
Student Learning Outcome 3: Use technology to solve mathematical problems.
• University SLO: 4. Information Fluency
Student Learning Outcome 4: Demonstrate a Comprehensive understanding of the Java programming language
• University SLO: 1. Disciplinary Competence and Skills; 5. Analytical Skills
Student Learning Outcome 5: Demonstrate a strong understanding of algorithms
• University SLO: 1. Disciplinary Competence and Skills; 2. Critical and Creative Thinking Skills; 5. Analytical Skills
Student Learning Outcome 6: Understand how to formulate and execute a data driven decision
• University SLO: 1. Disciplinary Competence and Skills; 3. Communication Skills