Data Science and Machine Learning Major, B.S.

About the Program

The Department of Computer Sciences offers coursework leading to a Bachelor of Science in Data Science and Machine Learning. 

A degree in Data Science and Machine Learning will provide students with a strong foundation in computing and statistics to be able to pursue a career in any industry in which data is used. Students are invited to consult with department faculty concerning career options and further specialization within this field. The department recommends that students be aware of the similar, yet distinct, MSU degree offerings in Statistical Science and Computer Science.

All Majors in Data Science and Machine Learning are required to complete the required courses with a required minimum grade of “C-” and a minimum overall GPA of 2.00 in these courses. Students should note that programs differ in the minimum grade required. 

Given the interdisciplinary nature of Data Science, and the broad range of domains/industries in which data-driven insights can yield innovation, students will be encouraged to concentrate their general undergraduate elective credits within another department outside of Math and Statistics/Computer Science.

Student Outcomes

The student outcomes to be reached upon completion of this program are an extension of the CS degree requirements and aligned with the ABET criteria for “Data Science, Data Analytics, and Similarly Named Computing Programs.” The student outcomes for this degree program are: 

1. An ability to apply knowledge of computing, mathematics, and machine learning to the broad discipline of data science.
2. An ability to analyze complex datasets, and identify, define, and extract patterns and insights appropriate for problem-solving.
3. An ability to design, implement, and evaluate data-driven models, machine learning algorithms, and systems to meet desired needs.
4. An ability to communicate effectively with stakeholders to answer or solve a given problem through the use of data and modeling.
5. An understanding of professional, ethical, legal, security, and social issues and responsibilities, particularly those concerning data and machine learning models.
6. An ability to communicate effectively with a range of audiences, particularly in translating data insights to actionable recommendations.
7. An ability to analyze the local and global impact of data science solutions on individuals, organizations, and society.
8. Recognition of the need for, and an ability to engage in, continuing professional development, particularly in the rapidly evolving field of machine learning.
9. An ability to use current techniques, skills, and tools necessary for data manipulation, analysis, visualization, and machine learning practices.
10. An ability to apply statistical foundations, algorithmic principles, and machine learning theory in the modeling and design of data-driven systems, demonstrating comprehension   of the trade-offs involved in design and model choices.
11. An ability to apply design and development principles in the construction of data pipelines, preprocessing systems, and machine learning models of varying complexity.