Help companies give meaning to their data by earning a data science degree

The need for data scientists is expected to increase drastically over the next 10 years, and graduates of the Marian University Bachelor of Science in Data Science program leave school equipped to address the data analytic and data management needs of organizations.

Students in the BS in Data Science program are prepared to enter the workforce, having honed their abilities to collect, manage, analyze, and interpret data and communicate their results effectively. To prepare learners for a wide range of employment opportunities, we offer extensive preparation in scientific methods, programming, database management, statistics, business decision making, and ethics.

Graduates can work in a wide variety of business sectors that place emphasis on hiring individuals who can investigate the principles of data representation, management, statistical modeling, and analysis, and apply these skills to uncover patterns within Big Data. Students of the Bachelor of Science in Data Science program graduate with experience in advanced data management tools, applied statistics, and operations research techniques.

Learn to be a Data Science Expert

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    THE Best Job

    An integration of technology, business, and liberal arts courses culminating in the student’s ability to apply technology to solve real-world problems through creativity, critical thinking, and communication skills.

    In-Demand Expertise

    Internships and cooperative work experiences align academic theory and practice with on-the-job applications to enhance career pursuits.

    Resume Building Internships

    The ability to study a problem, determine which technology is appropriate for the solution, when to deploy it, and how to use it effectively and efficiently will be explored.

    What is Data Science?

    Data Science identifies patterns within data sets. Once the pattern is identified, you are able to highlight and communicate these patterns to the business or organization in a variety of industries. Or you can specialize in an area of your own interest such as sports analytics, marketing, education, business, etc. To graduate with a degree in Data Science, you will need to complete two capstone projects with a business outside of Marian.

    Jobs to Aspire to:

    • Data scientist
    • Business intelligence analyst
    • Database developer
    • Applications architect
    • Database administrator
    • Infrastructure architect
    • Data engineer
    • Enterprise architect
    • Data analytics manager
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    The Program

    Graduates of the Marian University Bachelor of Science in Data Science program develop a broad background in applied statistics, coding, and research methods and learn advanced quantitative management techniques. With an emphasis on interdisciplinary and applied learning, students will complete two hands-on internships in areas such as sports analytics, finance, manufacturing/operations research, decision science, or data engineering.

    General Education Courses

    As a bachelor’s level student, you are required to take about 30 credits of general education courses as part of the 120 credits required for a bachelor’s degree.  Gen eds are required regardless of your major.

    All students take 10.5 to 17 credits in these areas:

    • Mathematical Reasoning
    • Argumentative and Research Writing
    • Introduction to Christian Theology
    • Interpersonal Communication
    • Introduction to Ethical Reasoning
    • First Year Studies

    Core Courses

    Data science majors will take courses covering topics such as:

    • Database management
    • Research methods
    • Applied statistics for social science
    • Programming and data structures
    • Data analytics and data mining

    For more details regarding this program, view Marian’s Academic Bulletin.

    Sample Course Plan:
    Download Sample Course Plan

    Required Courses (45-47 credits):

    A course focused on building students’ presentation skills in a variety of settings including proposal presentations, sales position advocacy and specialty presentations typical in business and professional settings. Individuals and teams design professional-length presentations involving the use of visual and audio aids, written materials for the audience and computer-generated graphic presentations. Students develop proficiency in the critique and analysis of professional presentations.

    This course overviews aspects of data science using a variety of discipline perspectives and demonstrates how these disciplines intersect in data science. Exposed students to central concepts in data science, including data collection, manipulation, exploration, analysis, and communication. Explores ethical considerations in 21st century data practices.


    Exposure to the application of Data Science in the work world. As a seminar, students investigate the variety of industry applications of the tenants of data science in general social service, education, health, business, STEM, and government work.


    Study of techniques to visually communicate descriptive data and complex statistical findings to a diverse audiences using the fundamental principles of graphic design, illustration, and cognitive science. Development of skills in data interpretation and effective communication of scientific information to a diverse audience. This course covers how to best leverage computer-based static and interactive visualization methods.


    Integration and application of the knowledge, skills, and critical thinking gained throughout the Data Science curriculum. In consultation with the program director, students work as a class with an organization and execute a capstone project that demonstrates their ability to integrate the knowledge gained throughout the curriculum. Projects include a direct service learning experience to apply learning.


    Integration and application of the knowledge, skills, and critical thinking gained throughout the Data Science curriculum. In consultation with the program director, students work independently with an organization to design and execute a capstone project that demonstrates their ability to integrate the knowledge gained through the curriculum. Projects include a direct service learning experience to apply learning.


    MAT 201 Calculus I, Appropriate math placement test score or MAT 201 with a grade of C or higher

    A study of elementary probability theory, discrete and continuous random variables, the Central Limit Theorem, sampling theory, estimation, confidence intervals, and hypothesis testing.

    A continuation of MAT 304. Topics will include 1- and 2-factor Analysis of Variance, Linear, Non-linear and Multiple Regression and Correlation. Included in this discussion are analysis of residuals,
    selection of explanatory variables, and some corresponding nonparametric tests. Extensive use of computer statistical packages will be used to reinforce the course topics and objectives.


    MAT 212 Intro to Abstract Mathematics, with a grade of C or higher

    A study of vector spaces, determinants, linear transformations, matrices, linear independence and bases, systems of linear equations, and elementary linear programming techniques. The course emphasizes the application of these topics to problems selected from business, industry, and the sciences.

    An introduction to the science of psychology through a survey of the biological, intra-psychic, and social bases of behavior. Major topics include cognition, sensation and perception, motivation and emotion, personality, behavior disorders, and social elements of behavior.


    An investigation of the influences of social factors on individual behavior, the role of social cognition when people interact, interpersonal and group dynamics, and application of social-psychological research data to various situations.


    MAT 001 Basic Algebra, Appropriate math placement test score or MAT 001 with grade of C or higher

    An interdisciplinary introduction to the basic principles of data analysis with an emphasis on application. Students are expected to apply these principles to data analysis in their respective areas of study. The applied focus is on the computerized application of summary statistics, one/two/multi-sample tests, linear models, association tests, randomness/normality tests, time series comparison, quality control charts and probability distributions as used across a variety of community and organizational settings. Other techniques may be added as appropriate for specific disciplines.


    This course presents the basic principles and methods of social science research. Students are introduced to techniques for critical analysis of the professional practice literature and how, as consumers, they can incorporate research findings into practice. Students also acquire knowledge and skills for applying research with their social work practice as well as in the area of program evaluation. Students conceptualize research questions, determine appropriate designs and methodologies, and incorporate qualitative and quantitative data analysis. Professional values and ethics, as well as sensitivity to human diversity, are subsumed within the conduct of research.

    This course will introduce fundamental concepts related to the creation of data structures and programming logic in modern information systems. This course will introduce the importance data organization in computer systems; the variety of possible structures used to represent data relationships, how data structures are stored in memory, and the link between the design of data structures and programming algorithms.


    This course continues study from TEC 210. This study focuses on strategic data planning and enterprise modeling using CASE tools. Personal demonstration in the mastery of the design process acquired from earlier courses is expected. The predominant objective of this course is to design and construct a physical system using database software to implement a logical design.

    Programming in an Algebraic Programming Language, if-then-else, loops, arrays, concepts of machine language, algorithms for searching, sorting and equation-solving. (A college course in math is highly recommended before taking TEC 212).


    ( also FIN 403) This course deals with computer applications in quantitative management decision making at an advanced level. Students will utilize a variety of research tools to locate, analyze and evaluate information. It will investigate the use and application of computer technologies within organizations such as management support systems, decision support systems and executive information systems. Hands on application of front-end software, such as Microsoft Office, will be used to conceptualize, analyze, and develop technological solutions to practical business situations.

    Learning Outcomes:

    Students entering the Marian University Bachelor of Science in Data Science program will master:

    • Data research and ethics: Combine research design and data from more than one perspective; develop a methodology; and discuss relevant solutions, the associated limitations, and implications.
    • Critical thinking and problem solving: Analyze assumptions/viewpoints to carefully place evidence and perspectives to evaluate and articulate limitations.
    • Integrated communication: Organize and synthesize information by choosing formats in verbal, written, and visual language.
    • Technological dexterity: Execute procedures for accurate, reliable, and valid data collection, storage, security, transfer, and export.
    • Quantitative Reasoning: Understand and articulate the mathematical and statistical assumptions and potential consequences related to information, data, and analyses.

    Marian University is accredited by the Higher Learning Commission.

    Linda Krueger, M.S.
    Assistant Professor

    Laramie Paxton, Ph.D.
    Assistant Professor

    Starting with a foundation in the liberal arts and enhanced by the core curriculum, graduates of the Marian University Bachelor of Science in Data Science program apply the knowledge they gain in class to solving real-world problems.

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