Change the world with data — gain the skills you’ll need with a degree in data science from Valparaiso University.
Analytics and modeling focuses on the integration of knowledge and methodologies from mathematics, statistics, and computer science to analyze and solve problems in science, engineering, and other fields. As scientific and engineering fields deal with increasingly complex and expanded information and data sets, the need for individuals with such computational skills is expected to expand greatly. This 36-credit program provides students with a set of highly marketable skills applicable to many areas of science, industry, business, and government.
What makes Valpo great is the commitment of the faculty and their desire to make you better both in the classroom and in the professional world. Valpo has an unbelievably advanced and sophisticated network of professors who teach the skills that are desired in today’s workforce. The small class sizes allowed for great conversations to occur during class and also allowed for direct access to professors.
Ed Orlando '16, Data Scientist
- Core courses built around statistics, databases, and visual imaging
- Advanced coursework in computational applications, including modeling techniques
- Experience in a data-focused programming language, such as Python, R, or SAS
- Research projects and internships allowing students to apply their skills to real-world problems
- A 4+1 B.S./M.S. Early Entry program allows qualified undergraduate students at Valpo to complete the M.S. in Analytics and Modeling in one year
According to the Education Advisory Board:
- Marketing Managers
- Financial Analysts
- Computer Systems Engineers/Architects
- Business Intelligence Analysts
- Computer Programmers
And, some top employers for data analytics-related skills are:
- Amazon.com
- UnitedHealth Group
- Microsoft Corporation
- JP Morgan Chase Company
- General Electric Company
Students complete four required core courses built around statistics, databases, and visual imaging and take at least two courses (6 cr.) in computational applications in science, engineering, or other applied areas. Students also complete either an internship experience or a research project. To allow specialization, students fill out the program with elective coursework in computational science applications, mathematics, or computer science.
Core Requirements (12 credits) |
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| STAT 540 | Statistics for Decision-Making | 3 Credits |
| IT 603 | Information Management | 3 Credits |
| IT 633 | Data Mining | 3 Credits |
| CS 525 | Simulation and Modeling | 3 Credits |
Core Applications in Computational Science (6 credits) |
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| Choose minimum of two | ||
| MATH 521 | Mathematical Models of Infectious Disease | 3 Credits |
| AMOD 531 | Numerical Weather Prediction | 3 Credits |
| AMOD 560 | Computational Molecular Science | 3 Credits |
| AMOD 610 | Business Analytics | 3 Credits |
| AMOD 620 | Bioinformatics | 3 Credits |
| AMOD 640 | Topics in Biostatistics | 3 Credits |
| AMOD 650 | Computational Social Science | 3 Credits |
Experiential Training (3 credits) |
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| Choose one | ||
| AMOD 686 | Internship | 1-3 Credits |
| AMOD 792 | Research Project | 1-3 Credits |
Electives (15 credits may be selected from Core Applications or from below) |
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| AMOD 545 | Evolutionary Algorithms | 3 Credits |
| AMOD 550 | Scientific Visualization | 3 Credits |
| AMOD 563 | Introduction to SAS | 2-3 Credits |
| AMOD 565 | Interactive Computer Graphics | 3 Credits |
| AMOD 573 | Introduction to Data Science | 3 Credits |
| AMOD 590 | Topics in Analytics and Modeling | 1-3 Credits |
| AMOD 690 | Advanced Topics Analytics and Modeling | 3 Credits |
| AMOD 695 | Independent Study | 1-3 Credits |
| CS 572 | Computability and Computational Complexity | 4 Credits |
| GEO 515 | Advanced Geographic Information Systems | 3 Credits |
| IT 664 | Natural Language Technologies | 2 Credits |
| MATH 520 | Dynamical Systems | 3 Credits |
| MATH 522 | Optimization | 3 Credits |
| MATH 523 | Game Theory | 3 Credits |
| MATH 530 | Partial Differential Equations | 3 Credits |
| STAT 543 | Time Series Analysis | 3 Credits |
| STAT 544 | Applied Probability and Statistical Decision Theory | 3 Credits |
| MATH 570 | Numerical Analysis | 3 Credits |
| MATH 571 | Experimental Mathematics | 3 Credits |
Undergraduate students at Valparaiso University may complete the M.S. in computational science in one year by following a special track that ensures completion of all admission requirements and allows elective graduate computational science coursework during their senior year. As part of their undergraduate study, such students will have either:
- earned a mathematics or computer science major along with a science minor, or
- earned a minimum major in one of the natural sciences or engineering fields (e.g., astronomy, biology, biochemistry, chemistry, environmental science, meteorology, physics, or any field of engineering) and completed the follow mathematics and computer science courses with a grade of B or higher:
- MATH 131 Analytic Geometry and Calculus I (Prerequisite: Precalculus)
- MATH 264 Linear Algebra I, or equivalent
- MATH 240 Statistical Analysis, or equivalent
- CS 157 Algorithms and Programming
- CS 525 Simulation and Modeling (during their junior or senior year)
Students are also encouraged to take MATH 132 Calculus II and CS 158 (Algorithms and Abstract Data Types).
Students meeting the above requirements with a 3.2 overall GPA and a 3.0 science or engineering GPA will be guaranteed admission to the 4+1 BS/MS program. Others may be considered on an individual basis. Students interested in pursuing this track should consult with the Graduate Office and/or the computational science program director during their junior year or, at the latest, in the fall of their senior year.
Valparaiso University students pursuing the B.S./M.S. track that have completed MATH 340 or CS 325 during their undergraduate study rather than MATH 540 or CS 525 may have these core requirements waived if the course instructor or academic advisor confirms that graduate-level requirements for the courses have been successfully completed.
