Data Science and Analysis BS, Mathematics Emphasis
General Education Requirements
Students must satisfy the university general education requirements. Many of the courses for the degree may be used to fulfill math proficiency, information literacy, social science, and math and life/natural sciences requirements. There is no foreign language requirement for the degree.
Satisfactory/Unsatisfactory Option
Courses required for the major may not be taken on a satisfactory/unsatisfactory basis.
Degree Requirements
The BS in Data Science and Analysis consists of a set of core courses along with an emphasis area.
| Core Course | ||
| MATH 1800 | Analytic Geometry and Calculus I 1 | 3-5 |
| or MATH 1100 | Basic Calculus | |
| Statistics Course | 3 | |
| The Introduction to Statistics course should align with the student's Discipline Emphasis Area. | ||
| Choose one of the following: | ||
| Quantitative Data Analysis in Social Science Research | ||
| Biostatistics | ||
| Economic Data and Statistics | ||
| Statistical Analysis in Criminology and Criminal Justice | ||
| Introduction to Probability and Statistics | ||
| Psychological Statistics | ||
| Political Analysis | ||
| Business Analytics and Statistics | ||
| MATH 4005 | Exploratory Data Analysis with R | 3 |
| CMP SCI 1250 | Introduction to Computing | 3 |
| CMP SCI 4200 | Python for Scientific Computing and Data Science | 3 |
| CMP SCI 4342 | Introduction to Data Mining | 3 |
| Total Hours | 18-20 | |
- 1
Students interested in the Computer Science emphasis area, the Mathematics Emphasis Area, or in taking additional mathematics courses should take MATH 1800.
Emphasis Area Requirements
| MATH 1900 | Analytic Geometry and Calculus II | 5 |
| MATH 2000 | Analytic Geometry and Calculus III | 5 |
| MATH 2450 | Elementary Linear Algebra | 3 |
| MATH 4200 | Mathematical Statistics I | 3 |
| MATH 4210 | Mathematical Statistics II | 3 |
| Choose two of the following: | 6 | |
| Introduction to High-dimensional Data Analysis | ||
| Bayesian Statistical Methods | ||
| Introduction to Statistical Computing | ||
| Introduction to Statistical Methods in Learning and Modeling | ||
| Introduction to Stochastic Processes | ||
| Total Hours | 25 | |
Learning Outcomes
Upon completion of the program, graduates will be able to:
- Apply knowledge of statistical data collection, analysis and quantitative modeling techniques
- Demonstrate proficiency in industry-standard programming languages that support data acquisition, retrieval and analysis
- Select, apply and build data-based models and visualizations to devise solutions to data science problems
- Effectively communicate technical results and recommendations in various formats to appropriate audiences
- Reformulate problems or question in relevant mathematical terms
- Solve multivariable problems which involve algebra or calculus
- Apply statistical concepts and data science methods to analyze real-world problems using appropriate mathematical processes and techniques