Data Science and Analysis BS, Computer Science 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. The program recommends students take ENGL 3130,Technical Writing or ENGL 3120, Business Writing, to satisfy the Junior-Level Writing requirement. Emphasis areas may require one of these courses. 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 | ||
| Additional Required Courses | ||
| 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
| CMP SCI 2250 | Programming and Data Structures | 3 |
| CMP SCI 2261 | Object-Oriented Programming | 3 |
| CMP SCI 3130 | Design and Analysis of Algorithms | 3 |
| CMP SCI 3411 | Introduction to Data Visualization | 3 |
| CMP SCI 4151 | Introduction to Statistical Methods for Data Science | 3 |
| CMP SCI 4340 | Introduction to Machine Learning | 3 |
| MATH 1900 | Analytic Geometry and Calculus II | 5 |
| MATH 3000 | Discrete Structures | 3 |
| Choose three of the following: | 9 | |
| Web Full Stack Development | ||
| Introduction to Cyber Threats and Defense | ||
| Introduction to Intelligent Web | ||
| Introduction to Artificial Intelligence | ||
| Introduction to Evolutionary Computation | ||
| Introduction to Biological Data Science | ||
| Introduction to Deep Learning | ||
| Database Management Systems | ||
| Elementary Linear Algebra | ||
| Total Hours | 35 | |
Other Data Science courses may be included as electives with prior approval of the program coordinator.
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
- Identify and interpret the basic computational issues in problem solving
- Apply appropriate tools and techniques necessary for programming practice
- Apply statistical concepts and data science methods to analyze real-world problems using appropriate computer science processes and techniques