Data Science and Analysis BS, Biology 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.
Courses required for the major may not be taken on a satisfactory/unsatisfactory basis.
The BS in Data Science and Analysis consists of a set of core courses along with an emphasis area.
|MATH 1800||Analytic Geometry and Calculus I 1||3-5|
|or MATH 1100||Basic Calculus|
|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|
|Economic Data and Statistics|
|Statistical Analysis in Criminology and Criminal Justice|
|Introduction to Probability and Statistics|
|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|
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
|BIOL 1821||Introductory Biology: Organisms and the Environment (MOTR BIOL 150L)||5|
|BIOL 1831||Introductory Biology: From Molecules to Organisms (MOTR BIOL 150L)||5|
|CHEM 1111||Introductory Chemistry I (MOTR CHEM 150L)||5|
|BIOL 4436||Applied Bioinformatics||3|
|Choose three of the following:||9|
|Principles of Biochemistry|
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
- Demonstrate an understanding of the fundamental principles of biology including the structure and functions of cells and their components, heredity and variation in populations, and evolution
- Apply statistical concepts and data science methods to analyze real-world problems in biology