Mission & Goals

Mission Statement

The Department of Mathematics and Computer Science enables students to develop the ability to think critically in order to confidently and competently solve complex problems. This is accomplished in an active learning environment that incorporates self-reflection, discussion and the effective use of quantitative tools and analytical methods. The Department is intentional about cultivating an inclusive community characterized by supportive collaboration among faculty and students. We prioritize promoting a culture of learning centered around equity, ethical behavior, and open discourse. We emphasize the importance of good technical communications skills, including reading, writing and speaking, and we aim to prepare students for graduate school and careers in education, business, industry, and government.

 

Learning Outcomes and Goals

Upon graduating from Muhlenberg College, students majoring in departmental programs will be able to:

  • Demonstrate a broad-based knowledge of both the theoretical development and the practical applications of the subject matter, as well as an understanding of how the discipline changes and evolves over time.
  • Appropriately apply technology to visualize and analyze both data and theoretical concepts.
  • Use quantitative tools, analytical methods, and algorithms to solve problems.
  • Read and master technical material, write precise, appropriately detailed arguments, and articulate orally in formal and informal contexts to experts and more general audiences.
  • Collaborate effectively in diverse and inclusive teams to complete projects.
  • Use and apply tools in unfamiliar situations to contend effectively and confidently with ambiguity and uncertainty.
  • Critically examine and evaluate the ethical usages of data and technologies.
  • Apply an intellectual agility that allows them to transfer existing knowledge to other disciplines.

Mathematics majors will also be able to:

  • Write and argue effectively using the language of mathematics, including the writing of proofs. 

Computer Science majors will also be able to:

  • Explain and apply advanced concepts in multiple programming languages from diverse paradigms.
  • Read, write, test, and modify software so that others can read and modify such material.
  • Explain, analyze, design, and apply advanced data structures and algorithms.
  • Develop solutions to novel problems by self-learning concepts and technologies beyond those specifically covered in class.

 Statistics majors will also be able to:

  • Clean and organize data using appropriate statistical techniques.
  • Build, assess, refine, and interpret appropriate statistical models using statistical software.
  • Communicate statistical results clearly and concisely to both technical and non-technical audiences.

 

Analytics minors will be able to:

  • Demonstrate a broad-based knowledge of both the theoretical development and the practical applications of the subject matter, as well as an understanding of how the discipline changes and evolves over time.
  • Appropriately apply technology to visualize and analyze both data and theoretical concepts.
  • Use quantitative tools, analytical methods, and algorithms to solve problems.
  • Read and master technical material, write precise, appropriately detailed arguments, and articulate orally in formal and informal contexts to experts and more general audiences.
  • Collaborate effectively in diverse and inclusive teams to complete projects.
  • Use and apply tools in unfamiliar situations to contend effectively and confidently with ambiguity and uncertainty.
  • Apply an intellectual agility that allows them to transfer existing knowledge to other disciplines.
  • Read, write, test, and modify software so that others can read and modify such material.
  • Develop solutions to novel problems by self-learning concepts and technologies beyond those specifically covered in class.

 

Curriculum Map

Information and requirements for potential majors in Mathematics and minors in Analytics, Mathematics or Statistics can be found here.

Information and requirements for potential majors and minors in Computer Science can be found here.

 

Learning Goals Matrix

The following is a matrix giving our assessment of how each course prepares students to achieve/accomplish goals.

I = Introductory, D = Developing, M = Mastery 

 

Learning Goals Matrix

1. Demonstrate broad-based knowledge2. Appropriately apply technology3. Use quantitative tools, analytic methods and algorithms4. Read, write and articulate5. Collaborate effectively6. Contend effectively with ambiguity and uncertainty7. Examine and evaluate ethical usages8. Apply an intellectual agility9. Write and argue effectively, including proofs10. Explain/apply advanced concepts11. Read, write, test and modify software12. Explain, analyze and design advanced structures13. Develop solutions to novel problems
Math Courses
MTH 101: Topics in Mathematics I I I I I   I I I        
MTH 104: Statistical Thinking I   I I I I I I I        
MTH 105: Math for Social Justice I I I I I I I I          
MTH 114: Fundamentals of Mathematics I I I I I I I I I        
MTH 116: Symmetry & Shape: Introduction to Geometry I I I I I I   I          
MTH 119: Statistical Analysis I D D I I I I I I        
MTH 120: Discrete Mathematics I I I I I D   I I        
MTH 121: Calculus I I I I I I I   I I        
MTH 122: Calculus II I I I I I D I D I        
MTH 219: Statistical Models D D D D I D I D D        
MTH 223: Calculus III D D D D D D   D D        
MTH 240: Transition to Abstract Mathematics D D D D D D   D D        
MTH 226: Linear Algebra D D D D D D   D D        
MTH 227: Differential Equations D I D I D I   D D        

MTH 229: Data Visualization

 D M D   I D I D          
MTH 314: Applied Mathematics & Modeling M D D M M D D D D        
MTH 318: Operations Research M M M M M M   M D        
MTH 319: Predictive Statistics D M M D D M I D D        
MTH 326: Abstract Algebra M     M M M   M M        
MTH 328: Codes & Ciphers M D M D M M D   D        
MTH 331: Probability D D D D D M I D D        
MTH 332: Mathematical Statistics D M M D D M I D D        
MTH 337: Mathematical Analysis M     M M M   M M        
MTH 342: Advanced Geometry M D   M D D   M M        
MTH 345: Combinatorics & Graph Theory M D M M M D D M M        
MTH 347: Number Theory M M M D D D   M M        
MTH 353: CUE: Landmarks of Mathematics M M M M M M M M M        
MTH 975: CUE: Directed Research M M M M   D D M          
MTH 970: CUE: Mathematics Independent Study D M M     D D D          
Computer Science Courses
CSI 10x: Intro to Computer Science I I I I I I I I     I   I
CSI 111: Computer Science II D D D D I D I D   I D I D
CSI 210: Software Engineering D D D D M D D D   D D D D
CSI 220: Data Structures & Algorithms D D D D D D D D   D D M D
CSI 240: Computer Organization D D I D D I I I   D D I I
CSI 305: Database Systems M M M M M M D M   M M M M
CSI 310: Programming Languages M M M M M M D M   M M M M
CSI 326: Artificial Intelligence M M M D I D D D   M D M M
CSI 345: Web Software Development M M I D I I D D   D I I I
CSI 350: Operating Systems M M M M M M D M   M M M M
CSI 355: Computer Networks M M D I M I I I   M I D I
CSI 370: CUE: CS Seminar M M M M M M M M   M M M M
CSI 970: CS Independent Study/Research M M M M M M M M   M M M M