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  1. Programs
  2. Bachelor of Science (B.S.) Major in Mathematics

Bachelor of Science (B.S.) Major in Mathematics

Texas State University

Bachelor's DegreeAcademic

Become a contributor for free to openly demonstrate student outcomes, industry alignment & eligibility criteria.

No description available.

Credits

120 credits

Format

In-Person

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Program Pathways

Credentials this program stacks toward

No program pathways.

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Course Pathway

16 courses in this program

116 courses
US 1100
1 credits
POSI 2310
3 credits
MATH 4393
3 credits
MATH 4383
3 credits
MATH 4350
3 credits
MATH 4336
3 credits
MATH 4327
3 credits
MATH 4315
3 credits
MATH 4307
3 credits
MATH 4306
3 credits
MATH 4305
3 credits
MATH 3398
3 credits
MATH 2472
4 credits
MATH 2471
4 credits
ENG 3303
3 credits
ENG 1310
3 credits
Program Requirements

Courses required to complete this program

MATH 3398Discrete Mathematics II
3 cr
MATH 4305Advanced Probability and Statistics
3 cr
MATH 4306Fourier Series and Boundary Value Problems
3 cr
MATH 4307Modern Algebra
3 cr
MATH 4315Analysis II
3 cr
US 1100University Seminar
1 cr
MATH 2472Calculus II
4 cr
MATH 4327Introduction to Complex Analysis and Its Applications
3 cr
MATH 2471Calculus I
4 cr
MATH 4336Studies in Applied Mathematics
3 cr
ENG 1310College Writing I
3 cr
ENG 3303Technical Writing
3 cr
MATH 4350Introduction to Combinatorics
3 cr
MATH 4383Numerical Analysis II
3 cr
POSI 2310Principles of American Government
3 cr
MATH 4393Introduction to Finite Element Methods
3 cr
Program Details

Detailed information about this program

No detailed information available.

Requirements

What you need to earn this credential

No requirements listed.

Financial Aid

Eligible funding programs

No funding information available.

Scholarships

No scholarships listed.

Visit Program Website
Locations

Where this program is offered

  • Texas

    Texas

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Related Programs

Programs related to this one

No related programs.

Skills & Competencies

Skills developed through this program

Auto-populated·from O*NET via SOC 25-1022.00

Skills

MathematicsSpeakingInstructingReading ComprehensionActive ListeningCritical ThinkingLearning StrategiesMonitoring

Knowledge

MathematicsEducation and TrainingEnglish LanguageComputers and Electronics

Abilities

Mathematical ReasoningOral ExpressionNumber FacilityOral ComprehensionWritten ComprehensionDeductive ReasoningWritten ExpressionInductive ReasoningSpeech ClarityNear Vision

Tasks

  • Compile, administer, and grade examinations, or assign this work to others.
  • Evaluate and grade students' class work, assignments, and papers.
  • Prepare and deliver lectures to undergraduate or graduate students on topics such as linear algebra,
  • Hire adjunct faculty.

Technology

Computer based training softwareData base user interface and query softwareCalendar and scheduling softwareWord processing softwareAnalytical or scientific software

Tools

Carousel slide projectorsCompact digital camerasCompact disk CD playersComputer data input scannersComputer laser printersConference telephonesDesktop computersDigital calculatorsDigital video camerasDigital video disk DVD playersHandheld microphonesInteractive whiteboard controllersInteractive whiteboardsLaptop computersLaser facsimile machines

Work Values

AchievementIndependenceRecognitionWorking ConditionsRelationshipsSupport
Career Pathways

Occupations this program prepares you for

Auto-populated·from O*NET + BLS
Occupations matched to this program, with median wage, top wage, growth, and openings
SOCOccupationMethodWageGrowthOpenings
Match confidence: medium25-1022.00Mathematical Science Teachers, Postsecondarytitle_inference———
What You'll Learn

Key competencies developed through this program

Auto-populated·from NSX Competency Framework

Mastery: proficient (Level 3)(based on Bachelor's Degree)

  • Graduate and upper-division undergraduate lectures — autonomously design and deliver advanced instruction on topics such as real analysis, abstract algebra, or topology, adapting content to diverse learner backgrounds in a doctoral-granting department.
  • Comprehensive assessment systems — independently develop, administer, and evaluate a full suite of examinations, projects, and written papers that measure both procedural fluency and mathematical reasoning in a postsecondary context.
  • Non-routine student work — assess and provide substantive written feedback on graduate-level proofs, theses chapters, and research expositions, applying expert judgment about mathematical validity and communication quality.
  • Curriculum design and revision — independently lead the planning, evaluation, and revision of course content and instructional methods for a mathematics program sequence, incorporating current disciplinary and pedagogical research.
  • Interdisciplinary course materials — develop innovative syllabi and handouts that integrate computational tools, real-world applications, and primary literature to enhance student engagement and disciplinary depth.
  • Student academic advising — provide sustained mentorship and guidance to undergraduate and graduate students during office hours and advising sessions, supporting degree planning and research development in a university mathematics department.
  • Classroom discourse management — facilitate high-level seminar discussions that require students to construct, critique, and refine mathematical arguments autonomously in graduate-level course environments.
  • Learning strategy differentiation — apply evidence-based instructional approaches to address varied levels of mathematical preparation and learning needs across a heterogeneous student population.
  • Analytical and scientific software integration — incorporate tools such as Mathematica, R, or Python seamlessly into course delivery and assessment to support computational mathematics pedagogy.
  • Systems evaluation — assess the effectiveness of instructional methods and course structures by analyzing student performance trends and implementing data-informed improvements across a full academic year.

Some details on this page are auto-populated from public workforce data sources: O*NET (opens in new tab), BLS (opens in new tab), College Scorecard (opens in new tab), DOL Training Provider Results (opens in new tab), NSX (opens in new tab). Provided in partnership with LER.me Career Intelligence.

Student Outcomes

Performance metrics for this program

Auto-populated·from Scorecard + DOL
Completion Rate
50%
Placement Rate
Not reported