# Mathematical Statistics BS

*Major: **Mathematical Statistics*

*Degree Awarded: *Bachelor of Science (BS)

*Calendar Type: Quarter*

Minimum Required Credits: 180.0

Co-op Options:* Three Co-op (Five years); One Co-op (Four years); No Co-op (Four years)*

*Classification of Instructional Programs (CIP) code:* 27.0502

*Standard Occupational Classification (SOC) code:**15-2041*

## About the Program

Statistics concerns itself primarily with the collection and analysis of data using mathematical and computational methods. It is an invaluable asset in a vast array of industries: agriculture, medicine, engineering, politics, education, pharmaceuticals, public health, the technology sector, manufacturing, media and finance all employ statisticians. From a streaming service using viewer data to determine which programs to produce, to a school district deciding if its math curriculum is working, statisticians play a key role in identifying problems and finding solutions to the same. Classical methods, for instance linear regression and principal component analysis, continue to be essential tools across many fields. Moreover, statistics is an exciting and ever-evolving subject, playing a major role in the rise of modern data science and machine learning.

Mathematical Statistics majors will learn both the theoretical grounding of modern statistical analysis and also the details of how such analysis is applied in practice across a number of industries and careers. Applied electives, drawn from classes across the University, permit students the flexibility to see how statistics is used in a field of their choosing, positioning them for a career in that area. Theoretical courses, taken in the Mathematics Department, will provide students with a deep understanding of how and why modern statistical analysis works, giving them the skills to adapt and extend existing tools to new settings along with a strong foundation to develop novel quantitative tools to tackle tomorrow’s problems.

### Additional Information

For more information about this program, contact the Department of Mathematics at mathinfo@drexel.edu.

## Degree Requirements

General Education Requirements: | ||

CIVC 101 | Introduction to Civic Engagement | 1.0 |

COM 230 | Techniques of Speaking | 3.0 |

COOP 101 | Career Management and Professional Development ^{*} | 1.0 |

ENGL 101 | Composition and Rhetoric I: Inquiry and Exploratory Research | 3.0 |

or ENGL 111 | English Composition I | |

ENGL 102 | Composition and Rhetoric II: Advanced Research and Evidence-Based Writing | 3.0 |

or ENGL 112 | English Composition II | |

ENGL 103 | Composition and Rhetoric III: Themes and Genres | 3.0 |

or ENGL 113 | English Composition III | |

UNIV S101 | The Drexel Experience | 1.0 |

UNIV S201 | Looking Forward: Academics and Careers | 1.0 |

College of Arts and Sciences Core Curriculum ^{**} | ||

Engaging the Natural World ^{**} | 6.0-8.0 | |

Analyzing Culture & Histories ^{**} | 6.0-8.0 | |

Understanding Society & Human Behavior ^{**} | 6.0-8.0 | |

Cultivating Global Competence ^{**} | 6.0-8.0 | |

Perspectives in Diversity ^{**} | 3.0-4.0 | |

Developing Quantitative Reasoning ^{^} | 6.0-8.0 | |

Computer Science sequence: | 9.0 | |

Computer Science Principles | ||

or CS 164 | Introduction to Computer Science | |

Computer Programming I | ||

Computer Programming II | ||

Any BIO, CHEM, PHYS, or PHEV course | 3.0-4.0 | |

Mathematics & Statistics required courses: | ||

MATH 121 | Calculus I ^{***} | 4.0 |

MATH 122 | Calculus II | 4.0 |

MATH 123 | Calculus III | 4.0 |

MATH 200 | Multivariate Calculus | 4.0 |

MATH 201 | Linear Algebra | 4.0 |

MATH 220 [WI] | Introduction to Mathematical Reasoning | 3.0 |

MATH 222 [WI] | Combinatorics | 3.0 |

MATH 311 | Probability and Statistics I | 4.0 |

MATH 312 | Probability and Statistics II | 4.0 |

MATH 313 | Probability and Statistics III | 3.0 |

MATH 318 [WI] | Mathematical Applications of Statistical Software | 3.0 |

MATH 401 | Elements of Modern Analysis I | 3.0 |

STAT 335 | Introduction to Experimental Design | 4.0 |

Applied Quantitative Methods course: | 3.0-4.0 | |

Select one for a minimum of 3.0 credits | ||

Research Methods & Analytics I | ||

Quantitative Research Methods in Communication | ||

Quantitative Research Methods in Political Science | ||

Research Design: Quantitative Methods | ||

Mathematics (MATH) Electives: ^{†} | 15.0 | |

Select a minimum of 15.0 credits from the following: | ||

Differential Equations | ||

Mathematics of Investment and Credit | ||

Differential Equations II | ||

Numerical Analysis I | ||

Numerical Analysis II | ||

Introduction to Optimization Theory | ||

Techniques of Data Analysis | ||

Actuarial Mathematics | ||

Vector Calculus | ||

Complex Variables | ||

Partial Differential Equations | ||

Abstract Algebra I | ||

Abstract Algebra II | ||

Linear Algebra II | ||

Elements of Modern Analysis II | ||

Introduction to Topology | ||

Mathematical Finance | ||

Introduction to Graph Theory | ||

Cryptography | ||

Introduction to Monte Carlo Methods | ||

Applied Electives: ^{‡} | 15.0 | |

Select a minimum of 15.0 credits from the following: | ||

Bioinformatics I | ||

Bioinformatics II | ||

Bioinformatics Laboratory | ||

Crime Analysis Using Open Data | ||

Research Methods and Analytics II | ||

Crime Prediction Using Open Data | ||

Crime Mapping I Using Geographic Information Systems | ||

Focus Groups | ||

Applied Deep Learning | ||

Bioinformatics | ||

Statistical Analysis of Metagenomics | ||

Using Big Data to Solve Economic and Social Problems | ||

Applied Econometrics | ||

Time Series Econometrics | ||

Experiments and Causality in Economics | ||

GIS and Environmental Modeling | ||

Advanced Environmental GIS | ||

Data Curation | ||

Data Science Programming I | ||

Data Science Programming II | ||

Information Visualization | ||

Data Mining Applications | ||

Social Media Data Analysis | ||

Linear Models for Decision Making | ||

Advanced Decision Making and Simulation | ||

Public Health Data Analysis | ||

Longitudinal Data Analysis | ||

Big Data Physics | ||

Free Electives | 39.0 | |

Total Credits | 180.0-193.0 |

- *
Co-op cycles may vary. Students are assigned a co-op cycle (fall/winter, spring/summer, summer-only) based on their co-op program (4-year, 5-year) and major.

COOP 101 registration is determined by the co-op cycle assigned and may be scheduled in a different term. Select students may be eligible to take COOP 001 in place of COOP 101.

- **
See Core Curriculum List for complete list of course options.

- ***
Math majors must pass MATH 121 with a grade of B or higher.

- †
MATH special topics courses may be substituted for Math Major Electives with departmental permission.

- ‡
At least 3 credits of these electives must be at the 400-level and another 3 credits must be either at the 300-level or the 400-level.

- ^
any required or elective MATH course taken cannot also be used to fulfill a Quantitative Reasoning Core requirement

MATH 100, MATH 101, MATH 102, MATH 110, MATH 119, MATH 180, MATH 171, MATH 172, MATH 173, and MATH 239 do not count towards the degree unless approved by the department.

### Writing-Intensive Course Requirements

In order to graduate, all students must pass three writing-intensive courses after their freshman year. Two writing-intensive courses must be in a student's major. The third can be in any discipline. Students are advised to take one writing-intensive class each year, beginning with the sophomore year, and to avoid “clustering” these courses near the end of their matriculation. Transfer students need to meet with an academic advisor to review the number of writing-intensive courses required to graduate.

A "WI" next to a course in this catalog may indicate that this course can fulfill a writing-intensive requirement. For the most up-to-date list of writing-intensive courses being offered, students should check the Writing Intensive Course List at the University Writing Program. Students scheduling their courses can also conduct a search for courses with the attribute "WI" to bring up a list of all writing-intensive courses available that term.

## Sample Plan of Study

### 4 year, no co-op

First Year | |||||||
---|---|---|---|---|---|---|---|

Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |

ENGL 101 or 111 | 3.0 | CIVC 101 | 1.0 | CS 172 | 3.0 | VACATION | |

CS 150 or 164 | 3.0 | CS 171 | 3.0 | ENGL 103 or 113 | 3.0 | ||

MATH 121 | 4.0 | ENGL 102 or 112 | 3.0 | MATH 123 | 4.0 | ||

UNIV S101 | 1.0 | MATH 122 | 4.0 | MATH 200 | 4.0 | ||

Engaging the Natural World | 3.0-4.0 | Engaging the Natural World | 3.0-4.0 | Any BIO, CHEM, PHYS, or PHEV course | 3.0-4.0 | ||

14-15 | 14-15 | 17-18 | 0 | ||||

Second Year | |||||||

Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |

COM 230 | 3.0 | MATH 201 | 4.0 | MATH 313 | 3.0 | VACATION | |

MATH 220 | 3.0 | MATH 312 | 4.0 | Applied Quantitative Methods | 3.0-4.0 | ||

MATH 311 | 4.0 | Analyzing Culture & Histories | 3.0-4.0 | Cultivating Global Competence | 3.0-4.0 | ||

UNIV S201 | 1.0 | Free Electives | 3.0 | Free Electives | 6.0 | ||

Perspectives in Diversity | 3.0-4.0 | ||||||

Free Electives | 3.0 | ||||||

17-18 | 14-15 | 15-17 | 0 | ||||

Third Year | |||||||

Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |

MATH 222 | 3.0 | MATH 318 | 3.0 | Applied Elective | 3.0 | VACATION | |

MATH 401 | 3.0 | MATH Electives | 3.0 | Cultivating Global Competence | 3.0-4.0 | ||

STAT 335 | 4.0 | Understanding Society & Human Behavior | 3.0-4.0 | MATH Elective | 3.0 | ||

Analyzing Culture & Histories | 3.0-4.0 | Free Elective | 5.0 | Free Electives | 5.0 | ||

Applied Electives | 3.0 | ||||||

16-17 | 14-15 | 14-15 | 0 | ||||

Fourth Year | |||||||

Fall | Credits | Winter | Credits | Spring | Credits | ||

Applied Electives | 3.0 | Applied Electives | 3.0 | Applied Elective | 3.0 | ||

MATH Elective | 3.0 | Developing Quantitative Reasoning | 3.0-4.0 | Developing Quantitative Reasoning | 3.0-4.0 | ||

Understanding Society & Human Behavior | 3.0-4.0 | MATH Elective | 3.0 | MATH Elective | 3.0 | ||

Free Electives | 6.0 | Free Electives | 6.0 | Free Electives | 6.0 | ||

15-16 | 15-16 | 15-16 | |||||

Total Credits 180-193 |

### 4 year, 1 co-op

First Year | |||||||
---|---|---|---|---|---|---|---|

Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |

CS 150 or 164 | 3.0 | CIVC 101 | 1.0 | CS 172 | 3.0 | VACATION | |

ENGL 101 or 111 | 3.0 | CS 171 | 3.0 | ENGL 103 or 113 | 3.0 | ||

MATH 121 | 4.0 | ENGL 102 or 112 | 3.0 | MATH 123 | 4.0 | ||

UNIV S101 | 1.0 | MATH 122 | 4.0 | MATH 200 | 4.0 | ||

Engaging the Natural World | 3.0-4.0 | Engaging the Natural World | 3.0-4.0 | Any BIO, CHEM, PHYS, or PHEV course | 3.0-4.0 | ||

14-15 | 14-15 | 17-18 | 0 | ||||

Second Year | |||||||

Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |

COM 230 | 3.0 | MATH 201 | 4.0 | COOP 101^{*} | 1.0 | Applied Elective | 3.0 |

MATH 220 | 3.0 | MATH 312 | 4.0 | MATH 313 | 3.0 | Cultivating Global Competence | 3.0-4.0 |

MATH 311 | 4.0 | Analyzing Culture & Histories | 3.0-4.0 | Applied Quantitative Methods | 3.0-4.0 | Free Electives | 9.0 |

UNIV S201 | 1.0 | Free Elective | 4.0 | Cultivating Global Competence | 3.0-4.0 | ||

Perspectives in Diversity | 3.0-4.0 | MATH Elective | 3.0 | ||||

Free Elective | 3.0 | Free Elective | 3.0 | ||||

17-18 | 15-16 | 16-18 | 15-16 | ||||

Third Year | |||||||

Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |

MATH 222 | 3.0 | MATH 318 | 3.0 | COOP EXPERIENCE | COOP EXPERIENCE | ||

MATH 401 | 3.0 | Applied Elective | 3.0 | ||||

STAT 335 | 4.0 | MATH Elective | 3.0 | ||||

Analyzing Culture & Histories | 3.0-4.0 | Understanding Society & Human Behavior | 3.0-4.0 | ||||

Free Elective | 3.0 | Free Elective | 3.0 | ||||

16-17 | 15-16 | 0 | 0 | ||||

Fourth Year | |||||||

Fall | Credits | Winter | Credits | Spring | Credits | ||

Applied Elective | 3.0 | Applied Elective | 3.0 | Applied Elective | 3.0 | ||

MATH Elective | 3.0 | Developing Quantitative Reasoning | 3.0-4.0 | Developing Quantitative Reasoning | 3.0-4.0 | ||

Understanding Society & Human Behavior | 3.0-4.0 | MATH Elective | 3.0 | MATH Elective | 3.0 | ||

Free Electives | 6.0 | Free Electives | 3.0 | Free Electives | 5.0 | ||

15-16 | 12-13 | 14-15 | |||||

Total Credits 180-193 |

- *
Co-op cycles may vary. Students are assigned a co-op cycle (fall/winter, spring/summer, summer-only) based on their co-op program (4-year, 5-year) and major.

COOP 101 registration is determined by the co-op cycle assigned and may be scheduled in a different term. Select students may be eligible to take COOP 001 in place of COOP 101.

### 5 year, 3 co-op

First Year | |||||||
---|---|---|---|---|---|---|---|

Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |

CS 150 or 164 | 3.0 | CIVC 101 | 1.0 | COOP 101^{*} | 1.0 | VACATION | |

ENGL 101 or 111 | 3.0 | CS 171 | 3.0 | CS 172 | 3.0 | ||

MATH 121 | 4.0 | ENGL 102 or 112 | 3.0 | ENGL 103 or 113 | 3.0 | ||

UNIV S101 | 1.0 | MATH 122 | 4.0 | MATH 123 | 4.0 | ||

Engaging the Natural World | 3.0-4.0 | Engaging the Natural World | 3.0-4.0 | MATH 200 | 4.0 | ||

Any BIO, CHEM, PHYS, or PHEV course | 3.0-4.0 | ||||||

14-15 | 14-15 | 18-19 | 0 | ||||

Second Year | |||||||

Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |

COM 230 | 3.0 | MATH 201 | 4.0 | COOP EXPERIENCE | COOP EXPERIENCE | ||

MATH 220 | 3.0 | MATH 312 | 4.0 | ||||

MATH 311 | 4.0 | Analyzing Culture and Histories | 3.0-4.0 | ||||

Perspectives in Diversity | 3.0-4.0 | Free Elective | 3.0 | ||||

Free Elective | 3.0 | ||||||

16-17 | 14-15 | 0 | 0 | ||||

Third Year | |||||||

Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |

MATH 222 | 3.0 | MATH 318 | 3.0 | COOP EXPERIENCE | COOP EXPERIENCE | ||

MATH 313 | 3.0 | Applied Elective | 3.0 | ||||

Analyzing Culture and Histories | 3.0-4.0 | MATH Elective | 3.0 | ||||

Applied Elective | 3.0 | Understanding Society & Human Behavior | 3.0-4.0 | ||||

Free Electives | 6.0 | Free Elective | 3.0 | ||||

18-19 | 15-16 | 0 | 0 | ||||

Fourth Year | |||||||

Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |

MATH 401 | 3.0 | Applied Quantitative Methods | 3.0-4.0 | COOP EXPERIENCE | COOP EXPERIENCE | ||

STAT 335 | 4.0 | Cultivating Global Competence | 3.0-4.0 | ||||

UNIV S201 | 1.0 | Free Electives | 8.0 | ||||

Cultivating Global Competence | 3.0-4.0 | ||||||

MATH Elective | 3.0 | ||||||

14-15 | 14-16 | 0 | 0 | ||||

Fifth Year | |||||||

Fall | Credits | Winter | Credits | Spring | Credits | ||

Applied Elective | 3.0 | Applied Elective | 3.0 | Applied Elective | 3.0 | ||

MATH Elective | 3.0 | Developing Quantitative Reasoning | 3.0-4.0 | Developing Quantitative Reasoning | 3.0-4.0 | ||

Understanding Society & Human Behavior | 3.0-4.0 | MATH Elective | 3.0 | MATH Elective | 3.0 | ||

Free Electives | 6.0 | Free Electives | 6.0 | Free Electives | 4.0 | ||

15-16 | 15-16 | 13-14 | |||||

Total Credits 180-193 |

- *
Co-op cycles may vary. Students are assigned a co-op cycle (fall/winter, spring/summer, summer-only) based on their co-op program (4-year, 5-year) and major.

COOP 101 registration is determined by the co-op cycle assigned and may be scheduled in a different term. Select students may be eligible to take COOP 001 in place of COOP 101.

## Mathematics Faculty

*(Duke University)*

*Associate Department Head, Mathematics*. Professor. Applied analysis and computing for systems of nonlinear partial differential equations, especially free-surface problems in fluid dynamics.

*(University of California at Berkeley)*. Associate Professor. Algebraic combinatorics, representation theory, and complexity theory.

*(Temple University)*. Assistant Teaching Professor.

*(University of Texas at Austin)*. Teaching Professor.

*(Drexel University)*. Associate Teaching Professor. Discrete mathematics and automata theory.

*(Drexel University)*. Associate Teaching Professor.

*(Massachusetts Institute of Technology)*. Assistant Professor. Algebraic Combinatorics, Noncommutative Algebra, Symmetric Functions, Hopf Algebras, Enumerative Combinatorics, Invariant Theory

*(Massachusetts Institute of Technology)*. Associate Professor. Intersection of physics, engineering, applied mathematics and computational science.

*(University of California at Berkeley)*. Associate Teaching Professor. Function theory and operator theory, harmonic analysis, matrix theory.

*(University of Pittsburgh)*. Associate Professor. Biomathematics, dynamical systems, ordinary and partial differential equations and math education.

*(University of Pennsylvania)*

*Undergraduate Advisor*. Professor. Geometry; optics; computer vision.

*(Warsaw University)*. Professor. Probability theory and its applications to analysis, combinatorics, wavelets, and the analysis of algorithms.

*(Duke University)*. Assistant Teaching Professor. Rare Event Simulation, Dynamical Systems, Numerical Analysis and Mathematical Biology

*(Boston University)*. Professor. Ordinary and partial differential equations, mathematical neuroscience.

*(Federal University of Rio de Janeiro)*. Assistant Professor. Analysis of Partial Differential Equations, Fluid Dynamics, Stochastic Processes

*(Rutgers University)*. Professor. Partial differential equations and numerical analysis, including homogenization theory, numerical methods for problems with rough coefficients, and inverse problems.

*(Omsk State University)*. Teaching Professor. Math education; geometrical modeling.

*(Drexel University)*. Assistant Teaching Professor.

*(University of North Carolina)*. Assistant Teaching Professor. Commutative Algebra

*(University of California at Berkeley)*. Associate Professor. Applied mathematics, numerical analysis, symbolic computation, differential geometry, mathematical physics.

*(Drexel University)*. Associate Teaching Professor.

*(University of Pennsylvania)*. Professor. Probabilistic combinatorics, asymptotic enumeration.

*(Rutgers University)*. Associate Professor. Discrete optimization, combinatorics, operations research, graph theory and its application in molecular biology, social sciences and communication networks, biostatistics.

*(Columbia University)*. Associate Professor. Partial differential equations, scientific computing and applied mathematics.

*(University of Kansas)*. Associate Professor. Stochastic Calculus, Large Deviation Theory, Theoretical Statistics, Data Network Modeling and Numerical Analysis.

*(Boston University)*. Associate Teaching Professor.

*(Physical Research Laboratory)*. Instructor.

*(Penn State University)*. Assistant Teaching Professor.

*(Vrije Universiteit, Amsterdam)*. Professor. Matrix and operator theory, systems theory, signal and image processing, and harmonic analysis.

*(Boston University)*

*Department Head*. Professor. Partial and lattice differential equations, specifically nonlinear waves and their interactions.

*(Cornell University)*. Associate Teaching Professor. Dynamical systems, neurodynamics.

*(Stanford University)*. Professor. Multiscale mathematics, wavelets, applied harmonic analysis, subdivision algorithms, nonlinear analysis, applied differential geometry and data analysis.

*(University of South Carolina)*. Assistant Teaching Professor. Functional Analysis, Operator Algebras, Semigroups, Mathematical Physics

## Emeritus Faculty

*(Polytechnic Institute of Brooklyn)*. Professor Emeritus.

*(University of Washington)*. Professor Emeritus. Functional analysis, wavelets, abstract harmonic analysis, the theory of group representations.

*(University of Pennsylvania)*. Professor Emeritus. Functional analysis, C*-algebras and the theory of group.

*(University of Pennsylvania)*. Professor Emeritus. Functional analysis, C*-algebras and group representations, computer science.

*(Temple University)*

*Dean Emeritus*. Professor Emeritus. Mathematics education, curriculum and instruction, minority engineering education.

*(Ohio State University)*. Associate Professor Emeritus. Number theory, approximation theory and special functions, combinatorics, asymptotic analysis.

*(Drexel University)*. Teaching Professor Emerita.

*(University of Pennsylvania)*. Professor Emeritus. Lie algebras; theory, applications, and computational techniques; operations research.

*(University of California at Davis)*. Professor Emeritus. Probability and statistics, biostatistics, epidemiology, mathematical demography, data analysis, computer-intensive methods.

*(University of California at Berkeley)*. Associate Professor Emerita. Applied mathematics, computed tomography, numerical analysis of function reconstruction, signal processing, combinatorics.

*(Courant Institute, New York University)*. Professor Emeritus. Applied mathematics, scattering theory, mathematical modeling in biological sciences, solar-collection systems.

*(Courant Institute, New York University)*. Professor Emeritus. Homotopy theory, operad theory, quantum mechanics, quantum computing.

*(University of Edinburgh)*. Professor Emeritus. Applied mathematics, special factors, approximation theory, numerical techniques, asymptotic analysis.

**LEARN MORE**