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 101Introduction to Civic Engagement1.0
COM 230Techniques of Speaking3.0
COOP 101Career Management and Professional Development *1.0
ENGL 101Composition and Rhetoric I: Inquiry and Exploratory Research3.0
or ENGL 111 English Composition I
ENGL 102Composition and Rhetoric II: Advanced Research and Evidence-Based Writing3.0
or ENGL 112 English Composition II
ENGL 103Composition and Rhetoric III: Themes and Genres3.0
or ENGL 113 English Composition III
UNIV S101The Drexel Experience1.0
UNIV S201Looking Forward: Academics and Careers1.0
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
Introduction to Computer Science
Computer Programming I
Computer Programming II
Any BIO, CHEM, PHYS, or PHEV course3.0-4.0
Mathematics & Statistics required courses:
MATH 121Calculus I ***4.0
MATH 122Calculus II4.0
MATH 123Calculus III4.0
MATH 200Multivariate Calculus4.0
MATH 201Linear Algebra4.0
MATH 220Introduction to Mathematical Reasoning3.0
MATH 222Combinatorics3.0
MATH 311Probability and Statistics I4.0
MATH 312Probability and Statistics II4.0
MATH 313Probability and Statistics III3.0
MATH 318Mathematical Applications of Statistical Software3.0
MATH 401Elements of Modern Analysis I3.0
STAT 335Introduction 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
Longitudinal Data Analysis
Big Data Physics
Free Electives39.0
Total Credits180.0-193.0
*

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 100MATH 101MATH 102MATH 110MATH 119MATH 180MATH 171MATH 172MATH 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

Plan of Study Grid
First Year
FallCredits
ENGL 101
Composition and Rhetoric I: Inquiry and Exploratory Research
or English Composition I
3.0
CS 150
Computer Science Principles
or Introduction to Computer Science
0.0-3.0
MATH 121 Calculus I 0.0,4.0
UNIV S101 The Drexel Experience 1.0
Engaging the Natural World 3.0-4.0
 Credits7-15
Winter
CIVC 101 Introduction to Civic Engagement 1.0
CS 171 Computer Programming I 0.0,3.0
ENGL 102
Composition and Rhetoric II: Advanced Research and Evidence-Based Writing
or English Composition II
3.0
MATH 122 Calculus II 4.0
Engaging the Natural World 3.0-4.0
 Credits11-15
Spring
CS 172 Computer Programming II 0.0,3.0
ENGL 103
Composition and Rhetoric III: Themes and Genres
or English Composition III
3.0
MATH 123 Calculus III 4.0
MATH 200 Multivariate Calculus 0.0,4.0
Any BIO, CHEM, PHYS, or PHEV course 3.0-4.0
 Credits10-18
Summer
VACATION  
 Credits0
Second Year
Fall
COM 230 Techniques of Speaking 3.0
MATH 220 Introduction to Mathematical Reasoning 3.0
MATH 311 Probability and Statistics I 0.0,4.0
UNIV S201 Looking Forward: Academics and Careers 1.0
Perspectives in Diversity 3.0-4.0
Free Electives 3.0
 Credits13-18
Winter
MATH 201 Linear Algebra 4.0
MATH 312 Probability and Statistics II 0.0,4.0
Analyzing Culture & Histories 3.0-4.0
Free Electives 3.0
 Credits10-15
Spring
MATH 313 Probability and Statistics III 3.0
Applied Quantitative Methods 3.0-4.0
Cultivating Global Competence 3.0-4.0
Free Electives 6.0
 Credits15-17
Summer
VACATION  
 Credits0
Third Year
Fall
MATH 222 Combinatorics 3.0
MATH 401 Elements of Modern Analysis I 3.0
STAT 335 Introduction to Experimental Design 4.0
Analyzing Culture & Histories 3.0-4.0
Applied Electives 3.0
 Credits16-17
Winter
MATH 318 Mathematical Applications of Statistical Software 0.0,3.0
MATH Electives 3.0
Understanding Society & Human Behavior 3.0-4.0
Free Elective 5.0
 Credits11-15
Spring
Applied Elective 3.0
Cultivating Global Competence 3.0-4.0
MATH Elective 3.0
Free Electives 5.0
 Credits14-15
Summer
VACATION  
 Credits0
Fourth Year
Fall
Applied Electives 3.0
MATH Elective 3.0
Understanding Society & Human Behavior 3.0-4.0
Free Electives 6.0
 Credits15-16
Winter
Applied Electives 3.0
Developing Quantitative Reasoning 3.0-4.0
MATH Elective 3.0
Free Electives 6.0
 Credits15-16
Spring
Applied Elective 3.0
Developing Quantitative Reasoning 3.0-4.0
MATH Elective 3.0
Free Electives 6.0
 Credits15-16
 Total Credits152-193

4 year, 1 co-op

Plan of Study Grid
First Year
FallCredits
CS 150
Computer Science Principles
or Introduction to Computer Science
0.0-3.0
ENGL 101
Composition and Rhetoric I: Inquiry and Exploratory Research
or English Composition I
3.0
MATH 121 Calculus I 0.0,4.0
UNIV S101 The Drexel Experience 1.0
Engaging the Natural World 3.0-4.0
 Credits7-15
Winter
CIVC 101 Introduction to Civic Engagement 1.0
CS 171 Computer Programming I 0.0,3.0
ENGL 102
Composition and Rhetoric II: Advanced Research and Evidence-Based Writing
or English Composition II
3.0
MATH 122 Calculus II 4.0
Engaging the Natural World 3.0-4.0
 Credits11-15
Spring
CS 172 Computer Programming II 0.0,3.0
ENGL 103
Composition and Rhetoric III: Themes and Genres
or English Composition III
3.0
MATH 123 Calculus III 4.0
MATH 200 Multivariate Calculus 0.0,4.0
Any BIO, CHEM, PHYS, or PHEV course 3.0-4.0
 Credits10-18
Summer
VACATION  
 Credits0
Second Year
Fall
COM 230 Techniques of Speaking 3.0
MATH 220 Introduction to Mathematical Reasoning 3.0
MATH 311 Probability and Statistics I 0.0,4.0
UNIV S201 Looking Forward: Academics and Careers 1.0
Perspectives in Diversity 3.0-4.0
Free Elective 3.0
 Credits13-18
Winter
MATH 201 Linear Algebra 4.0
MATH 312 Probability and Statistics II 0.0,4.0
Analyzing Culture & Histories 3.0-4.0
Free Elective 4.0
 Credits11-16
Spring
COOP 101 Career Management and Professional Development * 1.0
MATH 313 Probability and Statistics III 3.0
Applied Quantitative Methods 3.0-4.0
Cultivating Global Competence 3.0-4.0
MATH Elective 3.0
Free Elective 3.0
 Credits16-18
Summer
Applied Elective 3.0
Cultivating Global Competence 3.0-4.0
Free Electives 9.0
 Credits15-16
Third Year
Fall
MATH 222 Combinatorics 3.0
MATH 401 Elements of Modern Analysis I 3.0
STAT 335 Introduction to Experimental Design 4.0
Analyzing Culture & Histories 3.0-4.0
Free Elective 3.0
 Credits16-17
Winter
MATH 318 Mathematical Applications of Statistical Software 0.0,3.0
Applied Elective 3.0
MATH Elective 3.0
Understanding Society & Human Behavior 3.0-4.0
Free Elective 3.0
 Credits12-16
Spring
COOP EXPERIENCE  
 Credits0
Summer
COOP EXPERIENCE  
 Credits0
Fourth Year
Fall
Applied Elective 3.0
MATH Elective 3.0
Understanding Society & Human Behavior 3.0-4.0
Free Electives 6.0
 Credits15-16
Winter
Applied Elective 3.0
Developing Quantitative Reasoning 3.0-4.0
MATH Elective 3.0
Free Electives 3.0
 Credits12-13
Spring
Applied Elective 3.0
Developing Quantitative Reasoning 3.0-4.0
MATH Elective 3.0
Free Electives 5.0
 Credits14-15
 Total Credits152-193
*

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

Plan of Study Grid
First Year
FallCredits
CS 150
Computer Science Principles
or Introduction to Computer Science
3.0
ENGL 101
Composition and Rhetoric I: Inquiry and Exploratory Research
or English Composition I
3.0
MATH 121 Calculus I 4.0
UNIV S101 The Drexel Experience 1.0
Engaging the Natural World 3.0-4.0
 Credits14-15
Winter
CIVC 101 Introduction to Civic Engagement 1.0
CS 171 Computer Programming I 3.0
ENGL 102
Composition and Rhetoric II: Advanced Research and Evidence-Based Writing
or English Composition II
3.0
MATH 122 Calculus II 4.0
Engaging the Natural World 3.0-4.0
 Credits14-15
Spring
COOP 101 Career Management and Professional Development * 1.0
CS 172 Computer Programming II 3.0
ENGL 103
Composition and Rhetoric III: Themes and Genres
or English Composition III
3.0
MATH 123 Calculus III 4.0
MATH 200 Multivariate Calculus 4.0
Any BIO, CHEM, PHYS, or PHEV course 3.0-4.0
 Credits18-19
Summer
VACATION  
 Credits0
Second Year
Fall
COM 230 Techniques of Speaking 3.0
MATH 220 Introduction to Mathematical Reasoning 3.0
MATH 311 Probability and Statistics I 0.0,4.0
Perspectives in Diversity 3.0-4.0
Free Elective 3.0
 Credits12-17
Winter
MATH 201 Linear Algebra 4.0
MATH 312 Probability and Statistics II 4.0
Analyzing Culture and Histories 3.0-4.0
Free Elective 3.0
 Credits14-15
Spring
COOP EXPERIENCE  
 Credits0
Summer
COOP EXPERIENCE  
 Credits0
Third Year
Fall
MATH 222 Combinatorics 3.0
MATH 313 Probability and Statistics III 3.0
Analyzing Culture and Histories 3.0-4.0
Applied Elective 3.0
Free Electives 6.0
 Credits18-19
Winter
MATH 318 Mathematical Applications of Statistical Software 3.0
Applied Elective 3.0
MATH Elective 3.0
Understanding Society & Human Behavior 3.0-4.0
Free Elective 3.0
 Credits15-16
Spring
COOP EXPERIENCE  
 Credits0
Summer
COOP EXPERIENCE  
 Credits0
Fourth Year
Fall
MATH 401 Elements of Modern Analysis I 3.0
STAT 335 Introduction to Experimental Design 4.0
UNIV S201 Looking Forward: Academics and Careers 1.0
Cultivating Global Competence 3.0-4.0
MATH Elective 3.0
 Credits14-15
Winter
Applied Quantitative Methods 3.0-4.0
Cultivating Global Competence 3.0-4.0
Free Electives 8.0
 Credits14-16
Spring
COOP EXPERIENCE  
 Credits0
Summer
COOP EXPERIENCE  
 Credits0
Fifth Year
Fall
Applied Elective 3.0
MATH Elective 3.0
Understanding Society & Human Behavior 3.0-4.0
Free Electives 6.0
 Credits15-16
Winter
Applied Elective 3.0
Developing Quantitative Reasoning 3.0-4.0
MATH Elective 3.0
Free Electives 6.0
 Credits15-16
Spring
Applied Elective 3.0
Developing Quantitative Reasoning 3.0-4.0
MATH Elective 3.0
Free Electives 4.0
 Credits13-14
 Total Credits176-193
*

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

David M. Ambrose, PhD (Duke University). Professor. Applied analysis and computing for systems of nonlinear partial differential equations, especially free-surface problems in fluid dynamics.
Jason Aran, MS (Drexel University) Associate Department Head. Teaching Professor.
Jonah D. Blasiak, PhD (University of California at Berkeley). Associate Professor. Algebraic combinatorics, representation theory, and complexity theory.
Patricia Bobo, ASA (Temple University). Assistant Teaching Professor.
B. Cooper Boniece, PhD (Tulane University). Assistant Professor. Statistical inference (estimation, hypothesis testing, change-point detection) for high-dimensional time series and for high-frequency data.
Fernando Carreon, PhD (University of Texas at Austin). Teaching Professor.
Daryl Falco, MS (Drexel University). Associate Teaching Professor. Discrete mathematics and automata theory.
Raymond Favocci, MS (Drexel University). Associate Teaching Professor.
Ramesh Garimella, PhD (University of Toledo). Associate Teaching Professor. Functional analysis and operator theory
Darij Grinberg, PhD (Massachusetts Institute of Technology). Assistant Professor. Algebraic Combinatorics, Noncommutative Algebra, Symmetric Functions, Hopf Algebras, Enumerative Combinatorics, Invariant Theory
Pavel Grinfeld, PhD (Massachusetts Institute of Technology). Associate Professor. Intersection of physics, engineering, applied mathematics and computational science.
Anatolii Grinshpan, PhD (University of California at Berkeley). Associate Teaching Professor. Function theory and operator theory, harmonic analysis, matrix theory.
Yixin Guo, PhD (University of Pittsburgh). Associate Professor. Biomathematics, dynamical systems, ordinary and partial differential equations and math education.
R. Andrew Hicks, PhD (University of Pennsylvania). Professor. Geometry; optics; computer vision.
Pawel Hitczenko, PhD (Warsaw University). Professor. Probability theory and its applications to analysis, combinatorics, wavelets, and the analysis of algorithms.
Andrew Klimas, DA (Idaho State University). Assistant Teaching Professor.
Caitlin Klimas, DA (Idaho State University). Assistant Teaching Professor.
Jeffrey LaComb, PhD (Duke University). Assistant Teaching Professor. Rare Event Simulation, Dynamical Systems, Numerical Analysis and Mathematical Biology
Georgi S. Medvedev, PhD (Boston University). Professor. Ordinary and partial differential equations, mathematical neuroscience.
Cecilia Mondaini, PhD (Federal University of Rio de Janeiro). Associate Professor. Analysis of Partial Differential Equations, Fluid Dynamics, Stochastic Processes
Shari Moskow, PhD (Rutgers University) Undergraduate Advisor. Professor. Partial differential equations and numerical analysis, including homogenization theory, numerical methods for problems with rough coefficients, and inverse problems.
Oksana P. Odintsova, PhD (Omsk State University). Teaching Professor. Math education; geometrical modeling.
Dimitrios Papadopoulos, MS (Drexel University). Assistant Teaching Professor.
James Eldred Pascoe, PhD (University of California, San Diego). Assistant Professor. Functional analysis, complex analysis, noncommutative algebra, matrix analysis.
Ronald K. Perline, PhD (University of California at Berkeley). Associate Professor. Applied mathematics, numerical analysis, symbolic computation, differential geometry, mathematical physics.
Adam C. Rickert, MS (Drexel University). Associate Teaching Professor.
Eric Schmutz, PhD (University of Pennsylvania). Professor. Probabilistic combinatorics, asymptotic enumeration.
Li Sheng, PhD (Rutgers University). Associate Professor. Discrete optimization, combinatorics, operations research, graph theory and its application in molecular biology, social sciences and communication networks, biostatistics.
Gideon Simpson, PhD (Columbia University) Associate Department Head and Graduate Advisor. Professor. Partial differential equations, scientific computing and applied mathematics.
Xiaoming Song, PhD (University of Kansas). Associate Professor. Stochastic Calculus, Large Deviation Theory, Theoretical Statistics, Data Network Modeling and Numerical Analysis.
Jeanne M. Steuber, MS (Boston University). Associate Teaching Professor.
K. Shwetketu Virbhadra, PhD (Physical Research Laboratory). Assistant Teaching Professor.
Richard D. White, MS (Penn State University). Assistant Teaching Professor.
Hugo J. Woerdeman, PhD (Vrije Universiteit, Amsterdam). Professor. Matrix and operator theory, systems theory, signal and image processing, and harmonic analysis.
J. Douglas Wright, PhD (Boston University) Department Head. Professor. Partial and lattice differential equations, specifically nonlinear waves and their interactions.
Jungni Xiao, PhD (Hong Kong Baptist University). Assistant Professor. Inverse problems, partial differential equations, scattering theory, nonlocal operators, non-scattering phenomena.
Dennis G. Yang, PhD (Cornell University). Associate Teaching Professor. Dynamical systems, neurodynamics.
Thomas (Pok-Yin) Yu, PhD (Stanford University). Professor. Multiscale mathematics, wavelets, applied harmonic analysis, subdivision algorithms, nonlinear analysis, applied differential geometry and data analysis.
Matthew Ziemke, PhD (University of South Carolina). Associate Teaching Professor. Functional Analysis, Operator Algebras, Semigroups, Mathematical Physics

Emeritus Faculty

Howard Anton, PhD (Polytechnic Institute of Brooklyn). Professor Emeritus.
Robert P. Boyer, PhD (University of Pennsylvania). Professor Emeritus. Functional analysis, C*-algebras and the theory of group.
Robert C. Busby, PhD (University of Pennsylvania). Professor Emeritus. Functional analysis, C*-algebras and group representations, computer science.
Ewaugh Finney Fields, EdD (Temple University) Dean Emeritus. Professor Emeritus. Mathematics education, curriculum and instruction, minority engineering education.
William M.Y. Goh, PhD (Ohio State University). Associate Professor Emeritus. Number theory, approximation theory and special functions, combinatorics, asymptotic analysis.
Patricia Henry Russell, MS (Drexel University). Teaching Professor Emerita.
Marci A. Perlstadt, PhD (University of California at Berkeley). Associate Professor Emerita. Applied mathematics, computed tomography, numerical analysis of function reconstruction, signal processing, combinatorics.
Chris Rorres, PhD (Courant Institute, New York University). Professor Emeritus. Applied mathematics, scattering theory, mathematical modeling in biological sciences, solar-collection systems.
Justin R. Smith, PhD (Courant Institute, New York University). Professor Emeritus. Homotopy theory, operad theory, quantum mechanics, quantum computing.
Jet Wimp, PhD (University of Edinburgh). Professor Emeritus. Applied mathematics, special factors, approximation theory, numerical techniques, asymptotic analysis.