Data Science

Data Science is an emerging field that has grown out of the tremendous growth in statistics and computer science in the past few decades. Data can help one seek to understand patterns in the world in light of inherent uncertainty. In order to study and address many of the challenges in our society, one must have the tools to process data, perform computations, summarize, investigate, and communicate important findings from the information. Data science can play a critical role in many efforts to enhance the conditions of the human person and the world.

Data Science combines courses in Computer Science, Mathematics, and Statistics to equip students with the skills needed to analyze Big Data using advanced computing and statistical methods.  Students begin with introductory classes in these areas to build their knowledge of the mathematical foundations of data science and their computational and statistical thinking skills.

Students then move into more advanced courses like Computational Tools for Data Science, Applied Regression Analysis, and Data Structures in which they learn to create complex computer-based data models.

Data Science majors complete their degrees by applying what they have learned in their Data Science Senior Seminar.

Data Science Learning Outcomes

Data Science Major Requirements

Data Science Minor Requirements

Sample Four Year Plan for Data Science Majors*

First Year
Fall Spring
MA 150 Calculus I  MA 160 Calculus II 
CS 111 Introduction to Computer Science I CS 113 Introduction to Computer Science II
First Year Seminar Liberal Studies course
Liberal Studies course Liberal Studies course
Fall Spring
MA 213 Linear Algebra MA 240 Introduction to Mathematical Proof
CS 211 Data Structures DS 202 Computational Tools for Data Science
Liberal Studies course ST 120/220 Introduction to Statistics
Elective Liberal Studies course
Fall Spring
DS 203 Introduction to Data Science ST 251 Probability
  Junior Seminar    Electives  
Fall Spring
ST 252 Mathematical Statistics DS 410 Senior Seminar in Data Science
  MA/ST/CS Elective   Electives 

Consult with your faculty advisor to discuss other ways to tailor the Data Science major to fit your academic interests and other plans.

* For students who enroll in the fall of 2019.

George Ashline, PhD

Professor of Mathematics and Statistics
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Jeanmarie Hall 261
Box 355
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M.S., Ph.D. University of Notre Dame
B.S. St. Lawrence University

Areas of Expertise:

Mathematics education and mathematics pedagogy; mathematical preparation of in-service and pre-service teachers; and complex analysis.

Courses I Teach:

  • Calculus
  • Complex Analysis (view a classroom recording)
  • History of Mathematics
  • Linear Algebra
  • Mathematics Education Seminar
  • Number Theory
  • Real Analysis

Amir Barghi, Ph.D.

Assistant Professor of Mathematics and Statistics

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Jeanmarie Hall 260
Box 211
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Ph.D. Dartmouth College
M.A. Dartmouth College
M.S. Rochester Institute of Technology
B.S. University of Tehran

Areas of Expertise and Interest:

Algebraic and probabilistic graph theory; statistics and data science; enumerative combinatorics; stochastic processes. 

Courses I Teach:

  • Elementary Statistics
  • Probability and Statistics
  • Calculus I, II & III,
  • Linear Algebra
  • Introduction to Mathematical Proofs
  • Abstract Algebra I 

Michael Battig, PhD

Professor of Computer Science
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Jeanmarie Hall 265
Box 279
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Ph.D. Mississippi State University
M.S. University of North Texas
B.S. Miami University

Areas of Expertise

Software engineering; testing object-oriented software; and computer science/information systems education.

Courses I Teach:

  • Database Management
  • Introduction to Computer Science II
  • Organization of Programming Languages
  • Software Engineering

The class I enjoy teaching most is probably Introduction to Computer Science -- I like working with first-year students and helping them to discover the breadth of the computing field.

Jo Ellis-Monaghan, PhD

Mathematics and Statistics Department Chair, Professor of Mathematics and Statistics
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Jeanmarie Hall 279
Box 285
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Ph.D. University of North Carolina, Chapel Hill
M.S. University of Vermont
B.A. Bennington College

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Areas of Expertise:

Algebraic combinatorics, especially graph polynomials, and applied graph theory in statistical mechanics, computer chip design and bioinformatics. 

Courses I Teach:

Calculus I, II, III, Applied Graph Theory, Linear Algebra, Real Analysis, Abstract Algebra, Senior Seminar

Jim Hefferon, PhD

Professor of Mathematics and Statistics

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Saint Edmund's Hall 245
Box 285
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B.S., M.S., Ph.D. University of Connecticut

My background is in the theory of computation.

Courses I Teach:

  • Calculus
  • Numerical Methods
  • Statistics
  • Theory of Computing

Zsuzsanna Kadas, PhD

Engineering Co-Director, Professor of Mathematics and Statistics
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Jeanmarie Hall 263
Box 361
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M.S., Ph.D. Rutgers University
B.S. St. John's University

Areas of Expertise:

Differential equations; nonlinear dynamics; chaos and fractals; reaction-diffusion systems; mathematical models in chemistry, physiology, population dynamics

Courses I Teach:

  • Applied Mathematics
  • Calculus I and II
  • Differential Equations
  • Discrete Mathematics

Michael Larsen, PhD

Professor of Mathematics and Statistics

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Jeanmarie Hall 259
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M.A., Ph.D. Harvard University, Statistics

B.A., Harvard College, Mathematics cum laude

Courses I Teach

Introductory Statistics, Biostatistics, Probability, Mathematical Statistics, Regression, Data Analysis, Readings and Research in Statistics, and Statistical Topics

Interests and Areas of Research

Data Analysis, Statistical modeling, Survey sampling, Missing data, Record linkage and administrative data, Bayesian methods, Hierarchical models, Confidentiality, and Teaching Statistics

Professional Experience

2018 -present, Member of Education Committee, New England Statistical Society

2017-present, Professor of Mathematics and Statistics, Saint Michael’s College

2011-present, Advisroy Editor, Chance Magazine

2009-2017, Department of Statistics and Biostatistics Center, George Washington University

2009-10, Executive Editor, Chance Magazine

2003-2009, Dept. of Statistics and Center for Survey Statistics & Methodology, Iowa State University

1999-2003, Department of Statistics and National Opinion Research Center, The University of Chicago

1997-1999, Department of Statistics, Harvard University

1996-1997, Department of Statistics, Stanford University

Various summers, Mathematical Statistician, U.S. Census Bureau

1995 – present, Statistical Consultant,


Barbara O'Donovan, MS

Instructor of Engineering and Mathematics & Statistics, Engineering Program Coordinator

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Jeanmarie Hall 255
Box 364
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B.S. in Mathematics, University of Massachusetts at Amherst
M.S in Mechanical Engineering, University of Massachusetts Amherst

Courses I Teach

Math for Social Justice, Calculus, Statics, Elements of Calculus, Introduction to Engineering

Areas of Expertise 

Mathematics and Engineering Education, Wind Energy Applications

I care deeply about my students’ learning and I want all students to feel capable and competent using mathematics in their everyday lives. In the classroom, I use a differentiated approach to instruction and strive to elicit higher order thinking skills to encourage students to use critical thinking to develop problem solving strategies. Being comfortable with mathematics in our ever-changing and high-tech world is essential!

Greta Pangborn, PhD

Computer Science and Information Systems Department Chair, Associate Professor of Computer Science
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Jeanmarie Hall 257
Box 363
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B.S., M.S., Ph.D. Cornell University

Areas of Expertise:

Computational optimization and algorithms. Recent applications I have  looked at include: self-assembling DNA nanostructures, VLSI chip layout, and unit rectangle visibility graphs.

Courses I Teach:

  • Data Structures & Algorithms
  • eCommerce
  • Introduction to Computing
  • Machine Organization
  • Programming Languages for Information Systems

My Saint Michael's:

I am always struck by the number of Saint Michael's students who participate in volunteer activities to make a difference both locally and globally, and I really appreciate the strong sense of community. My classes are small, so I am able to get to know my students well. We are able to have events, such as class dinners, that would not be possible at a larger institution. There also are many independent study and student research opportunities available that might not be possible at a larger institution. In my five first years at Saint Michael's I have been able to work with 10 students on projects beyond the scope of an ordinary class.

My students are smart, hardworking, and friendly. I am always impressed, not just by their performance in my classes (which is very good), but by the wide range of their interests beyond the field of computer science.

I really enjoy all of my classes, but if pressed to pick a favorite I would say Data Structures and Algorithms, which is closely tied to my area of research.

Lloyd Simons, PhD

Engineering Co-Director, Professor of Mathematics and Statistics

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Jeanmarie Hall 286
Box 369
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M.Sc., Ph.D. McGill University
B.Sc. University of British Columbia

Areas of Expertise:

Algebraic Number Theory; Algebraic K Theory

Courses I Teach:

  • Abstract Algebra
  • Calculus I,II,III
  • Linear Algebra
  • Probability and Statistics

My favorite class to teach is Calculus III. I very much like the material, which is the interplay of geometry and calculus. The power of mathematics to solve hard problems really begins to be evident in this class. And at this point, the students are for the most part very mathematically smart, motivated, and interested in the material.

My Saint Michael's:

Saint Michael's students are bright, polite, outgoing, and usually willing to learn. What more could a professor ask for?  The smaller class sizes and the relatively relaxed relationship one can have with one's students are also things I appreciate along with the overall friendly atmosphere of the students and the faculty.

John Trono, MS

Professor of Computer Science
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Jeanmarie Hall 267
Box 243
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M.S. Purdue University
B.S. University of Vermont

Areas of Expertise:

Simulation and predictive modeling; minimal perfect hashing functions; computer science education, concurrent programming using semaphores; Sidon set discovery; the MIPS architecture, analysis of algorithms and cryptography

Courses I Teach:

  • Computer Architecture
  • Crypto/Security
  • Data Communications and Networks
  • Intro to Computer Science
  • Operating Systems

My Saint Michael's:

I came to Saint Michael's College when the Computer Science department began back in 1982. I use my computer (which is not just for e-mail and searching the Web!) as a tool to solve problems that involve a significant amount of tedious calculations. Many of these problems require a mathematical model to simulate inside the computer what is happening in the real world. The computer can then be used to evaluate these "virtual worlds", and examine their ability to predict the future. The computer can also be used to help determine how realistic these models are in relation to our own physical world. In my classes, if I see that some topics are very difficult for students to learn, I try to develop some pedagogical tools to aid in their understanding, and if these are successful, I then share them with colleagues at other institutions.

Because my classes have fewer than 15 students in them, I really get to know the students fairly well each semester, and therefore, I can give them more individual help (if they need it) than if I were teaching much larger classes. The atmosphere in the classroom is also less formal, which hopefully encourages the students to feel more relaxed and comfortable asking questions or putting forth their ideas during class.

There is great unfulfilled demand in industry and government for people trained in quantitative methods in general and in Data Science in particular. Many fields increasingly utilize statistical methods and large, complex data sets in routine practice. Research across a wide spectrum of disciplines to an extent never seen before uses and relies on statistical methods together with computing skills with insight and expertise in working with Big Data. 

The career outlook for Data Science graduates is excellent. According to a 2017 study by IBM, demand for data scientists will increase 28% by 2020, with more than 60,000 job openings per year.

For more information on career paths in Data Science, visit our Alumni Spotlights.

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