Data Science

Big data. Bigger ideas.

The digitization of information—social networks, business transactions, healthcare records and more—has transformed the world, necessitating a demand for people who understand all of the big data being collected and who can take action to help individuals, corporations and organizations become smarter, faster and ultimately more effective.

Through Drew’s Master of Science and graduate certificate programs in Data Science, you will:

Learn how to obtain, prepare and manage data from a wide variety of sources.
Gain mastery of data analytical techniques.
Leave with a portfolio of projects and work experience from experiential learning projects and internships.
Focus on statistics, data science, and programming.

Faculty

Sarah Abramowitz, John H. Evans Professor of Mathematics and Computer Science

John H. Evans Professor of Mathematics and Computer Science

Email: sabramow@drew.edu
Office: HS 312
Phone: 973-408-3346

Sarah Abramowitz earned a BA in Mathematics from Cornell University, an MS in Mathematics from the State University of New York at Stony Brook, and a PhD in Mathematics Education from New York University. Dr. Abramowitz has been a professor in the Department of Mathematics and Computer Science at Drew University since 1998. She specializes in Educational Statistics. She is the co-author with Sharon Weinberg of “Statistics Using IBM SPSS: An Integrative Approach,” “Statistics Using Stata: An Integrative Approach,” “Statistics Using R: An Integrative Approach,” and serves as an Associate Editor and Social Media Editor of the Journal of Statistics Education.

Prasad Kothapalli, Adjunct Professor

Diane Liporace, Adjunct Professor

Assistant Teaching Professor of Mathematics and Computer Science

Email: dliporace@drew.edu

Diane Liporace earned a M.S. in Computer Science from Montclair State University, and received a B.S. in Mathematics with a minor in Computer Science from Mercy College. She is co-author of a research paper, Urban Legislation Assessment by Data Analytics with Smart City Characteristics. In 2017, she joined the Department of Mathematics and Computer Science at Drew University as an adjunct instructor.

Yi Lu, Assistant Professor

Norma Gilbert Junior Assistant Professor of Mathematics and Computer Science

Email: ylu1@drew.edu
Office: HS 309
Phone: 973-408-3769

Yi Lu received a Ph.D. in Statistics from the Ohio State University in 2017. She has taught a few undergraduate classes at Ohio State and worked as a statistical consultant on various research projects with graduate students from other disciplines. She studied both History and Mathematics as an undergraduate (Mars Hill University, North Carolina) and enjoys using statistics in very diverse applications. Her current research interests include Bayesian methods, functional data, and curves and images. She recently moved to New Jersey and loves running in her spare time.

Elizabeth Pemberton, Adjunct Professor

Email: epemberton@drew.edu
Website: elizabeth-pemberton.com

Elizabeth Pemberton received a BA in Physics from Drew University and an MS in Applied Mathematics and Statistics from Stony Brook University. Elizabeth is currently a PhD Candidate in Data Science at Northcentral University. Her research is in the field of computational content analysis and focuses on using a combination of network analysis, natural language processing, and computer vision to programmatically evaluate representation across hundreds of films from the past 40 years. Elizabeth also has five years of experience as a full-time data scientist and currently works at a New York City startup as a senior data scientist. When she’s not thinking about the ways she can use data science to quantify the realities of our world, Elizabeth enjoys playing video games on her playstation, building 3D puzzles, and reading science fiction.

Alexander Rudniy, Assistant Professor

Alex Rudniy earned his PhD in Computer Science from New Jersey Institute of Technology in 2012.Before joining Drew, he taught courses at University of Scranton and Fairleigh Dickinson University. He earned his MS and BS in Applied Mathematics at National University of Radio Electronics, Ukraine. Dr. Rudniy’s research interests are in artificial intelligence, machine learning, natural language processing, time series forecasting, cybersecurity and the amalgamation of the above. Dr. Rudniy enjoys spending time outdoors doing hiking, biking, kayaking, swimming, or fishing.

Ellie Small, Assistant Professor, Program Director

Norma Gilbert Junior Assistant Professor of Mathematics and Computer Science

Email: esmall1@drew.edu
Office: HS 304
Phone: 973-408-3406
Website: elliesmall.org

Ellie Small received a BSc degree in Mathematics with Statistics and Computer Science from the University of London, Birkbeck College. She earned her PhD in Statistics from Rutgers University, New Brunswick, in 2019, and joined the Department of Mathematics and Computer Science at Drew University that same year. Dr. Small has taught statistics and mathematics at Centenary University in Hackettstown for 6 years. She specializes in data science and has completed research papers in networks and text mining. In her spare time she enjoys any type of dance, and she goes ballroom dancing with her husband whenever the opportunity presents itself.

Drew’s Data Science programs are structured to focus on the intersections of statistics, computer science, skills and technologies. ”

Programs

Both our 12-credit certificate programs and our 30-36 credit Master of Science degree will prepare you to become an advanced analyst who can create and develop data sets and analytic protocols, communicate your analytic findings to leaders and general audiences and ultimately participate in data-informed decision making.

The examples for the course of studies below are for full-time programs, but part-time options are also available.

Master of Science (30-36 credits, one year full-time)

Foundational courses (6 credits)
Must be taken prior to enrolling in core courses. May be taken during the summer before the program starts.

Introductory Statistics (or equivalent)
Introduction to Programming (or equivalent)

Fall (12 credits)

Statistics Using R (3 credits)
Computational Thinking/Programming in Python (3 credits)
Applied Regression Analysis (3 credits)
Elective (3 credits)

Spring (12 credits)

Networks and Text Mining (3 credits)
Data Science Using SQL and Relational Databases (3 credits)
Statistical Machine Learning (3 credits)
Data Visualization and Communication (3 credits)

Summer (6 credits)

Data Science Internship (3 credits)
Capstone: Case studies in Data Science (3 credits)

View the Course Catalog for more details.

Master of Science (30-36 credits - two years part-time - fall start)

Foundational courses (6 credits)
Must be taken prior to enrolling in core courses. May be taken during the summer before the program starts.

Introductory Statistics (or equivalent)
Introduction to Programming (or equivalent)

Fall I (6 credits)

Statistics Using R (3 credits)
Computational Thinking/Programming in Python (3 credits)

Spring I (6 credits)

Networks and Text Mining (3 credits)
Data Science Using SQL and Relational Databases (3 credits)

Fall II (6 credits)

Applied Regression Analysis (3 credits)
Elective (3 credits)

Spring II (6 credits)

Statistical Machine Learning (3 credits)
Data Visualization and Communication (3 credits)

Summer (6 credits)

Data Science Internship (3 credits)
Capstone: Cases studies in Data Science (3 credits)

View the Course Catalog for more details.

Master of Science (30-36 credits - two years part-time - spring start)

Foundational courses (6 credits)
Must be taken prior to enrolling in core courses. May be taken during the summer before the program starts.

Introductory Statistics (or equivalent)
Introduction to Programming (or equivalent)

Spring I (6 credits)

Data Science Using SQL and Relational Databases (3 credits)
Data Visualization and Communication (3 credits)

Fall I (9 credits)

Statistics Using R (3 credits)
Computational Thinking/Programming in Python (3 credits)
Applied Regression Analysis (3 credits)

Spring II (6 credits)

Networks and Text Mining (3 credits)
Statistical Machine Learning (3 credits)

Summer (6 credits)

Data Science Internship (3 credits)
Capstone: Case studies in Data Science (3 credits)

Fall II (3 credits)

Elective (3 credits)

View the Course Catalog for more details.

Graduate Certificates (12 credits each)

Our graduate certificates enhance core skills required to draw information from data. You may choose from one of three emphases: Statistics, Data Science or Business Analytics. Or, design your own program in consultation with the program director. If you complete one of these certificate programs, you may apply the credits to the Master of Science in Data Science.

Prerequisites (6 credits)
Must be taken prior to enrolling in a Data Science certificate program. May be taken during the summer before the program starts.

Introductory Statistics (or equivalent)
Introduction to Programming (or equivalent)

Data Science Certificate: Statistics (12 credits)

Statistics Using R (3 credits)
Applied Regression Analysis (3 credits)
Computational Thinking/Programming in Python (3 credits)
Statistical Machine Learning (3 credits)

View the Course Catalog for more details.

Data Science Certificate: Data Science (12 credits)

Statistics Using R (3 credits)
Networks and Text Mining (3 credits)
Computational Thinking/Programming in Python (3 credits)
Data Analytics Using SQL and Relational Databases (3 credits)

View the Course Catalog for more details.

Data Science Certificate: Business Analytics (12 credits)

Statistics Using R (3 credits)
Applied Regression Analysis (3 credits)
Financial Quantitative Analysis (3 credits)
Computational Finance and Large Data Analysis (3 credits)

View the Course Catalog for more details.

Outcomes

With the digital transformation of academia and the workforce, it is not surprising that data science and data analytics jobs are projected to grow at some of the fastest rates in the near future.*

Job title	Average salary	Projected five-year growth
Analytics managers	$105,909	15%
Data scientists & advanced analysts	$94,576	28%
Data-driven decision makers	$91,467	14%
Data systems developers	$78,553	15%
Data analysts	$78,553	16%
Functional analysts	$69,162	17%