STEVENS ONLINE MASTER’S IN DATA SCIENCE (MSDS)

Learn the skills that unlock data’s extraordinary potential. The Stevens Online M.S. in Data Science (MSDS) offers advanced training that prepares students for careers leveraging the power of data.

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STEVENS ONLINE MASTER’S IN DATA SCIENCE PROGRAM

Learn Natural Language Processing (NLP) to help develop the next generation of AI tools

Gain expertise in machine learning to reveal patterns in large datasets

Master data visualization techniques to convey key insights to business stakeholders

Obtain predictive modeling skills to help businesses plan for the future

Get Started On Your Data Science Career Today

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Stevens Online Master’s in Data Science Degree Overview

Stevens’ online master’s in data science program equip students with the education they need to master the technical, analytical and practical skills to accelerate careers in fintech, business intelligence and analytics, database management and academia — as well as government positions requiring skills in data analysis. Our online data science students engage in challenging, rigorous coursework that fosters a deep, math-based understanding of data science in addition to technical proficiency in the leading industry tools, including:

Python and R, SQL, Hadoop, Hive, and TensorFlow

Machine learning and deep learning

Predictive modeling

Advanced statistical and optimization methods

Supervised/unsupervised learning

Neural networks

Natural language processing (NLP)

Data visualization

Courses focus on data science skills that are increasingly in demand across industries and sectors, and they are taught by industry experts who are redefining how data is used to drive impact. Our online curriculum is influenced by the corporate headquarters and bustling tech startups in New York City, just next door to our campus. No matter your location, as a remote learner you will benefit from groundbreaking insights from one of the world’s largest tech hubs.

QUICK FACTS

TERM START DATE

SUMMER 2024: May 20, 2024

OVERVIEW

  • 30 Credit Hours
  • 10 Courses
  • 100% Online
  • 2 Years or Less Completion Time*

*Total time to complete the program may vary based on the number of credits taken each semester.

100%

EMPLOYMENT

Three months after graduation, 100% of MSDS graduates in the Class of 2021 accepted job offers.1

7x

WINNER

21st Century Award for Best Practices in Distance Learning by the United States Distance Learning Association.

No. 13

IN THE NATION

Stevens was named one of the Top 20 U.S. Private Schools for Best Career Placement by The Princeton Review (2022).

No. 10

IN THE NATION

Recognized as the No. 10 Online Master’s in Data Science program in the country by UniversityHQ (2023).

1 Based on data from 50% of spring 2021 full-time program graduates.

Data Science Career Outlook

Demand for qualified data science professionals has risen significantly over the past decade. Currently, there are numerous opportunities in the field — and the U.S. Bureau of Labor Statistics (BLS) projects employment in data science to grow by 36% from now until 2031 (which equals an average of 13,500 new openings for data scientists each year). 

The states with the highest employment of data scientists include California, New York, Texas, North Carolina and Illinois — though numerous remote data science jobs are available — and salaries for many data science roles reach well into the six figures.

Related Career Fields

National employment and job postings statistics for data science careers.

Job Title
Employed
Median Annual Earnings
Job Title Computer and Information Systems Manager
Employed 557,400
Median Annual Earnings $164,070
Job Title Computer and Information Research Scientist
Employed 36,500
Median Annual Earnings $136,620
Job Title Computer Network Architect
Employed 180,200
Median Annual Earnings $126,900
Job Title Software Developer and Software Quality Assurance Analyst and Tester
Employed 1,795,300
Median Annual Earnings $124,200
Job Title Information Security Analyst
Employed 168,900
Median Annual Earnings $112,000
Job Title Data Scientist Occupations
Employed 168,900
Median Annual Earnings $103,500
Job Title Computer Systems Analyst
Employed 531,400
Median Annual Earnings $102,240

Source: U.S. Bureau of Labor Statistics, 2023.

Online Master’s in Data Science Curriculum/Concentrations

Below are the Traditional and Advanced course sequences for the M.S. in Data Science program. Students will engage in coursework on the following topics to develop skills as data scientists who can glean insights and aid in informed decision-making. The MSDS program consists of 30 credit hours, with 10 courses, and is 100% online.

TERM 1

This course provides students with the essential background in calculus and linear algebra needed to pursue the study of Data Science. Topics include limits, derivatives and integrals of (multivariable) functions; vectors and matrices; vector spaces and subspaces; norms and projections; basis and dimension; eigenvalues and eigenvectors; singular values; continuous optimization; and maps between Euclidean spaces and Jacobians. Throughout, various applications to Data Science will be considered, with hands-on numerical and coding exercises supplementing the theory.

This course provides the theoretical basis for studying the properties of modern statistical and machine learning methods. Students will learn the definitions and properties of probability spaces, random variables, distributions, expectations and limit theorems. Students will work with density functions, conditional expectations, and convergence of random variables. After successful completion of this course, students will be able to determine the probability distribution function and density of random variables/vectors, use the properties of expectations and higher-order moments in computations, and examine the appropriate convergence of random variables given specific situations. Students will also be able to apply results from probability theory to study the properties of sample statistics such as estimators.

TERM 2

This course offers an introduction to exploratory data analysis and the use of basic statistical tools. Topics will include data collection; descriptive statistics, and graphical and tabular treatment of quantitative, qualitative, and count data; detecting relations between variables; confidence intervals and hypothesis testing for one and two samples; simple and multiple linear regression; analysis of variance; design of experiments; and nonparametric methods. Selected topics, such as quality control and time series analysis, may also be included. Statistical software will be used throughout the course and statistical inference will be based on examples using real data. Students will participate in group projects of data analysis. They will be trained in the different phases of the professional statistician’s work, namely: data collection, description, analysis, testing, and presentation of the conclusions. Prerequisite: MA 540.

This course will introduce foundational ideas as well as advanced techniques in linear algebra that are employed in computational science of big data. Students will work with vector-matrix representation of various types of structured and unstructured data and how different models and processes could be understood in terms of linear algebra operations and algorithms. Efficient implementation of algorithms for high dimensional data by using Randomized Numerical Linear Algebra will be one of the focal points. Students will develop and improve their coding skills in Python and MATLAB for implementation of several algorithms. In addition, students will read past and current literature in machine learning and data science to familiarize themselves with current trends and challenges in linear algebra for solving real life problems. Prerequisites: MA 123, MA 124 or equivalent, MA 232 or equivalent, MA 222 or equivalent, and have basic knowledge of MATLAB (FE 516) or Python (FE 520).

TERM 3

The objective of this course is to introduce the students to the theory and methods of optimization used in data science. The first portion of the class focuses on elements of convex analysis and subgradient calculus for non-smooth functions, optimality conditions for differentiable and for non-smooth optimization problems, and Lagrangian duality. The main part of the class discusses numerical methods for optimization with a focus and application to problems arising in data science. Approaches to large-scale/big-data optimization include decomposition methods, design of distributed and parallel methods of optimization, as well as stochastic approximation methods. Examples of optimization models in classification, clustering, statistical learning, compressed sensing will be discussed in order to illustrate the theoretical and numerical challenges and to demonstrate the scope of applications.

An introductory course for machine learning theory, algorithms, and applications. Content aims to provide students with the knowledge to understand key elements of how to design algorithms/systems that automatically learn, improve, and accumulate knowledge with experience. Topics covered in this course include decision tree learning, neural networks, Bayesian learning, reinforcement learning, ensembling multiple learning algorithms, and various application problems. Students will be provided opportunities to simulate their algorithms in a programming language and apply them to solve real-world problems. Cross-listed with: EE 695.

TERM 4

Deep learning (DL) is a family of the most powerful and popular machine learning (ML) methods and has wide real-world applications including face recognition, machine translation, self-driving car, recommender system, and playing the Go game. This course is designed for students either with or without ML background. The course will cover fundamental ML, computer vision, and natural language problems and DL tools for solving the problems. The students will be able to use DL methods for solving real-world ML problems. The homework is mostly implementation and programming using the Python language and popular DL frameworks such as TensorFlow and Keras. Knowledge and skills in Python programming and linear algebra are strictly required. Knowledge of probability theory, statistics, and numerical analysis are recommended but not required. Knowledge of machine learning and artificial intelligence is helpful but unnecessary.

This course provides a broad and systematic introduction to time series models and their applications to modeling and prediction. It utilizes real-world examples to apply a variety of time series models and methods. After successful completion of this course, students will be able to work with stationarity and measures of dependency, time series regression, graphical analysis, trend and seasonality detection and removal, and moving-average filtering. Students will also be able to apply linear time series analysis, spectral analysis, and multivariate time series methods. Additional topics that will be covered include long-memory processes, unit root testing, volatility modeling, state space models and Kalman filtering.

TERM 5

This course uses advanced technologies, such as IBM’s Blue Mix and Google’s TensorFlow, as building blocks, allowing student teams to exercise their ingenuity to develop applications that use AI and machine learning in entirely new business application areas. The products of cognitive computing are beginning to appear in the marketplace, while so-called “deep-learning” AI applications are finding their way into healthcare, energy management, security, marketing and financial services.

The field of Big Data is emerging as one of the transformative business processes of recent times. It utilizes classic techniques from Business Intelligence & Analysis, along with new tools and processes to deal with the volume, velocity, and variety associated with big data. As they enter the workforce, a significant percentage of BIA students will be directly involved with big data either as technologists, managers, or users. This course will build on their understanding of the basic concepts of BI&A to provide them with the background to succeed in the evolving data centric world, not only from the point of view of the technologies required, but in terms of management, governance, and organization. Tools will include Hadoop, Hbase, and related software.

TERM 1

This course will provide an introduction to exploratory data analysis and the use of basic statistical tools. Topics will include: data collection; descriptive statistics, and graphical and tabular treatment of quantitative, qualitative, and count data; detecting relations between variables; confidence intervals and hypothesis testing for one and two samples; simple and multiple linear regression; analysis of variance; design of experiments; and nonparametric methods. Selected topics, such as quality control and time series analysis, may also be included. Statistical software will be used throughout the course and statistical inference will be based on examples using real data. Students will participate in group projects of data analysis. They will be trained in the different phases of the professional statistician’s work, namely: data collection, description, analysis, testing, and presentation of the conclusions. Prerequisite: MA 540.

This course will provide foundational ideas as well as advanced techniques in linear algebra that are employed in computational science of big data. Students will work with vector-matrix representation of various types of structured and unstructured data and how different models and processes could be understood in terms of linear algebra operations and algorithms. Efficient implementation of algorithms for high dimensional data by using Randomized Numerical Linear Algebra will be one of the focal points. Students will develop and improve their coding skills in Python and MATLAB for implementation of several algorithms. In addition, students will read past and current literature in machine learning and data science to familiarize themselves with current trends and challenges in linear algebra for solving real life problems. Prerequisites: MA 123, MA 124 or equivalent, MA 232 or equivalent, MA 222 or equivalent, and have basic knowledge of MATLAB (FE 516) or Python (FE 520).

TERM 2

The objective of this course is to introduce the students to the theory and methods of optimization used in data science. The first portion of the class focuses on elements of convex analysis and subgradient calculus for non-smooth functions, optimality conditions for differentiable and for non-smooth optimization problems, and Lagrangian duality. The main part of the class discusses numerical methods for optimization with a focus and application to problems arising in data science. Approaches to large-scale/big-data optimization include decomposition methods, design of distributed and parallel methods of optimization, as well as stochastic approximation methods. Examples of optimization models in classification, clustering, statistical learning, compressed sensing will be discussed in order to illustrate the theoretical and numerical challenges and to demonstrate the scope of applications.

This course will provide an introduction to machine learning theory, algorithms, and applications. Content aims to provide students with the knowledge to understand key elements of how to design algorithms/systems that automatically learn, improve, and accumulate knowledge with experience. Topics covered in this course include decision tree learning, neural networks, Bayesian learning, reinforcement learning, ensembling multiple learning algorithms, and various application problems. Students will be provided opportunities to simulate their algorithms in a programming language and apply them to solve real-world problems. Cross-listed with: EE 695.

TERM 3

Deep learning (DL) is a family of the most powerful and popular machine learning (ML) methods and has wide real-world applications including face recognition, machine translation, self-driving car, recommender system, and playing the Go game. This course is designed for students either with or without ML background. The course will cover fundamental ML, computer vision, and natural language problems and DL tools for solving the problems. The students will be able to use DL methods for solving real-world ML problems. The homework is mostly implementation and programming using the Python language and popular DL frameworks such as TensorFlow and Keras. Knowledge and skills in Python programming and linear algebra are strictly required. Probability theory, statistics, and numerical analysis are recommended by not required. Knowledge of machine learning and artificial intelligence is helpful but unnecessary.

This course provides a broad and systematic introduction to time series models and their applications to modeling and prediction. It utilizes real-world examples to apply a variety of time series models and methods. After successful completion of this course, students will be able to work with stationarity and measures of dependency, time series regression, graphical analysis, trend and seasonality detection and removal, and moving-average filtering. Students will also be able to apply linear time series analysis, spectral analysis, and multivariate time series methods. Additional topics that will be covered include long-memory processes, unit root testing, volatility modeling, state space models and Kalman filtering.

TERM 4

This course uses advanced technologies, such as IBM’s Blue Mix and Google’s TensorFlow, as building blocks, allowing student teams to exercise their ingenuity to develop applications that use AI and machine learning in entirely new business application areas. The products of cognitive computing are beginning to appear in the marketplace, while so-called “deep-learning” AI applications are finding their way into healthcare, energy management, security, marketing and financial services.

The field of Big Data is emerging as one of the transformative business processes of recent times. It utilizes classic techniques from Business Intelligence & Analysis, along with new tools and processes to deal with the volume, velocity, and variety associated with big data. As they enter the workforce, a significant percentage of BIA students will be directly involved with big data either as technologists, managers, or users. This course will build on their understanding of the basic concepts of BI&A to provide them with the background to succeed in the evolving data centric world, not only from the point of view of the technologies required, but in terms of management, governance, and organization. Tools will include Hadoop, Hbase, and related software.

TERM 5

This course will provide an introduction to dynamic programming as the most popular methodology for learning and control of dynamic stochastic systems. We discuss basic models, some theoretical results, and numerical methods for these problems. They will be developed starting from basic models of dynamical systems, through finite-horizon stochastic problems, to infinite-horizon stochastic models of fully or partially observable systems. Throughout the class, special attention will be paid to the application of dynamic programming to statistical learning. The class will include introduction to approximate dynamic programming techniques, which are used in statistical learning, such as tree-based methods for classification, Bayesian learning, among others. The concepts and methods will be illustrated by various applications including learning in stochastic networks, engineering, business, and finance. Prerequisites: MA 547, MA 623.

In this course, students will learn through hands-on experience how to extract data from the web and analyze web-scale data using distributed computing. Students will learn different analysis methods that are widely used across the range of internet companies, from start-ups to online giants like Amazon or Google. At the end of the course, students will apply these methods to answer a real scientific question.

Why Choose Stevens’ MS in Data Science For Your Online Master’s Degree in Data Science?

Stevens Institute of Technology is ranked number eight in U.S. News and World Report’s “Best Online Master’s in Computer Technology Programs” for 2023. Stevens was also ranked 13 in The Princeton Review’s “Top 20 U.S. Private Schools for Best Career Placement” (2022) and 14 on PayScale’s list of “Best Value Colleges” (2021). Stevens online master’s in data science program graduates are employed by Amazon, Disney Streaming Services, Bank of America, Two Sigma Investments and Cityblock Health. Stevens faculty members consult with companies such as Microsoft, IBM and Google, while others have won National Science Foundation CAREER awards. These elements earned Stevens seven 21st Century Awards for Best Practices in Distance Learning. 

The Stevens Online Master of Science in Data Science coursework is offered 100% online, and students can finish the program in two years or less. The Stevens M.S. in Data Science program lives in the math department — and the program’s emphasis on foundational data science mathematics ensures a deep understanding of data science that enables our students to adapt and respond to accelerated changes in technology post-graduation. Upon completion of the program, Stevens data science master’s program graduates are equipped with the expertise to analyze and interpret large datasets, glean valuable and actionable insights and support informed decision-making.

FREQUENTLY ASKED QUESTIONS

Our graduates think so. A survey of the Class of 2021 taken three months after graduation found that 100% of the Stevens online data science master’s program graduates had accepted job offers. Prospective students are often attracted to our program because they are looking to accelerate their careers, and holding an advanced degree is becoming an expectation in the data science field (according to a 2017 Burtch Works study, 49% of data scientists have a master’s degree and an additional 41% have earned a Ph.D.).

The Stevens online data science master’s program is demanding, and its requirements are the most restrictive within our portfolio. The ideal MSDS program candidate possesses professional experience in mathematics, computer science, tech or business and has a strong background in mathematics and statistics. This selectivity guarantees a student will be among a highly-proficient, high-potential cohort.

The Stevens Online Master of Science in Data Science curriculum includes coursework in Foundational Mathematics for Data Science, Probability, Statistical Methods and Numerical Linear Algebra for Big Data.

Online Master’s In Data Science (MSDS) Accreditation

Stevens Institute of Technology has been continually accredited by the Middle States Commission on Higher Education (MSCHE) since 1927. Stevens is accredited until 2027 and the next self-study evaluation is scheduled to take place during 2026-2027.

MSDS ALUMNI HAVE GONE ON TO BE EMPLOYED AT ORGANIZATIONS SUCH AS:

AMAZON

NEW YORK UNIVERSITY

DISNEY STREAMING SERVICES

BANK OF AMERICA

CITYBLOCK HEALTH

TWO SIGMA INVESTMENTS

FACULTY

Our faculty includes National Science Foundation (NSF) CAREER winners as well as researchers who consult with companies such as Microsoft, IBM, Google, Bell Labs and other top industry firms.

Professional headshot of Stevens faculty member, Michael Zabarankin

Michael Zabarankin

Associate Professor and Department Chair of the Department of Mathematical Sciences
Professional headshot of Stevens faculty member, Eduardo Bonelli

Eduardo Bonelli

Teaching Professor
Professional headshot of Stevens faculty member, Darinka Dentcheva

Darinka Dentcheva

Professor
Professional headshot of Stevens faculty member, Hadi Safari Katesari

Hadi Safari Katesari

Teaching Assistant Professor
Professional headshot of Stevens faculty member, Upendra Prasad

Upendra Prasad

Lecturer
Professional headshot of Stevens faculty member, Pedro Vilanova-Guerra

Pedro Andres Vilanova-Guerra

Teaching Assistant Professor

PROGRAM ADMISSION REQUIREMENTS

BACHELOR’S DEGREE

Minimum GPA of 3.0 from an accredited institution. Degree required to begin the program; completion not required at time of application.

TWO LETTERS OF RECOMMENDATION

Faculty members and/or professional colleagues.

STATEMENT OF PURPOSE

Optional, but strongly recommended.

ACADEMIC TRANSCRIPTS

Applicants must submit transcripts from all undergraduate and graduate institutions where credit was earned. You may submit unofficial transcripts during the application process. After admission, you will be required to submit official transcripts.

TOEFL/IELTS/DUOLINGO SCORES

Required for international students.

RESUME

Optional, but strongly recommended.

Key Dates & Deadlines

Term
Early Submit
Priority Submit
Final Submit
Start of Classes
Summer 2024
February 13, 2024
Deposit Waiver* and Application Fee Waiver Available.
March 19, 2024
Application Fee Waiver Available and Early Application Review.
April 16, 2024
May 20, 2024

*Applicants who apply by the early submit deadline and are admitted may be eligible for a $250 deposit waiver. Applicants who receive education assistance from employers or other tuition discounts are not eligible. Other eligibility conditions may apply.

TUITION*

$1,864

Per Credit (30 Credits)

$60

Application Fee

Fee waivers available

$250

Enrollment Deposit

Financial Aid

*Tuition based on fall 2023 rates effective September 2023. Tuition and fees are subject to change annually. Additional program fees may apply.

UPCOMING WEBINARS

Attendees will receive an application fee waiver.

At the Cutting Edge of Data: Stevens Online Data Science Master’s Program
Thursday March 7, 2024
07:00 PM ET
The StevensOnline Experience: Current Student Perspectives
Wednesday March 13, 2024
07:00 PM ET

Request Information

By submitting this form, I agree to be contacted via email, phone, or text to learn more about the programs at Stevens Institute of Technology. Since this program is 100% online, Stevens Institute of Technology does not offer US visas to attend.