Yes! Tell me more about Merrimack’s Data Science degree!

Real Data Science. Real Impact.

Spend your class time learning applicable skills you can use on the job. The online Master of Science in Data Science degree from Merrimack College teaches students to solve problems using the latest tools and analytical methods.

Offered through a collaboration between the School of Engineering and the School of Business, this data science degree combines the engineering skills needed to function as a data scientist with the business acumen needed to translate data sets into insights stakeholders can use to make business decisions.

The projects and coursework in this program were specially designed by an industry advisory council to develop data science skills that employers need in today’s graduates. Be ready to meet the demands of a career in the hottest and fastest-growing field in America.

APPLY NOW

#10 – Top 50 Best Value Online Big Data Programs 2020 – ValueColleges.com

0%
of Data Scientists have an Advanced Degree
0
median salary for Data Scientist
#0
ranked Best Job in America

Source: Glassdoor.com, Obtained April 2017

What Skills Will You Develop?

To earn your Master of Science in Data Science degree, you will complete the 8 courses listed below for a total of 32 credit hours. These courses build on your progression throughout the program based on foundational, intermediate, and advanced levels. Our classes are offered through a collaboration between the School of Engineering and the School of Business ensure your skills in both coding/development and business are aligned as you learn.

Foundational Courses

Foundations of Data Management provides students exposure to fundamental data management skills used in modern information systems that support various operational and functional areas within a business organization. Topics covered in the course emphasize how data are identified, organized, processed and managed, in a manner warranting being considered one of the most valuable organizational assets. The course emphasizes a combination of theoretical ‘why’ and experiential ‘how’ within the confines of relational data modeling and querying. It is intended to help students develop fundamental data management skills that are essential to succeeding in subsequent courses.

This course provides students with foundational knowledge of descriptive and inferential statistics. The scope of this course is limited to univariate and bivariate statistics that are commonly used to conduct basic analyses of data, including reading-in data, reviewing individual variables and their properties, conducting any requisite data corrections and enhancements, developing an overall descriptive baseline, and taking initial steps of discovering and testing possible relationships. Also included in the course scope is an introductory overview of the basics of probability and sampling theories. Topic-wise, students will develop an understanding of numerous core statistical notions, including variable types and the underlying measurement scales, variable distributions, sampling distributions, the Central Limit Theorem, Bayes Theorem, measures of central tendency and variance, statistical significance, hypothesis testing, tests of difference, as well as correlation and cross-tabulation.

This course equips students with foundational concepts and techniques required for telling compelling stories with large complex data sets, using static and interactive graphics to depict outcomes, networks, time and maps. The importance of visualizing information for many analysts is often overlooked or downgraded, but if the visualization is ineffective, the decision-making processes and knowledge discovery will be compromised. This is a project-based course that begins with reviewing concepts of human perception and cognition and perceptual accuracy and preferences, followed by exploration of the basics of graphic design and making a “good” graph. Students explore why some data visualizations present information effectively and others do not, all while learning to appreciate visualization as a key component of Data Scientists’ and Business Analysts’ skill set.

Intermediate Courses

This course introduces students to the fundamentals of statistical learning, framed within the confines of exploratory data analyses. Students will learn common strategies and techniques used in extracting valid and reliable insights out of available data, all framed within the combination of theoretical ‘why’ and experiential ‘how’ competencies. Building on the foundation acquired in Foundations of Data Management and Foundations of Statistical Analysis courses, the Data Exploration course immerses students in basics of statistical significance testing and story-telling focused on identification of meaningful data patterns and associations. Prerequisite: Grade of B or higher in DSA5020, Foundations of Statistical Analysis.

This course introduces students to the fundamental concepts surrounding legal rights and responsibilities associated with data capture, storage and leveraging data for decision-making. Given the very diverse mix of topics falling under this broad umbrella, the aim of the course is to provide a general overview of the applicable aspects of the US regulatory and legislative framework, and then to offer more topically-focused overview of the key notions falling within the domains of data-capture related rights and responsibilities, data governance design and management, data security and privacy, information quality, and the ethical aspects of data access, usage, and sharing.

Advanced Courses

This course offers students an introduction to the basic concepts and applications of statistical learning models. The course is designed to immerse students in statistical prediction related multivariate statistical concepts, including an in-depth discussion of commonly used types of multivariate predictive models, all focused on strengthening students’ comprehension of core statistical notions, including regression and classification, non-linear modeling, and tree-based estimation. The course focuses on hands-on applications of model fitting and evaluation using programming applications in R or Python.

This course offers students and introduction to the fundamentals conceptual, operational and experiential aspects of machine learning, or broadly defined algorithmic capability to manipulate, process, amend & analyze data using appropriate applications. This is an introductory course, designed to endow students with the foundational theoretical and experiential knowledge of automated pattern detection approaches focused on four key outcomes of categorization, prediction, identification and detection, and further framed within the confines of supervised and unsupervised learning. The course is meant to offer an overview of this highly complex and rapidly evolving field; as such, it focuses on established approaches, key developmental trends, and hands-on applications of select techniques.

Capstone

This course is the culmination of students’ learning – its intent is to offer students opportunities to apply the knowledge acquired in the program, in a directed, hands-on setting. As such, the course is a practicum built around solving applied industry data analytical problems, using available raw data. While substantial data processing, preparation, and outcome estimation guidance is offered throughout the course, the course does not encompass formal lectures, pre-determined assignments or examinations; instead, student’s performance in the Capstone course is based solely on successful completion of an assigned, student-specific project.

Discover the Merrimack Difference

100% ONLINE

Combination of self-paced delivery with live, online instructor-led sessions provides an engaging learning experience without getting to a campus for your classes.

EMPLOYER ALIGNED

Industry input ensures that the skills and programs you learn in this degree match the skills employers need in candidates.

PRACTICAL APPLICATION

Classes are taught by leading practitioners in the data science field who bring their experiences to your assignments and class projects.

TOP-RANKED

Merrimack College is recognized among the top universities in the country by U.S. News & World Report and The Princeton Review, to name a few.

Learn from Experts
in the field of Data Science and Analytics!

Meet Our Faculty

Where Can A Master in Data Science Degree Take You?

Potential career paths for graduates of the Master of Science in Data Science include:

Data Scientist

$92,089per year

Research Scientist

$77,028per year

Sr Data Analyst

$76,209per year

Source: PayScale.com, Obtained May 2017

Data Science Career Insights

BGT University
testimonial

“As a new manager with expanding responsibilities at times I’m faced with resource constraints.  The Data Science program has given me the ability to support my team by taking on some of the analytic work when we have resource constraints.  Not only am I gaining a greater understanding of analytics, but I am able to apply my new knowledge every day in the workplace.”

WILL LINDSEY, Manager of Analytics, Blue Cross and Blue Shield of North Carolina

Yes! Tell me more about Merrimack’s Data Science degree!

MERRIMACK COLLEGE ACCOLADES

At Merrimack College, we’re proud of our long history of providing quality degrees to students entering the job market. Our faculty are more than just teachers. We are committed to helping you grow — academically, personally and spiritually — so that you may graduate as a confident, well-prepared citizen of the world.

  • #10 Top 50 Best Value Online Big Data Programs 2020 by ValueColleges.com
  • U.S. News & World Report 2022 #34 Best Regional Universities North
  • U.S. News & World Report 2022 #41 Best Value Schools
  • U.S. News & World Report 2022 #3 Most Innovative Schools
  • Money Magazine’s Best Colleges 2020
  • The Princeton Review 2021 Best Northeastern Regional College
  • Apple Inc., Apple Distinguished School