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100% Online Master’s Degree and Graduate Certificate in Data Science

Industry-Aligned | 100% Online | Competitive Tuition



Top 25 Best Value Online Big Data Programs 2022 –

MS in Data Science

The 100% online degree features 8 courses that can be completed in 12-16 months, with no background in coding or statistics required. The MS in Data Science program has over 125 graduates working at companies such as Capital One, IBM,, Liberty Mutual Insurance, Microsoft, Thermo Fisher Scientific, Vertex Pharmaceuticals, and more.

Graduate Certificate in Data Science

The Graduate Certificate in Data Science includes the first three “core” courses in R, Python, statistics, and data visualization. Students can start with the 12-credit certificate and apply the credits towards the 32-credit MS in Data Science program.

Potential Career Paths

Data-driven professionals with a master’s degree and advanced skill sets are in high demand, earning significantly more than those with a bachelor’s degree alone.

Data Scientist

$120,966per year

Research Scientist

$123,826per year

Senior Data Scientist

$146,210per year

Source:, Obtained September 2022

MS in Data Science

  • 100% Online
  • 12-16 Months, Part-Time
  • 8 Courses (32 credits)
  • No GMAT Required
  • $20,000 Tuition
  • Federal Financial Aid Eligible
  • 6 Start Dates Per Year

Graduate Certificate

  • 100% Online
  • 24 Weeks, Part-Time
  • 3 Courses (12 credits)
  • No GMAT Required
  • $7,500 Tuition
  • Transfer Credits to Master’s Degree
  • 6 Start Dates Per Year

MS in Data Science Curriculum


Students will become familiar with the field of data science, its applications, and use cases. Students will learn relevant statistical topics with applications in data science.

Students will learn to write and read Python and R programming codes for exploring, summarizing, and visualizing numerical, categorical, date, and text data.

Learn to use industry-leading software to “tell the story of the data” by creating graphical summaries using Tableau and interactive dashboards using R Shiny.


Fitting and validation of multivariate predictive models focused on estimation of continuous or categorical outcomes, emphasizing statistical bases of models

Automated pattern detection approaches focused on unsupervised and supervised learning, feature engineering, classification, regression, neural networks

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. operational and experiential aspects of data governance and differential privacy.


Use analytical techniques to convert social media data into marketing insights, benefits and limitation of social media listening, creation of monitors, discussion of standard social media metrics, market structure, consumers’ perception of brand.

Apply analytical techniques including multiple regression and discriminant analysis to predict player performance and team outcomes as well as business models for sports franchises.

This course is hands-on analytics of real-world healthcare datasets. Students will apply their modeling skills to real-life applications in the health care environment.


In this capstone experience, students take a problem through the full data science lifecycle using data provided by the instructor or a data set from an employer or internship. Instructor provides data and requirements. Students must complete six courses from the MSDS program before taking this capstone course.

In this capstone experience, students take a problem through the full data science lifecycle using data provided by the instructor or a data set from an employer or internship. Students provide their own data, or complete an industry internship.Students must complete six courses from the MSDS program before taking this capstone course.


Students will learn to write and read SQL and no-SQL queries. Students will develop an understanding of the design and function of relational and non-relational databases.

This course will provide students with a comprehensive understanding of the big data processing foundation and techniques. Students will understand basic concepts of parallel computing, big data, Hadoop, MapReduce, and Spark. Students will develop skills to solve big data processing problems.

Story-telling with data linked to maps, 2D and 3D spatial analysis using industry leading software ArcGIS, coordinate systems, projections, layering, raster and vector data.

Sentiment analysis with logistic regression and naïve Bayes; dynamic programming, hidden Markov models; encoding, decoding, machine translation.

*Graduate Certificate curriculum includes the three “foundational” courses.

Looking for an Advanced Data Science track? Skip the fundamentals!

Students with prior educational achievement or work experience can request a waiver of the required courses R and Python Programming and Visual Data Exploration and replace them with additional electives.

Students already working in the industry can use a real-life project to complete their capstone – approval from the program director is needed.

Discover the Merrimack Difference


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 professionally — so that you graduate as a confident, well-prepared leader.

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  • The Princeton Review 2022 Best Northeastern Regional College
  • Forbes Magazine 2019 Top 10 Most Innovative School – Regional Universities North
  • Money Magazine’s Best Colleges Most Transformative College 2019
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