Curriculum

M.S. in Data Science

Program Benefits & Overview

Curriculum

Faculty Experts

Industry News

M.S. in Data Science Curriculum and Outcomes

The Master of Science in Data Science (MSDS) equips students with the essential hard and soft skills that are needed to start and build a successful career in the field of data science.

Hard Skills

  • Coding for Data Manipulation and Statistical Analysis
  • Data Cleansing
  • Predictive Modeling
  • Machine Learning
  • Artificial Intelligence

Soft Skills

  • Problem Formulation
  • Data Governance
  • Data Privacy
  • Data Ethics
  • Data Visualization
  • Data-driven Storytelling (turn data into actionable insights)

Industry Tools:

  • R
  • RStudio
  • Python
  • Keras
  • TensorFlow
  • Tableau
  • R Shiny
  • SQL
  • NoSQL
  • Hadoop
  • Spark
  • DataBricks

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Curriculum

Foundational Core (3 Courses)

If you do not have experience in statistics or coding, the foundational coursework gives you the framework needed to advance to more complex data modeling concepts. In addition, Merrimack offers free Bootcamps in R, Python, SQL, Tableau, and a review of Statistics.

Students with prior experience will be automatically considered for waiver of the first two courses to replace them with additional electives.

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.

Advanced Data Science Core (3 Courses)

These courses allow students the opportunity to apply their knowledge to industry applications and gain deeper understanding in an area of interest.

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.

Application & Elective Courses (2 courses)

These courses allow students the opportunity to apply their knowledge to industry applications and gain deeper understanding in an area of interest.

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.

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.

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

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.

*Required

Learning Outcomes

1

Develop an understanding about what data science involves, what data scientists do, what types of problems data scientists solve, and what fundamental statistical techniques that are used to solve these problems.

2

Learn to use R, Python, and other coding languages to collect, explore, clean, wrangle, and summarize large data sets.

3

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

4

Understand how to fit and validate multivariate Predictive Models focused on estimation of continuous or categorical outcomes, emphasizing statistical bases of models.

5

Understand how to fit and validate multivariate Predictive Models focused on estimation of continuous or categorical outcomes, emphasizing statistical bases of models.

6

Gain insights into 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 using Bayesian statistics.

7

Apply their modeling and data analytic skills to real-life applications in their chosen area, such as the health care, life sciences, social media/marketing, sports analytics and other Big Data environments.

8

Demonstrate the ability to apply their broad data science skills by taking a problem through the full data science lifecycle, using data provided by the instructor, or a data set from an employer or internship.

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!

News & Events

Trends in Data Science

Any talk of data science trends typically features a familiar cast of characters. Articles chronicling the data science revolution explore big data, the cloud, artificial intelligence, machine learning, the internet of things, and

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