Master of Science in Applied Data Science

Developed by the School of Information Studies in conjunction with the Martin J. Whitman School of Management, the M.S. in Applied Data Science draws insights from both the field of information studies and the field of management to help students effectively apply analytical concepts to gain insight from data. The interdisciplinary curriculum fosters collaboration, problem-solving and analysis with diverse professionals. As a result, students learn practical analytical and technical skills to make data-driven decisions using data capture, management, analysis and communication.

Why Earn a Master’s in Data Science?  

Ranked as Glassdoor’s No. 1 Job of 2016, 2017 and 2018, data scientists are critical to the success of any organization. As the data science field evolves, the demand for analytics skills continues to grow. Employers are actively seeking candidates with the advanced technical expertise to make data-driven decisions.

A master’s in data science can help meet the demand in a variety of careers, including:

  • Data engineer
  • Data architect
  • Database administrator
  • Statistical programmer
  • Data analyst
  • Business intelligence analyst

Learning Outcomes

The M.S. in Applied Data Science program prepares students to use applications of data science so they can effectively collect, organize and manage data, as well as master data visualization tools to communicate results in a wide range of business situations. The interdisciplinary curriculum focuses on four key learning competencies:

Capture and Organization

Students will gain the foundational skills to collect, organize and manage data. The curriculum emphasizes database concepts, including data modeling, data normalization and data warehousing techniques. Additionally, it prepares students with strategies for managing data security, privacy, audit/control, fraud detection, backup and recovery.

Technical Analysis

The program provides students with the fundamental tools for supervised and unsupervised machine learning techniques. Students will develop programming language skills in Python and R to master the statistical and computational methods to process and synthesize large unstructured data sets. Additionally, they will build competencies in data mining, regression analysis, text mining and predictive analytics to identify patterns in data. The curriculum explores advanced concepts of machine learning, including deep learning and natural language processing (NLP) to prepare students to effectively build, tune and glean actionable insights from predictive modes.

Visualization and Communication

Students will learn how to create rich visualizations that communicate results, including identifying the optimal type of visualization to minimize viewer cognitive overload and maximize interpretability across a broad range of stakeholders. They will learn to critically assess visualizations, including interpreting and analyzing the meanings of data visualizations. Students will also gain the skills to identify appropriate audiences, adding an ethics-based perspective to their development and interpretation of visualizations.

Practical Application

Graduates of the program will be able to demonstrate data science competencies through practical application and analysis in various business environments. Students develop a portfolio of resources, demonstrations, recipes and examples of various analytical techniques while growing their specialization in one or more areas of interest. In their studies, they will consider privacy and ethical implications of post-optimal solutions in order to recommend appropriate actions and to evaluate those recommendations in light of environmental assumptions.

View M.S. in Applied Data Science courses.