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Curriculum

Data Science

Curriculum and Specializations

The Master of Science in Data Science program requires the successful completion of 12 courses to obtain a degree. These requirements cover six core courses, a leadership or project management course, two required courses corresponding to a declared specialization, two electives, and a capstone project or thesis. A specialization may be declared as part of the application process or may be declared at any time during a student’s tenure in the program. Students also have the option of choosing a general data science curriculum with no declared specialization. Current students should refer to curriculum requirements in place at time of entry into the program.

Analytics and Modeling Specialization

In the world of data science, the analysts and modelers specialize in testing real-world predictions about data. Data analysts and modelers conduct research and take complex factors into account to build predictive models and create forecasts upon which data-driven decisions can be made. With a focus on traditional methods of applied statistics, this specialization prepares data scientists to utilize algorithms for predictive modeling and analytics, developing models for marketing, finance, and other business applications.

  • MSDS 410-DL Regression Analysis and Multivariate Methods
  • MSDS 411-DL Generalized Linear Models

Data Engineering Specialization

After analysts and modelers have built and tested models, data engineers implement models to scale within an information infrastructure, creating systems and workflows to organize and manage large quantities of data. This means understanding computer systems (including software, hardware, data collection, and data processes) and solving problems related to data collection, security, and organization. This specialization trains data scientists to utilize system-wide problem-solving skills, choose hardware systems, and build software systems for implementing models made by data analysts to scale in productions systems.

  • MSDS 432-DL Foundations of Data Engineering
  • MSDS 434-DL Analytics Application Development

Analytics Management Specialization

As the strategic and tactical decisions of organizations become increasingly data-driven, analytics managers bridge the work of analysts and modelers with business operations and strategy to lead data science teams, address future business needs, identify business opportunities, and translate the work of data scientists into language that business management understands. This specialization equips data scientists with the communication and management strategies needed to be data-driven leaders who utilize models, analyses, and statistical data to improve business performance.

  • MSDS 474-DL Accounting and Finance for Analytics Managers
  • Either MSDS 475-DL Project Management or MSDS 480-DL Business Leadership and Communications*

*Students in this specialization are required to take both MSDS 475-DL and MSDS 480-DL to complete the program.

 

Artificial Intelligence Specialization

Advances in machine learning algorithms, growth in computer processing power, and access to large volumes of data make artificial intelligence possible. Recent advances flow from the development of deep learning methods, which are neural networks with many hidden layers. Artificial intelligence builds on machine learning, with computer programs performing many tasks formerly associated with human intelligence. Students in this specialization learn how to move from the traditional models of applied statistics to contemporary data-adaptive models employing machine learning. Students learn how to implement solutions in computer vision, natural language processing, and software robotics.

  • MSDS 453-DL Natural Language Processing
  • MSDS 458-DL Artificial Intelligence and Deep Learning

General Data Science Track

Students seeking a less prescriptive curriculum may tailor elective coursework to their personal and professional needs. This generalist track is particularly useful for data scientists seeking employment with small businesses and smaller-scale projects, in which a single data scientist might have to serve as data analyst, data engineer, and analytics manager. Instead of two required courses and two electives, students choosing the general data science track (no specialization) are able to take four electives.

About the Final Project

As their final course in the program, students take either a master’s thesis project in an independent study format or a classroom final project class in which students integrate the knowledge they have gained in the core curriculum in a team project approved by the instructor. In both cases, students are guided by faculty in exploring the body of knowledge of data science. The master’s thesis or capstone class project count as one unit of credit.

  • MSDS 498-DL Capstone Project or
  • MSDS 590-DL Thesis Research
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