Data Science

理工类, 计算机与信息技术
美国, 北美
硕士
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专业详情

The integration of data science and business strategy has created a demand for professionals who can make data-driven decisions that move their organizations forward. Students can build the essential analysis and leadership skills needed for careers in today’s data-driven world in Northwestern’s online Master of Science in Data Science program.

Northwestern Master of Science in Data Science (MSDS) students learn how to utilize relational, document, and graph database systems and analytics software built upon open-source systems such as R, Python, and Go, with TensorFlow and Keras for deep learning. They learn how to make trustworthy predictions using traditional statistics and machine learning methods.  

Work in the programming language that meets your needs

The MSDS program features three languages:  Python, R, and Go.  Students in the data science master’s program gain experience with these three languages and can tailor their studies to one language or another.

  • Python is currently the most popular computer language in data science.  It is especially strong in natural language processing and as a client to deep learning platforms.
  • R, with numerous packages for analytics and modeling, is well-regarded by applied statisticians.  It is an excellent choice for scientific programming and applied research.
  • Go is a systems programming language designed for today’s multi-processor computers.  It is well-suited for implementing scalable, high-performance systems for data science.

Learn why the master’s in data science program has incorporated the Go Programming Language.

Build critical skills for a career in data science

As a student in the master’s of data science program, you’ll learn how to:

  • Articulate analytics as a core strategy of data science
  • Transform data into actionable insights
  • Develop statistically sound and robust analytic solutions
  • Demonstrate leadership
  • Formulate and manage plans to address business issues
  • Evaluate constraints on the use of data
  • Assess data structure and data lifecycle 

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