Bachelor of Science in Data Science and Health Analytics (STEM)

Massachusetts College of Pharmacy and Health Sciences (MCPHS) University - Boston Campus

USA,Massachusetts

 0 Shortlist

48 Months

Duration

CAD 39,240/year

Tuition Fee

CAD 0 FREE

Application Fee

Apply Date

USA, Massachusetts

Type: University

Location Type: Urban

Founded: 1823

Total Students: 7,502 +

Campus Detail

Main Campus Address

179 Longwood Ave, Boston, MA 02115, United States

Bachelor of Science in Data Science and Health Analytics (STEM)

Program Overview

If you are interested in working at the intersection of data science and healthcare, two rapidly growing areas, then consider this four-year, full-time program on the Boston campus. The rigorous curriculum will cover data science fundamentals such as statistical modeling, analytics, data management, and data manipulation in addition to introducing you to unique challenges and practices of the healthcare industry. At MCPHS, students learn from and with peers studying a variety of distinct, yet related disciplines, providing unique opportunities for collaboration-including through hackathons-that will position you for success. The final year of the program includes a practical experience at a health or life sciences company in the Boston area, a global hub for innovation in the sector. Graduates will be ready to transform healthcare through programming, data representation, and applied machine learning.

Learning Outcomes

Upon completion of the program, students will be able to:

  • Apply fundamental principles of health data science, including statistical modeling, data management, and data manipulation, to identify patterns and gain actionable insight to address challenges in industry, life sciences, and healthcare.
  • Design workflows and applications as solutions to complex challenges in healthcare delivery.
  • Collaborate across teams and functions to define problems, identify appropriate data, analyze sufficiency, and create visualizations to support communication and decision-making.
  • Communicate effectively about properties of computer coding, task workflows, databases, and issues with data, including sufficiency, representation, and distribution.
  • Explain machine learning, algorithms, and ethical challenges they represent in context with data science and their impacts on society.

 

 

 

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