Master of Science in Data Science

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

USA,Massachusetts

 0 Shortlist

24 Months

Duration

CAD 25,920/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

Master of Science in Data Science

Program Overview

The rapidly growing field of data science leverages computer science, statistics, and logical analysis to identify patterns that illuminate previously unattainable insights and inform critical decision-making. The field has become central to advances in the medical and life sciences because it allows us to expedite research into complex processes. With our strategic location in the world’s preeminent life sciences hub and our extensive range of expertise in the healthcare and life sciences realms, MCPHS can provide you with unparalleled opportunities to build a highly competitive career in a broad range of fields. Organizations focused on consumer health, technology, healthcare finance, insurance, and biotech all need data scientists. The degree may also help experienced informaticists and managers advance their careers because of their specialized skills. While a background in math and statistics is helpful, you also can prepare for this program with a course in foundational linear functions and measures of centrality. A bachelor’s degree is required for acceptance into the program.

Learning Outcomes:-

Upon completing the MS in Data Science, students will be able to:

  • Apply statistical modeling, data management, and data manipulation to support actionable insight.
  • Design advanced workflows and applications to solve complex challenges in life sciences and healthcare.
  • Lead teams across business functions to define problems, identify appropriate data, analyze sufficiency, and create visualizations to drive decision-making.
  • Communicate effectively with multiple constituencies 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.
  • Critically evaluate data integrity and sufficiency and make recommendations to resolve issues.

 

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