Bachelor of Science in Data Science and Biotechnology - Research and Development (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 Biotechnology - Research and Development (STEM)

Program Overview

If you are passionate about the intersection of data science, biotechnology, and cutting-edge research and aspire to be at the forefront of scientific discovery and contribute to advancements in healthcare and life sciences, then you should consider this four-year, full-time program on the Boston campus. The interdisciplinary curriculum blends computer science, statistics, and data analysis with biotechnology and life sciences. This unique approach equips you with the skills to bridge the gap between data-driven insights and scientific discovery. At MCPHS, you'll engage in collaborative practice, working with healthcare professionals and peers on real-world projects. Together, you'll simulate the challenges and rewards of working in a professional environment. This program also offers the opportunity for experiential learning at health or life science companies located in two of the top biotech clusters in the United States: Boston and Worcester.

Learning Outcomes

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

  • Apply fundamental principles of 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 organizational challenges.
  • 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 distributions.
  • Explain machine learning, algorithms, and ethical challenges they represent in context with data science and their impacts on society.
  • Evaluate data integrity and sufficiency and critically question analytical methods and approaches to manipulation across large data sets.
  • Describe critical insight derived from large data sets.

 

 

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