Data Science, Major, Undergraduate Program, �鶹ɫ��Ƭ

About the Data Science Major

Data science is an interdisciplinary field of study which combines the power of mathematics, statistics, computer science, and domain specific expertise to uncover hidden insights and extract knowledge from data. Employers are actively looking for those with strong technological skills who can harness the power of large-scale data in order to enhance informed decision makings. The Data Science program at �鶹ɫ��Ƭ is a collaboration of several departments and programs, including Mathematics, Computer Science, Cybersecurity, Natural Sciences, and Business.

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Program Highlights

Student-centered engaged learning with case studies and projects
Collaborative undergraduate research opportunities
Opportunity to combine the Data Science degree with possible double majors in Applied Mathematics, Computer Science, Cyber Security, Biology/Marine Biology, Finance, Economics, and Public Health, etc.

According to the :

Median annual wage of Data Scientists in 2023: $108,020
Remarkable 36% growth in the field of data science by 2033, opening over 73,100 new job opportunities.

Data Science Degree Requirements

The Bachelor of Science (BS) in Data Science program provides students with strong theoretical and technical foundations of data science. Students learn a diverse set of skills, including data science principles, tools, and techniques across the entire data life cycle; data privacy and security; and legal and ethical considerations. Students are prepared to integrate and implement those skills to find data-driven solutions for challenging real-world problems. They learn how to ask research questions, formulate hypotheses, design experiments, implement and evaluate different mathematical and computational approaches, and present outcomes in a scientific or professional community. Students in the BS program study mathematics and computer science in depth. They also select an area of application of data science, for example, finance, public health, or political science, and take a four-course sequence in the area of their choice. Prospective graduates are well prepared for graduate school or may choose career paths in many industries in roles such as Data Scientist or Data Engineer.

Learning Outcomes

Apply principles of data science, computing, mathematics, and other relevant disciplines to identify solutions.
Describe and compare different mathematical and computational approaches utilized in the field of data science and machine learning.
Utilize appropriate tools throughout the data lifecycle, including data extraction, exploration, preprocessing, normalization, and visualization.
Construct and implement experimental designs and evaluate the outcomes.
Compare the performance of constructed models and identify the best performing model by deploying various model selection strategies.
Recognize professional responsibilities and make informed judgments based on legal and ethical principles.
Apply the latest state-of-the-art technology in the field of data science and machine learning.
Document and present project outcomes in a scientific or professional community.
Collaborate with others to accomplish a common objectives.
Identify research problems and related work in the given domain of interests.
Understand the mathematical, statistical, and computational basis of data science and machine learning techniques.

The Bachelor of Arts (BA) in Data Science program provides students with a solid foundation in data science. Students learn a diverse set of skills, including data science principles, tools, and techniques across the entire data life cycle; data privacy and security; and legal and ethical considerations. Students are prepared to integrate and implement those skills to find data-driven solutions for challenging real-world problems. They learn how to ask research questions, formulate hypotheses, design experiments, implement and evaluate different mathematical and computational approaches, and present outcomes in a scientific or professional community. Students in the BA program is exposed to the mathematics and computer science necessary for the practical application of data science. The BA program includes significant flexibility, and students are encouraged to pursue a minor or second major in an area of application of data science. Students can enroll directly in the BA program as an incoming freshman or may transfer to the program in their second year or add it as a second major. Prospective graduates are prepared for employment in many industries in roles such as Data Analyst or Quantitative Analyst or may choose to apply their knowledge of data science to their primary field of study.

Learning Outcomes

Apply principles of data science, computing, mathematics, and other relevant disciplines to identify solutions.
Describe and compare different mathematical and computational approaches utilized in the field of data science and machine learning.
Utilize appropriate tools throughout the data lifecycle, including data extraction, exploration, preprocessing, normalization, and visualization.
Construct and implement experimental designs and evaluate the outcomes.
Compare the performance of constructed models and identify the best performing model by deploying various model selection strategies.
Recognize professional responsibilities and make informed judgments based on legal and ethical principles.
Apply the latest state-of-the-art technology in the field of data science and machine learning.
Document and present project outcomes in a scientific or professional community.
Collaborate with others to accomplish a common objectives.
Identify research problems and related work in the given domain of interests.

The minor in Data Science at �鶹ɫ��Ƭ is a multidisciplinary program that prepares students to derive insights from data and identify hidden patterns and trends. Students learn basic mathematical, statistical, and computational skills and apply those theoretical and technical skills to produce data-driven solutions for challenging real-world problems. Students are exposed to the entire data life cycle, data privacy, and legal and ethical considerations while working with given data. Upon the completion of this minor, students can apply their skills to many fields and industries, including healthcare, retail, insurance, finance, bioinformatics, political science, environmental science, epidemiology, and public health. The minor has a unique structure tailored to address the needs of a diverse group of students to meet their academic and career goals. Students with a data science minor are strongly encouraged to expand their knowledge by pursuing a major or double major in related programs, such as Data Science, Mathematics, Applied Mathematics, Computer Science, Cybersecurity, etc.

Learning Outcomes

Apply principles of data science to identify the solutions for real world problems.
Utilize appropriate data science tools throughout the data lifecycle, including data extraction, exploration, preprocessing, normalization, and visualization.
Implement various machine learning and predictive models and evaluate their performances.
Recognize professional responsibilities and make informed judgments based on legal and ethical principles.
Document and present of developed data products in a professional manner.
Collaborate with others to accomplish a common objectives.

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Learning Outcomes

Apply principles of data science, computing, mathematics, and other relevant disciplines to identify solutions.
Describe and compare different mathematical and computational approaches utilized in the field of data science and machine learning.
Utilize appropriate tools throughout the data lifecycle, including data extraction, exploration, preprocessing, normalization, and visualization.
Construct and implement experimental designs and evaluate the outcomes.
Compare the performance of constructed models and identify the best performing model by deploying various model selection strategies.
Recognize professional responsibilities and make informed judgments based on legal and ethical principles.
Apply the latest state-of-the-art technology in the field of data science and machine learning.
Document and present project outcomes in a scientific or professional community.
Collaborate with others to accomplish a common objectives.
Identify research problems and related work in the given domain of interests.
Understand the mathematical, statistical, and computational basis of data science and machine learning techniques.

Learning Outcomes

Apply principles of data science, computing, mathematics, and other relevant disciplines to identify solutions.
Describe and compare different mathematical and computational approaches utilized in the field of data science and machine learning.
Utilize appropriate tools throughout the data lifecycle, including data extraction, exploration, preprocessing, normalization, and visualization.
Construct and implement experimental designs and evaluate the outcomes.
Compare the performance of constructed models and identify the best performing model by deploying various model selection strategies.
Recognize professional responsibilities and make informed judgments based on legal and ethical principles.
Apply the latest state-of-the-art technology in the field of data science and machine learning.
Document and present project outcomes in a scientific or professional community.
Collaborate with others to accomplish a common objectives.
Identify research problems and related work in the given domain of interests.

Learning Outcomes

Apply principles of data science to identify the solutions for real world problems.
Utilize appropriate data science tools throughout the data lifecycle, including data extraction, exploration, preprocessing, normalization, and visualization.
Implement various machine learning and predictive models and evaluate their performances.
Recognize professional responsibilities and make informed judgments based on legal and ethical principles.
Document and present of developed data products in a professional manner.
Collaborate with others to accomplish a common objectives.

Data Science at �鶹ɫ��Ƭ

Data Science at �鶹ɫ��Ƭ prepares graduates not only to derive knowledge and insights from data today, but also to adapt to future trends and technologies and to influence the direction of the field in the years to come. Data has become a key asset that enables institutions to be effective, vigilant, and remain competitive in today’s dynamic environment. Potential applications are wide ranging and include healthcare, finance, transportation, security, manufacturing, entertainment, and retail.

The interdisciplinary Data Science program draws heavily from mathematics and computer science for foundational courses. In addition to foundational course work, students will explore an area of application of data science drawn from fields such as science, engineering, cybersecurity, business, public health, political science, or one of many other fields and complete a project that requires integration and application of knowledge and skills gained through coursework.

Data Science at �鶹ɫ��Ƭ has a highly flexible undergraduate interdisciplinary curriculum which provides our students an innovative and project-based learning experience by integrating inquiry based teaching, learning, research, innovation, and collaboration.

We offer three different programs in Data Science:

Bachelors of Science(BS) in Data Science,
Bachelors of Arts(BA) in Data science, and
Minor in Data Science.

Each program has a unique structure tailored to address the needs of diverse group of students in order to meet their academic and career goals. Interested students can enroll directly in the program as incoming freshman, transfer to the program in their sophomore or junior levels, or pursue double major/minor as their secondary area of interests.

Career Prospects in Data Science

Graduate Study in the areas of Data Science, Business Analysis, Applied Mathematics, Computational Sciences, etc.
Data Scientist and Machine Learning Engineer
Data Analyst and Data Engineer
Database Architect, Business Analyst, and Quantitative Analyst
Federal jobs in DOD, DOE, NASA, and other National Labs
Several other high-paying job opportunities in different industries such as Amazon, Microsoft, Apple, Google, CVS, Fidelity Investments, Liberty Mutual, Raytheon, and so on.

Program Advisory Committee

Hum Nath Bhandari, Associate Professor of Mathematics (Chair)
Sonya Cates, Associate Professor of Computer Science
Russell Beauchemin, Associate Professor of Cybersecurity and Networking
Christopher Burtner, Associate Professor of Biology
Kerri Warren, Professor of Biology and Public Health
Sunil Kumar, Assistant Professor of Accounting

Contact Us

Hum Nath Bhandari, Associate Professor of Mathematics

Address MNS 225

Emailhbhandari@rwu.edu

Phone(401) 254-3782

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Undergraduate Admission

Graduate Admission

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