Engineering Knowledge:
Apply the knowledge of mathematics, science, and engineering fundamentals to solve complex computer science and data science problems.

Problem Analysis:
Identify, formulate, review research literature, and analyze complex problems using principles of data science.

Design/Development of Solutions:
Design solutions for complex engineering problems and develop data-driven applications that meet specified needs.

Conduct Investigations of Complex Problems:
Use research-based knowledge and methods including data collection, analysis, and interpretation to draw valid conclusions.

Modern Tool Usage:
Create, select, and apply appropriate techniques, resources, and modern tools for data science applications with an understanding of limitations.

The Engineer and Society:
Apply contextual knowledge to assess societal, health, safety, legal, and cultural issues relevant to data-driven solutions.

Environment and Sustainability:
Understand the impact of data science solutions in societal and environmental contexts and demonstrate sustainable practices.

Ethics:
Apply ethical principles and commit to professional ethics, responsibilities, and norms of data science practice.

Individual and Team Work:
Function effectively as an individual and as a member or leader in diverse teams and multidisciplinary settings.

Communication:
Communicate effectively on complex data science activities with engineering communities and society at large.

Project Management and Finance:
Demonstrate knowledge of management and apply it to data science projects in a multidisciplinary environment.

Lifelong Learning:
Recognize the need for and engage in independent and lifelong learning in the context of technological change.