Data Science PO/CO/&PSO

Program Outcomes (POs) – B.Sc. (Data Science)

Graduates of the program will be able to:

1. Foundational Knowledge: Demonstrate a strong foundation in mathematics, statistics, computer science, and core data science concepts to support advanced learning and applications.
2. Data Analysis and Interpretation: Apply statistical and computational techniques to collect, clean, analyze, and interpret structured and unstructured data for actionable insights.
3. Programming Proficiency: Utilize programming languages such as Python, R, and SQL, along with modern tools and frameworks, for effective data science applications.
4. Problem-Solving Skills: Identify real-world problems and develop appropriate data-driven models and algorithms to provide efficient solutions.
5. Research and Innovation: Employ scientific methods and innovative approaches to conduct research and generate novel insights in data science and interdisciplinary domains.
6. Communication Skills: Present technical concepts, analytical findings, and data-driven insights effectively in oral, written, and visual formats.
7. Teamwork and Leadership: Work collaboratively and productively as an individual and in teams, demonstrating leadership, adaptability, and cooperative skills.
8. Ethics and Social Responsibility: Apply ethical principles, ensure data privacy and security, and use data responsibly for societal benefit.
9. Global and Societal Perspective: Understand and evaluate the impact of data science solutions on global, economic, environmental, and societal contexts.
10. Lifelong Learning :Engage in continuous learning, adapt to emerging technologies, and pursue advanced research or professional development in data science.

Program Specific Outcomes (PSOs) – B.Sc. (Data Science)

After completing the program, graduates will be able to:

1. Statistical and Mathematical Competence: Apply mathematical, statistical, and probabilistic models to analyze data and support informed decision-making.
2. Data Science Tools and Technologies: Utilize programming languages, databases, visualization tools, and machine learning frameworks to process, manage, and analyze large-scale data efficiently.
3. Machine Learning: Design, implement, and evaluate machine learning algorithms for classification, regression, clustering, and predictive modeling problems.
4. Domain-Specific Applications: Apply data science techniques to domains such as business, healthcare, social sciences, and engineering to generate actionable insights.
5. Professional Readiness: Demonstrate analytical, technical, and problem-solving competencies required for careers in data science, artificial intelligence, business analytics, and related sectors.