Programme Outcomes (PO's)
Programme Outcomes (PO's)
- PO 1 - Disciplinary knowledge: Demonstrate a strong foundation in data science concepts, including statistics, programming, machine learning, and data visualization, with the ability to apply these principles effectively in real-world scenarios.
- PO 2 - Scientific Reasoning: Develop analytical and logical reasoning skills to critically evaluate data-driven problems, apply appropriate algorithms, and develop computational solutions using data science methodologies.
- PO 3 - Problem Solving: Apply data science techniques to address complex, real-world problems by designing, implementing, and optimizing data-driven solutions using cloud computing, big data technologies, and scalable architectures.
- PO 4 - Environment and Sustainability: Understand and assess the environmental impact of data science applications, leveraging sustainable computing practices and ethical AI principles to promote responsible data usage and storage.
- PO 5 - Research and Innovation: Acquire skills in hypothesis formulation, experimental design, statistical analysis, and data interpretation to conduct research in data science, enabling innovation in fields such as healthcare, finance, and smart infrastructure.
- PO 6 - Ethics and Responsibility: Adhere to ethical guidelines in data handling, ensuring privacy, security, and fairness while addressing biases in AI and machine learning applications.
- PO 7 - Teamwork and Collaboration: Work effectively in interdisciplinary teams, contributing to collaborative data science projects that integrate expertise from computer science, mathematics, and domain-specific fields.
- PO 8 - Communication Skills: Present complex data-driven insights in a clear and concise manner, using data storytelling, visual analytics, and technical reports to communicate findings to diverse audiences, including technical and non-technical stakeholders.
- PO 9 - Self-Directed and Life-Long Learning: Engage in continuous learning through online platforms, certifications, and industry-relevant courses to stay updated with emerging technologies and evolving trends in data science and artificial intelligence.
