BBA | Data Science

Programme Outcome

Graduates will be able to: 

PO1: Demonstrate a foundation of digital marketing theoretical concepts and practices through innovative techniques to step out as corporate-ready professionals. 

PO2: Use capabilities and skills in areas of business and marketing to take up roles in digital marketing services for associate and analyst positions across diverse industries. 

PO3: Apply sensitivity towards organisational, economic and cultural diversity while designing solutions to meet global challenges. 

PO4: Build creativity and innovative thinking to develop entrepreneurial skills.

Programme Specific Outcomes

PSO1: Apply foundational and advanced data science principles, including data cleaning, preprocessing, and statistical analysis, to solve complex business problems across industries such as marketing, finance, operations, and HR. 

PSO2: Utilise data analytics tools, programming languages (e.g., Python, R), and machine learning algorithms to build predictive models and generate actionable insights for business decision-making. 

PSO3: Demonstrate the ability to interpret large datasets, visualise trends, and present business intelligence using tools like Tableau, Power BI, and other data visualisation platforms to inform strategic business decisions. 

PSO4: Exhibit critical thinking and problem-solving skills to identify key business challenges, analyse data from various sources, and apply appropriate methodologies to formulate effective, data-driven solutions. 

PSO5: Apply ethical considerations and data privacy principles in handling sensitive information, ensuring compliance with data protection regulations and promoting responsible data usage in business environments.

PSO6: Prepare for professional roles in data analytics, business intelligence, and data science fields, with readiness for both corporate positions and entrepreneurial ventures in data-driven industries such as tech, finance, and consulting.

Course Outcome

CO1: Understand core concepts of data science, business analytics, and statistical analysis in the context of business decision-making.

CO2: Apply data wrangling, cleaning, and preprocessing techniques to prepare datasets for analysis using tools like Excel, SQL, and Python. CO3: Use data visualisation techniques and tools such as Tableau and Power BI to present business insights in an impactful and meaningful manner. 

CO4: Demonstrate the ability to build and evaluate predictive models using machine learning algorithms for business forecasting and classification tasks. 

CO5: Analyse business scenarios and apply data analytics strategies to solve functional problems in marketing, finance, operations, and HR. CO6: Interpret and communicate analytical results effectively to both business and technical audiences through written reports and visual dashboards. 

CO7: Evaluate the ethical, legal, and social implications of data science practices, with a focus on data privacy, security, and responsible AI. 

CO8: Collaborate in cross-functional teams to develop data-driven solutions through business case studies, real-time datasets, and industry-based projects.