Overview

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The B.Sc. in Data Science is a comprehensive three-year undergraduate program structured across six semesters, designed to equip students with a multidisciplinary skill set essential for thriving in today’s data-driven world. The program builds a strong foundation in mathematics and statistics, complemented by programming skills in languages such as Python and R for efficient data manipulation and analysis.

The curriculum covers key areas including machine learning (both supervised and unsupervised techniques) and big data technologies such as Hadoop and Spark. Students gain hands-on experience through practical assignments and capstone projects, enabling them to apply theoretical knowledge to real-world challenges across diverse industries.

In addition, the program emphasizes ethical considerations in data usage, privacy, and responsible decision-making. Students are trained to communicate insights effectively through data visualization and presentations, ensuring they can convey complex findings to both technical and non-technical audiences. Overall, the program prepares graduates to make meaningful, ethical, and impactful contributions in the field of data science.

2. Aims and Objectives

A) Aims

• To develop a strong foundation in mathematical, statistical, and computational concepts essential for data science.
• To enable students to understand and apply machine learning techniques for predictive analysis and pattern recognition.
• To familiarize students with tools and technologies for handling large-scale data efficiently.
• To instill ethical responsibility and awareness of data privacy issues.
• To prepare students for applying data science techniques across various industry domains.
• To enhance communication skills for effectively presenting data-driven insights.
• To bridge theoretical knowledge with real-world applications.
• To prepare students for continuous learning in an evolving technological landscape.

B) Objectives

• To offer comprehensive training in mathematics, statistics, and programming for data analysis.
• To cover supervised and unsupervised learning methods for solving diverse data problems.
• To provide hands-on experience with big data tools such as Hadoop and Spark.
• To incorporate ethical practices in data collection, analysis, and dissemination.
• To enable domain-specific applications in areas like healthcare, finance, and marketing.
• To develop skills in data visualization, report writing, and effective presentation.
• To provide practical exposure through projects, internships, and capstone work.
• To encourage adaptability and continuous learning aligned with emerging trends.