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Bachelor of Computer Applications (Honours) - Data Science


The Data Science Honors program is an undergraduate degree program designed to provide students with a comprehensive understanding of computer applications and data science principles. Throughout the program, students are immersed in a curriculum integrating foundational computer science knowledge with advanced data science techniques.

This program equips students with the analytical, technical, and problem-solving skills needed to excel in various roles within the data science field, including data analysis, machine learning, artificial intelligence, and business intelligence. Graduates are prepared to contribute effectively to organizations in today's data-driven world.


Four Years / Eight Semesters

Commence of the course

Course Duration

Four Years

Mode of Study


10+2 from recognized Board / Council with minimum 50% marks.

A relaxation of 5% marks or its equivalent grade may be allowed for those belonging to SC / ST.

Key Areas

Core Computer Science Knowledge:

Develop a strong understanding of fundamental computer science concepts, algorithms, and data structures.

Programming Proficiency:

Acquire proficiency in programming languages commonly used in Data Science. Develop the ability to design, implement, and debug software solutions for data-related challenges.

Data Science Fundamentals:

Gain a comprehensive understanding of key data science concepts, including data manipulation, exploratory data analysis, and statistical modeling.

Mathematical Foundation:

Build a solid foundation in mathematical concepts essential for data science, such as linear algebra, calculus, and probability.

Database Management:

Learn to design, implement, and manage databases for efficient data storage and retrieval.

Machine Learning and Predictive Modeling:

Explore machine learning algorithms and techniques for predictive modeling, classification, and clustering.

Data Visualization:

Develop skills in data visualization to effectively communicate insights and findings from data analysis.

Big Data Technologies:

Understand and work with big data technologies and frameworks such as Hadoop and Spark for processing and analyzing large datasets.

Business Intelligence:

Learn how to extract actionable insights from data to support decision-making in a business context.

Ethical and Legal Considerations:

Understand the ethical and legal implications of working with data, including issues related to privacy and data security.

Project Management and Collaboration:

Develop project management skills to plan, execute, and deliver data science projects on time and within scope. Foster collaboration and effective communication within interdisciplinary teams.

Internship and Practical Experience:

Provide opportunities for internships or real-world projects to allow students to apply their knowledge and skills in practical settings.

Project Work:

A capstone project where students apply their skills to solve real-world problems or a significant internship experience.

Programme Objectives

  • To equip students with a well-rounded skill set, preparing them for a successful career in data science or related fields.
  • To strike a balance between theoretical knowledge and practical application to ensure graduates are ready to address real-world challenges in the rapidly evolving field of data science.

Programme Outcomes

Demonstrate proficiency in programming languages commonly used in data science, such as Python, R, and SQL.

Apply statistical methods and machine learning algorithms for data analysis and decision-making.

Apply mathematical concepts, including linear algebra, calculus, and probability, to solve data science problems.

Design, implement, and manage databases for efficient data storage and retrieval.

Implement and evaluate machine learning models for predictive modeling, classification, and clustering.

Create effective data visualizations to communicate insights and findings to both technical and non-technical audiences.

Utilize big data technologies and frameworks, such as Hadoop and Spark, for processing and analyzing large datasets.

Apply data science techniques to extract actionable insights and support decision-making in a business context.

Demonstrate an understanding of ethical and legal considerations related to data science, including privacy and data security issues.

Collaborate effectively within interdisciplinary teams and communicate technical concepts to diverse audiences.

Demonstrate a commitment to continuous learning to keep up with advancements in data science and technology.

Apply data science skills to solve real-world problems through projects, internships, or industry collaborations.

Conduct research in data science and contribute to the development of innovative solutions in the field.

Exhibit professionalism in the workplace, including effective time management, teamwork, and adherence to ethical standards.

Equip students with a strong foundation for pursuing advanced studies in data science or related fields.

Prepare students for successful entry into the workforce with skills that meet industry demands.

Programme Structure

Curriculum Delivery

Teaching methodologies for a BCA Honors program in Data Science should be designed to provide students with a balanced and practical understanding of the subject. Here are some effective teaching methodologies that can be employed:

Interactive Lectures:

  • Conduct engaging and interactive lectures to introduce theoretical concepts related to computer science, programming languages, and game development.
  • Use multimedia, presentations, and real-world examples to make the content more accessible and interesting.

Hands-On Coding Sessions:

  • Conduct practical coding sessions to reinforce programming skills using languages like Python or R. Hands-on experience is crucial for data science students.

Case Studies and Real-World Projects:

  • Engage students with real-world case studies and projects that simulate actual data science challenges. This helps bridge the gap between theory and application.

Interactive Discussions:

  • Facilitate discussions on current trends, challenges, and ethical considerations in data science. Encourage students to express their opinions and perspectives.

Workshops and Tutorials:

  • Organize workshops and tutorials to provide in-depth guidance on specific tools, frameworks, or methodologies commonly used in data science, such as TensorFlow or scikit-learn.

Collaborative Learning:

  • Foster a collaborative learning environment where students work together on group projects, share knowledge, and solve problems collectively.

Online Learning Platforms:

  • Integrate online learning platforms and resources to provide supplementary materials, quizzes, and interactive exercises. Platforms like Jupyter Notebooks can be particularly useful for data science courses.

Field Visits and Internships:

  • Arrange field visits to companies or organizations involved in data science, and encourage students to undertake internships to gain practical experience.

Assessment through Projects and Portfolios:

  • Evaluate students based on their performance in hands-on projects, portfolios, and presentations. This allows them to showcase their practical skills.

Research Assignments:

  • Assign research projects to encourage students to explore advanced topics in data science, fostering a deeper understanding of emerging trends and technologies.

Career Counseling:

  • Provide career counseling sessions to guide students in choosing career paths within the Data Science industry.
  • Assist with resume building, portfolio development, and interview preparation.

By incorporating these methodologies, we can create a dynamic and engaging learning environment for BCA (Hons.) students, preparing them for success in the rapidly evolving field of Data Science.

Career Opportunities

  • Data Scientist
  • Data Analyst
  • Business Intelligence Analyst
  • Machine Learning Engineer
  • Big Data Engineer
  • Quantitative Analyst
  • AI (Artificial Intelligence) Engineer
  • Research Scientist
  • Data Engineer
  • Predictive Modeler
  • IoT (Internet of Things) Analyst
  • Healthcare Data Analyst

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