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Chandigarh University - BCA Data Structure Overview

VISION

• Aiming to nurture computer professionals through innovation, experiential learning, and research to meet industry and societal needs.

MISSION OF THE DEPARTMENT

• Provide innovative learning facilities and quality-oriented teaching to solve computational problems.

• Support a multidisciplinary approach through Project Based Learning.

• Develop a clear evaluation system and experiential learning mechanisms aligned with future technologies.

• Promote research, innovation, and entrepreneurship collaboratively with industry and academia.

• Engage in activities for the upliftment of rural and deprived sections of society.

Program Outcomes

• PO1: Utilize mathematics and computing fundamentals to solve defined problems. (Computational Knowledge)

• PO2: Apply mathematical principles to identify and formulate solutions for complex problems. (Problem Analysis)

• PO3: Design and evaluate solutions for societal health and environmental systems.

• PO4: Conduct investigations of complex computing problems through research-based methods. (Conduct Investigations of Complex Computing Problems)

• PO5: Use modern computing tools to solve computing tasks within limitations. (Modern Tool Usage)

• PO6: Uphold professional ethics in computing practices. (Professional Ethics)

• PO7: Enhance understanding through independent learning strategies. (Life-long Learning)

• PO8: Implement ideas in multidisciplinary environments as team members or leaders. (Project Management and Finance)

• PO9: Contribute to the community by writing reports, designing documents, and effective presentations. (Communication Efficacy)

• PO10: Solve societal issues through mathematical and computing knowledge. (Societal and Environmental Concern)

• PO11: Foster confidence and continuous learning as team members or leaders. (Individual and Teamwork)

• PO12: Innovate and develop solutions for market business challenges and development issues. (Innovation and Entrepreneurship)

Program Educational Objectives

• PEO1: Exhibit design and analytical skills to generate creative solutions through effective communication.

• PEO2: Build successful careers using application development methods aligned with Industry 4.0 principles.

• PEO3: Implement knowledge to advance societal growth and address health and safety challenges.

• PEO4: Exercise critical thinking and problem-solving skills in varied professional contexts.

Program Specific Outcomes

• PSO1: Systematically plan, develop, execute, and test computing applications across various domains (Social-Media, Analytics, Web Applications, Data Interpretations).

• PSO2: Cultivate sustainable creativity and study approaches that address global interests.

Course Components

Topics to be Covered

• Introduction to Data Structures

• Importance and Need of Data Structures

• Applications of Data Structures

Data Structures Overview

• Definition: A method of organizing data effectively to facilitate processing, retrieval, and storage.

• Types Include: Arrays, Linked Lists, Trees, Graphs, Stacks, Queues.

Key Operations and Concepts

Fundamental Operations

• Traversal: Visiting each element in a specific order.

• Insertion: Adding an element at a specific location.

• Deletion: Removing an element from a defined location.

• Search: Finding the location of an element in a data structure.

Sorting Algorithms and their Complexity

• Bubble Sort: O(n^2) – Simple but inefficient for large datasets.

• Selection Sort: O(n^2) – Consistently poor performance.

• Insertion Sort: O(n^2) in average case, O(n) in best case (when sorted).

• Merge Sort: O(n log n) in all cases – efficient and stable.

• Quick Sort: O(n log n) average case, O(n^2) worst case – efficient in practice.

Advanced Structures

• Abstract Data Types (ADTs): Define behaviors without specifying implementation.

• Self-Referential Structures: Structures with pointers pointing to their type.

• Complexity Analysis: Time/Space complexity for predicting efficiency.

Algorithms

Linear Search

• Description: Sequentially compares each element to a target value.

• Time Complexity: O(n) average/worst cases.

• Implementation in C provided.

Binary Search

• Description: Efficiently finds elements in sorted arrays by repeatedly dividing the search range in half.

• Time Complexity: O(log n) average/worst cases.

• Implementation in C for iterative and recursive approaches provided.

Representational Methods

• Polynomials Representation: Using arrays and linked lists to manage coefficients and exponents.

• Operations on Polynomials: Addition, evaluation, and representation using data structures.

Conclusion

• Data Structures are foundational to software and application development, informing how data is organized, accessed, and manipulated efficiently.