**Data Structures** are the programmatic way of storing data so that data can be used efficiently. Almost every enterprise application uses various types of data structures in one or the other way. Data structures can be broadly classified in two categories - linear structures and hierarchical structures. Arrays, linked lists, stacks, and queues are linear structures, while trees, graphs, heaps etc. are hierarchical structures.

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- Abstract Data Types (ADTs)
- Advanced Algorithms
- Advanced Data Structures, Collections, and Generics
- Algorithm Analysis (Performance, Complexity)
- Algorithmic Thinking and Algorithm Design
- Algorithms for Computational Biology
- Algorithms for Computer Animation
- Amortized analysis, red-black trees, 2-4 trees
- Approximation in Science and Engineering
- Arrays and Lookup Tables
- Artificial Intelligence
- Autonomy and Decision Making
- Behavior of Algorithms
- Biased search trees
- Big O Notation (e.g. O(n) and O(n log n))
- Binary Search trees
- Binary Trees and Heaps
- Binomial heaps, binomial heaps with lazy delete, Fibonacci heaps, Fat heaps.
- Coding Theory
- Combinatorial Optimization
- Computational Geometry
- Computational Methods of Scientific Programming
- Computational Techniques in Systems Biology
- Computer Algorithms in Systems Engineering
- Computer Graphics
- Data Structures
- Decision Making in Large Scale Systems
- Detailed Comparison of Available Data Structures
- Distributed Algorithms
- Double Linked Lists
- Dynamic trees and their applications
- Error-Correcting Codes
- Finger search trees and their applications
- Fixed (Immutable) and Variable (Dynamic) Size Implementation of Data Structures
- Geometric Combinatorics
- Hash Tables (Dictionaries), Maps, and Graphs
- Identification, Estimation and Learning
- Implementation of Data Structures in Various Programming Languages, with C/C++ and Java
- Insertion Sort
- Linked Lists (Basic, Circular, Double)
- Machine Learning
- Maintaining order in a list
- Mathematical Programming
- Network Optimization
- Numerical Analysis for Engineering
- Numerical Computation
- Numerical Methods for Partial Differential Equations
- Parallel Computing
- Parallel Hardware
- Performance Engineering of Software Systems
- Persistent data structures
- Pointer Arithmetic
- Pointers and Pointer Arithmetic
- Priority Queues
- Program Analysis
- Quantum Complexity Theory
- Quantum Computation
- Randomized Algorithms
- Red-Black trees
- Search Algorithms and Techniques such as Linear Search, Binary Search Tree, Brute Force Search, and Heuristics
- Sort Algorithm Implementation and Comparison
- Sorting, Searching and Merging Algorithms
- Statistical Learning Theory
- Statistical Learning Theory and Applications
- Techniques in Artificial Intelligence
- The algorithm of Garsia and Wachs for finding optimal alphabetic trees
- Theoretical Computer Science
- Weight balance trees, range searching

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