Part I · Foundations, Programming and Algorithmic Thinking
1. Introduction to Data Structures and Algorithms
7 lessons•57 topics•Beginner
7 of 7 lessons published
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Why learn DSA
- Problems vs algorithms
- Data vs information
- Data structures vs abstract data types
- Input-output contracts
- Correctness vs efficiency
- Scalability
- Real-world use cases
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Algorithm fundamentals
- Algorithm properties
- Finiteness
- Determinism
- Definiteness
- Preconditions and postconditions
- Pseudocode
- Flowcharts
- Dry runs
- Tracing tables
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Core vocabulary
- Element
- Index
- Key and value
- Node and edge
- Pointer and reference
- State
- Invariant
- Mutable and immutable
- Ordered and unordered
- Stable vs unstable
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Classifying data structures
- Primitive vs non-primitive
- Linear vs non-linear
- Static vs dynamic
- Contiguous vs linked
- Homogeneous vs heterogeneous
- Persistent vs ephemeral
- Mutable vs immutable
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Classifying algorithms
- Iterative vs recursive
- Deterministic vs randomized
- Exact vs approximation
- Online vs offline
- Sequential vs parallel
- In-place vs out-of-place
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Choosing a solution
- Constraints analysis
- Input size
- Time limit
- Memory limit
- Data access patterns
- Operation frequency
- Edge cases
- Cost-performance trade-offs
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Problem-solving workflow
- Understand requirements
- Build examples
- Brute-force baseline
- Identify patterns
- Choose structure
- Prove
- Analyze
- Implement
- Test
- Optimize