Professional Diploma in Advanced Data Structures and Algorithms with C++

Advanced Data Structures and Algorithms with C++ offered by Geneve Institute of Business Management is an intensive, hands-on program designed to elevate practitioners from competent coders to engineers who can craft robust, high-performance algorithmic systems. The course blends rigorous algorithmic thinking with pragmatic C++ engineering: you will learn to reason about complexity precisely, choose data structures that meet real constraints, and implement solutions that are both correct and efficient. Emphasis is placed on modern C++ techniques that yield low overhead, safe resource management, and clear abstractions suitable for production systems. Throughout the program you will encounter patterns and strategies used in system kernels, latency-sensitive services, and high-throughput data pipelines, with attention to memory layout, instruction-level performance, and predictable behavior under stress. This course is ideal for professionals who must translate theoretical designs into dependable, high-performing code.

Target group

• Senior software engineers and backend developers focused on systems and performance.

• Systems architects and technical leads responsible for low-latency, high-throughput services.

• Graduate students, researchers, and engineers working on algorithm-intensive domains.

• Developers preparing for competitive engineering roles or technical interviews emphasizing implementation detail.

Objectives

• Build deep intuition for algorithmic complexity, trade-offs, and when to apply specific structures.

• Master C++ idioms that affect performance: templates, move semantics, memory ownership, and inlining.

• Design and implement advanced data structures with attention to cache behavior and space-time trade-offs.

• Analyze and optimize end-to-end algorithmic solutions for realistic constraints and workloads.

Course outline

• Foundations of high-performance C++

  • Language patterns for efficiency

    • Value vs reference choices and ownership models

    • Move semantics, RVO, and eliminating unnecessary copies

    • RAII patterns and deterministic resource cleanup

    • Inline, constexpr, and compile-time evaluation for performance

  • Generic programming and metaprogramming

    • Writing zero-overhead abstractions with templates

    • Type traits and SFINAE-based dispatch

    • Concepts and constraints for clearer APIs

    • Compile-time vs runtime decision trade-offs

• Measurement and complexity modeling

  • Cost modeling and analysis

    • Amortized and average-case reasoning techniques

    • Practical worst-case considerations for production

    • Modeling memory- and cache-related costs

    • Relating theoretical metrics to wall-clock behavior

  • Profiling and benchmarking methodology

    • High-resolution timing and statistical measurement

    • Controlling noise: warmup, pinning, and isolated runs

    • Interpreting hotspots and call-stack samples

    • Microbenchmarks vs macrobenchmarks and pitfalls

• Arrays, strings, and bit hacks

  • Cache-aware array algorithms

    • In-place transforms and block processing patterns

    • Loop tiling and memory prefetch techniques

    • Strided accesses and alignment consequences

    • Choosing representations for locality and throughput

  • Bitwise and integer-level techniques

    • Bitmask tricks, shifts, and arithmetic hacks

    • Population count, leading/trailing bit operations

    • Compact bitsets and packed data representations

    • Using hardware intrinsics safely and portably

• Trees and balanced structures

  • Self-balancing trees and augmentations

    • AVL, red-black fundamentals and rotations

    • Augmented nodes: order statistics and interval data

    • Balancing strategies and their operation costs

    • When to prefer trees over other structures

  • Memory layouts and implicit trees

    • Implicit-array trees and pointer-less designs

    • Succinct tree encodings and space-efficient representations

    • Cache-conscious node packing and pointer compression

    • Traversal strategies optimized for modern caches

• Heaps, priority structures, selection

  • Heap variants and operational trade-offs

    • Binary heap mechanics and cache considerations

    • Pairing and binomial heaps: practical differences

    • Fibonacci heap concepts and amortized insights

    • Choosing a heap for inserts, decreases, and merges

  • Selection and top-k techniques

    • Quickselect and median-of-medians approaches

    • Tournament trees and online top-k maintenance

    • Multiway selection and reservoir patterns

    • Performance tuning for repeated selection workloads

• High-performance hashing

  • Hash table designs and layouts

    • Open addressing vs separate chaining trade-offs

    • Probing strategies: linear, quadratic, robin-hood

    • Cache-friendly bucket layouts and grouping

    • Resizing policies and growth factor choices

  • Hash function and collision analysis

    • Strong non-cryptographic hash choices

    • Universal hashing and mitigation strategies

    • Fingerprinting and compact keys

    • Attack vectors and robustness in untrusted input

• Graph storage and traversal

  • Graph representations and memory formats

    • Adjacency lists, matrices, and compressed formats

    • CSR/CSC layouts and benefits for traversals

    • Dynamic graph representations and update costs

    • Choosing representations for algorithms and memory constraints

  • Traversal frameworks and invariants

    • Iterative DFS/BFS patterns for deep or wide graphs

    • Stack vs recursion and resource implications

    • Parent/visit invariants and state encoding

    • Traversal ordering impacts on cache and parallelism

• Shortest paths and network flows

  • Shortest-path families

    • Dijkstra variants and priority strategies

    • A* heuristics and admissible estimates

    • Multi-source and multi-criteria path computations

    • Speedup techniques: potentials, pruning, and goal-directed search

  • Flow and circulation methods

    • Ford–Fulkerson and Edmonds–Karp basics and limits

    • Dinic, push-relabel and scaling optimizations

    • Residual graph maintenance and capacity scaling

    • Practical complexity considerations for dense vs sparse graphs

• Advanced string indices and matching

  • Suffix structures and compressed indices

    • Suffix array construction principles and heuristics

    • Suffix tree fundamentals and memory costs

    • FM-index and compressed full-text indexes

    • Index selection for space vs query-time trade-offs

  • Pattern matching and approximate search

    • KMP, Z-algorithm and linear-time matching

    • Automata-based matching and bit-parallelism

    • Edit-distance approaches and banded optimizations

    • Filtering and verification pipelines for approximate matches

• Parallelism and external-memory algorithms

  • Parallel algorithm patterns

    • Task decomposition and work-stealing models

    • Data partitioning, synchronization minimization, and atomics

    • Cache-coherent vs NUMA-aware strategies

    • Designing algorithms tolerant of load imbalance

  • Algorithms for very large data

    • External-memory models and I/O-efficient primitives

    • Batching, buffering, and streaming-friendly layouts

    • Sort and search strategies optimized for disks/SSDs

    • Checkpointing, failure modes, and recovery considerations