Data-Engineering-Algorithms

Graduate coursework repository implementing core algorithms for data processing, complexity analysis, and scalability reasoning in data engineering contexts.

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1. Title and Summary

Data Engineering Algorithms
Northwestern University M.S. in Data Science (Data Engineering specialization): Jupyter-based implementations and empirical benchmarks of sorting, search, graph, dynamic programming, and greedy algorithms, with Big O analysis tied to data-intensive processing design.

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2. Concepts and Methods

• Comparison sorting: selection sort, bubble sort, insertion sort, and quicksort (divide-and-conquer with pivot partitioning); empirical timing across increasing input sizes
• Search algorithms: linear search, binary search on sorted collections, and average-case lookup via Python set membership (hash-based O(1) retrieval)
• Recursion vs. iteration: factorial computed recursively and with a for loop; stack-depth and execution-time comparison
• Graph traversal: breadth-first search (BFS) over a multi-level adjacency-list graph using collections.deque; shortest-path routing with Dijkstra's algorithm on a weighted networkx graph (NYC to LA route scenario)
• Dynamic programming: bottom-up solution to the Boolean Parenthesization Problem (T/F symbols with &, |, ^ operators); 2D tables T[i][j] and F[i][j] for subexpression counts
• Greedy scheduling: cost-minimizing security-guard shift assignment under hourly and overtime wage rules
• Complexity analysis: Big O notation for each algorithm (e.g., O(n²), O(n log n), O(log n), O(n+e), O(n³)); discussion of scalability implications for data-intensive systems
• Empirical benchmarking: controlled random seeds, time / datetime timing, pandas summary tables, and matplotlib plots of input size vs. execution time

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3. Stack

Layer	Tools
Language	Python 3
Environment	Jupyter Notebook
Numerics / tables	NumPy, pandas
Visualization	matplotlib
Graphs	NetworkX
Utilities	collections.deque, iteration_utilities, time, random, string

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4. Structure

Data-Engineering-Algorithms/
├── Selection Sort Algorithm.ipynb
├── Quick Sort Algorithm .ipynb
├── Binary Search Algorithm.ipynb
├── Recursive Algorithms and Iterative Algorithms .ipynb
├── Hash Functions.ipynb
├── Breadth-First Search Algorithm.ipynb
├── Dijkstra's Algorithm.ipynb
├── Dynamic Programming Algorithm .ipynb
├── Greedy Algorithm.ipynb
└── README.md

• Organization: flat layout; one notebook per algorithm assignment with embedded narrative reports (Introduction, Methodology, Analysis & Results, Big O Discussion, Conclusion)
• Reusable modules: none; logic lives in notebook cells rather than importable packages
• Engineering practice: reproducible random seeds, timed benchmarks at multiple input scales, tabular and visual comparison of algorithm variants, and written analysis connecting algorithm choice to operational scalability

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Course context: Northwestern University, M.S. in Data Science, Data Engineering specialization
Repository: https://github.com/EAName/Data-Engineering-Algorithms