Efficient Algorithms (EALG)

Materials for the Efficient Algorithms (EALG) course in the FHNW Data Science BSc program. This repository contains lecture notebooks, exercises, reference solutions, and profiling labs used during the course.

Goals

• Build a practical understanding of core algorithms and data structures.
• Compare algorithmic approaches using runtime and memory profiling.
• Practice implementing and analyzing solutions in Python.

Repository Structure

ealg/
├── algorithms/
│   ├── dp.ipynb
│   ├── graphs.ipynb
│   ├── sorting_algorithms.ipynb
│   ├── sorting_exercise.ipynb
│   └── sorting_exercise_key.ipynb
├── data_structures/
│   ├── arrays_and_lists.ipynb
│   ├── stack_and_queue.ipynb
│   ├── stack_and_queue-key.ipynb
│   ├── tree_and_dictionary.ipynb
│   └── StackAndQueue.java
├── profiling/
│   ├── time_and_memory_complexity.ipynb
│   ├── profiling_time.ipynb
│   ├── profiling_memory.ipynb
│   ├── profiling_exercise.ipynb
│   ├── profiling_exercise_key.ipynb
│   ├── storage_exercise.ipynb
│   ├── storage_exercise_key.ipynb
│   └── storage_lab_data/
├── img/
├── requirements.txt
└── README.md

Course Content Overview

All sections include exercises + key notebooks for self-checking.

1) Algorithms (algorithms/)

• Sorting concepts and implementations
• Graph fundamentals and traversal ideas
• Dynamic programming (DP) principles

2) Data Structures (data_structures/)

• Arrays and lists
• Stacks and queues
• Trees and dictionary-like structures
• One Java example (StackAndQueue.java) for cross-language perspective

3) Profiling (profiling/)

• Time complexity experiments
• Memory usage analysis
• Storage-related practical labs

Requirements

Main Python dependencies are pinned in requirements.txt, including:

• numpy, pandas, matplotlib, seaborn
• networkx
• scikit_learn
• tensorflow, torch
• psutil, Pympler
• ipython, ipywidgets, rich

Setup

1) Create and activate a virtual environment

python3 -m venv env_ealg
source env_ealg/bin/activate

2) Install dependencies

pip install -r requirements.txt

3) Start Jupyter

jupyter lab

Then open the notebooks from algorithms/, data_structures/, or profiling/.

Working Pattern (Recommended)

• Use notebooks without _key first.
• Solve tasks independently.
• Use matching _key notebooks to compare and review your approach.

Naming Convention

• *_exercise.ipynb: student version
• *_exercise_key.ipynb or *-key.ipynb: reference solution

Notes

• Some notebooks are computation-heavy due to ML/profiling examples.
• If a notebook feels slow, restart the kernel and run cells step by step.

Instructor / Maintainer

• Instructor: Merve Selcuk Simsek
• Program: FHNW Data Science BSc
• Role: Course Instructor and Repository Maintainer

License

This repository uses dual licensing:

• Code is licensed under the MIT License (see LICENSE).
• Course content (notebooks, exercise text, solutions, images, and explanatory material) is licensed under CC BY-NC-SA 4.0 (see LICENSE-CONTENT).

Usage

This repository is intended for teaching and learning in the EALG context. Please follow the terms in LICENSE and LICENSE-CONTENT when reusing material.