A growing collection of Python problems covering algorithms, data structures, string manipulation, optimization, and data science simulations — solved with clean code, complexity analysis, and real-world context.
Nabankur Ray Data Science & Business Analytics | Deakin University 🔗 LinkedIn | 🐙 GitHub
This repository is a living portfolio of Python problem-solving work.
Every problem added here demonstrates applied thinking in:
- Core Python programming fundamentals
- Algorithm design and complexity analysis
- Data science and simulation techniques
- Real-world problem modelling
Problems come from university coursework, self-practice, and data science challenges. The repository grows continuously as new problems are solved and new topics are explored.
python-dsa-and-data-science-problems/
│
├── Count how many other digits are smaller than it.ipynb
├── Design a solution to generate exactly n magic coins.ipynb
├── Find the maximum without max() or sort().ipynb
├── Reverse the string into list using list comprehension.ipynb
├── Scoring function to determine the winner of the 3MT competition.ipynb
├── Split text into sentences and find the top 30 most common words.ipynb
├── Stochastic Optimization with NumPy.ipynb
├── LICENSE
└── README.md
📌 New folders will be added as new problem categories are introduced.
Unit: SIT742, Deakin University | Trimester: 2, 2025
| Problem | Topic | Key Concepts |
|---|---|---|
| Q1.1 | Reverse Student ID String | List comprehension, Exception handling |
| Q1.2 | Partial Reverse from Index | String slicing, Input validation |
| Q1.3 | Max Digit & Second Max (No built-ins) | Manual search, O(n) complexity |
| Q1.4 | Count Smaller Digits (find_digits) |
Nested iteration, O(n²) complexity |
| Q1.5 | 3MT Competition Scorer | Multi-criteria normalization, Data wrangling |
| Q1.6 | Magic Coin Generator (Machine 1 & 2) | Bit manipulation logic, Greedy approach |
| Q1.7 | Word Frequency Counter (Top 30) | Text parsing, Dictionary sorting |
| Q2.1 | Monte Carlo Simulation — Product A | NumPy, Stochastic modelling |
| Q2.2 | Multi-Product Optimization with Budget | Constraint optimization, Simulation |
| Q2.3 | Profit vs Stock Level Analysis | Matplotlib visualization, Insights |
- Python Core — Data types, control flow, functions, modules
- Algorithm Design — Custom search, sort, and traversal without built-ins
- List Comprehension & Functional Style — Concise, readable Pythonic code
- Exception Handling — Robust input validation and error management
- NumPy — Array operations, random simulation, vectorized computation
- Monte Carlo Simulation — Probabilistic modelling under uncertainty
- Stochastic Optimization — Inventory management with demand variability
- Text Processing — Sentence splitting, word frequency analysis
- Data Visualization — Matplotlib charts for analytical storytelling
- Complexity Analysis — Time and space complexity discussion per solution
Make sure you have Python 3.10+ installed. Install dependencies (if any) using:
pip install numpy matplotlibjupyter notebook university_assignments/SIT742_MidTerm/SIT742_MidTerm.ipynbClick the badge below to open the SIT742 notebook in Colab:
Replace the link above with your actual Colab share URL after uploading.
| When | What Gets Added |
|---|---|
| University assignments | Full notebooks with outputs and comments |
| Self-practice problems | Standalone .py scripts with explanations |
| Data science challenges | Notebooks with EDA, modelling, and visualization |
| Algorithm practice | Clean functions with complexity notes |
Each problem or file will be tagged in its docstring or markdown header using:
# [TOPIC] Algorithm Design | [DIFFICULTY] Medium | [SOURCE] SIT742 Assignment
Tags used:
- Topic:
Algorithm,Optimization,Simulation,Text Processing,Data Science - Difficulty:
Beginner,Intermediate,Advanced - Source:
University,Self-Practice,Competition
This repository is licensed under the MIT License. You are free to use, adapt, and build upon this work with attribution.
- Deakin University — SIT742 Modern Data Science unit team
- Google Colab — Cloud notebook environment used for development
- NumPy & Matplotlib — Core scientific Python libraries
Nabankur Ray
Passionate about real-world data-driven solutions
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