Data Storage and Analysis

This repository contains assignments, labs, midterm, and final projects for the course CSCI4235 - Data Storage and Analysis.

Project Structure

📁 assignment-3

Leetcode-based tasks:

• 262_trips_and_users.py — SQL task related to analyzing trips.
• 3374_first_letter_capitalization_II.py — text processing task.
• 601_human_traffic_of_stadium.py — stadium traffic analysis task.
• Folder leetcode/ contains SQL scripts and additional Python solutions.

📁 assignment-4

More Leetcode tasks:

• 3451_find_invalid_ip_add.py — finding invalid IP addresses.
• 3482_analyize_organization_hierarchy.py — organization hierarchy analysis.
• Folder leetcode/ includes both Python and SQL implementations.

📁 assignment-5

Netflix data analysis project:

• netflix.ipynb — analysis of Netflix movies and shows.
• netflix_titles.csv — Netflix dataset.

📁 lab1

• Data exploration of football appearances.
• Files include Jupyter Notebooks, Python scripts, images, and datasets (appearances.csv and others).
• Contains course syllabus: CSCI4235 Data storage and analysis_Syllabus_2024_Kartbayev.doc.

📁 lab2

• Data analysis of video game sales (vgsales.csv).
• Notebook: vgsales.ipynb.

📁 midterm

• Notebook and data for the midterm exam.
• Dataset: appearances.csv.

📁 endterm

• Final project focusing on FIFA player data analysis.
• Notebooks: fifa.ipynb, fifa_players.ipynb.
• Dataset: fifa_players.csv.

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🚀 What I Did Technically

• Developed SQL queries for data analysis tasks
• Created Python scripts for data cleaning, transformation, and analysis
• Performed Exploratory Data Analysis (EDA) using Jupyter Notebooks
• Visualized data with Matplotlib and Seaborn
• Worked with various CSV datasets (Netflix, FIFA, video game sales, etc.)
• Wrote PostgreSQL SQL scripts for solving database-related problems
• Applied basic data science techniques to real-world datasets

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🚀 Getting Started

1. Clone the repository:

git clone https://github.com/your-username/dataStorage.git
cd dataStorage
Install required libraries:

bash
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pip install -r requirements.txt
If requirements.txt is missing, install manually (pandas, matplotlib, seaborn, jupyter, etc.).

Launch Jupyter Notebook:

bash
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jupyter notebook
Open any .ipynb file in your browser to start working.

🛠️ Technologies Used
Python

SQL (PostgreSQL)

Pandas, Matplotlib, Seaborn

Jupyter Notebook

📄 License
This project is for educational purposes within the course CSCI4235 Data Storage and Analysis.