https://github.com/hit07/data_science

Data [ Exploration, Cleaning, Manipulation, Visualisation ]
https://github.com/hit07/data_science

data-analysis data-cleaning data-exploration data-manipulation data-visualization eda jupyter-notebook matplotlib numpy pandas-dataframe scipy

Last synced: about 10 hours ago
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Data [ Exploration, Cleaning, Manipulation, Visualisation ]

• Host: GitHub
• URL: https://github.com/hit07/data_science
• Owner: Hit07
• Created: 2024-06-20T06:30:28.000Z (8 months ago)
• Default Branch: master
• Last Pushed: 2024-07-03T06:41:21.000Z (7 months ago)
• Last Synced: 2025-02-01T19:45:53.646Z (about 10 hours ago)
• Topics: data-analysis, data-cleaning, data-exploration, data-manipulation, data-visualization, eda, jupyter-notebook, matplotlib, numpy, pandas-dataframe, scipy
• Language: Jupyter Notebook
• Homepage:
• Size: 15.4 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: Readme.md

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README

        
# Data Science Project

This project covers several data analysis and visualization tasks using Python.

## 1. Google Play Store Apps & Reviews Analysis

### Overview

Analyzing Google Play Store app data for insights into ratings, sizes, reviews, and revenue estimates.

### Files:

- `apps.csv`: Dataset with app details.

- `play_store.ipynb`: Jupyter notebook for data analysis.

### Skills Learned:

- Data cleaning and preprocessing.

- Exploratory data analysis (EDA) techniques.

- Visualization using matplotlib and seaborn.

## 2. Data Exploration

### Overview

Exploring salaries by college major dataset.

### Files:

- `salaries_by_college_major.csv`: Dataset on salaries by major.

- `Salaries.ipynb`: Notebook for data exploration.

### Skills Learned:

- Data manipulation and handling missing data.

- Basic statistical analysis.

- Pandas operations for data summarization.

## 3. Data Visualization

### Overview

Visualizing programming language popularity trends.

### Files:

- `prog_lang.ipynb`: Jupyter notebook for visualization.

- `QueryResults.csv`: Dataset with programming language data.

### Skills Learned:

- Plotting with matplotlib.

- Creating informative charts and graphs.

- Data interpretation and presentation.

## 4. Google Trends Analysis

### Overview

Analyzing trends related to Bitcoin, TESLA, and unemployment benefits.

### Files:

- Various CSV files for trend data.

- `trends.ipynb`: Notebook for trend analysis.

### Skills Learned:

- Time series data analysis.

- Correlation analysis between different trends.

- Insightful visualization techniques.

## 5. LEGO Data Analysis

### Overview

Analyzing LEGO dataset to understand themes and sets.

### Files:

- Datasets (`colors.csv`, `sets.csv`, `themes.csv`).

- `Lego.ipynb`: Notebook for LEGO data analysis.

### Skills Learned:

- Data aggregation and merging.

- Visualizing hierarchical data structures.

- Insights into product trends and categorization.

## 6. Numpy & N-dimensional Array

### Overview

Practical usage of NumPy for array operations.

### Files:

- `Numpy.ipynb`: Notebook for NumPy operations.

- Images for illustration (`img_1.png`, `yummy_macarons.jpg`).

### Skills Learned:

- Efficient computation with NumPy arrays.

- Basic image manipulation with NumPy.

- Broadcasting and vectorization techniques.

# Conclusion

This repository showcases various data science skills including data cleaning, exploration, visualization, and specialized tools like NumPy for efficient computation. Each section provides practical insights and skills applicable to real-world data analysis projects.