Data Analyst Challenge

Project Overview

This project focuses on analyzing complex datasets to derive meaningful insights and demonstrate key data analytics capabilities. It includes data processing, cleaning, visualization, and summary reporting.

Key Features

• Data Ingestion and Cleaning: Efficiently read and preprocess raw data from various sources.
• Data Analysis: Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies.
• Visualizations: Generate insightful plots and charts to support data-driven conclusions.
• Statistical Analysis: Apply statistical methods to uncover hidden relationships.

Project Structure

Data Analyst Challenge:
├── data/                # Raw and processed data files
├── Data Analyst - Challenge_Code           # Jupyter Notebook for analysis
├── Cointab Data Analyst - Challenge                 # Challenge PDF which need to solved.
├── requirements.txt     # Required Python packages
└── README.md            # Project documentation

Installation Instructions

1. Clone the repository:

   git clone <repository-url>
   cd data-analyst-challenge

2. Create a virtual environment:

   python3 -m venv env
   source env/bin/activate   # On Windows: env\Scripts\activate

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the Jupyter Notebook:

   jupyter notebook

Key Functions

• data_loader(): Loads raw data into a structured format.
• clean_data(): Cleans the data by handling missing values and duplicates.
• visualize_data(): Generates various plots for exploratory analysis.
• summarize_data(): Provides summary statistics and key insights.

Analysis Summary

The project analyzes the given dataset to:

• Understand challenge and dataset provided, Find out the relation between data .
• understand the distribution of key variables.
• Identify trends and outliers.
• Evaluate correlations between variables.
• Provide actionable insights supported by visualizations.

Example Plots

• Distribution histograms.
• Correlation heatmaps.
• Time-series plots.

How to Use

1. Upload your data to the data/ directory.
2. Open and run the notebook in Data Analyst - Challenge_Code.
3. Modify code from jupyter python file for custom analysis.

Contributions

Feel free to contribute by opening pull requests or suggesting enhancements.

License

This project is licensed under the MIT License.