DataScience Project

This repository contains data, Python analysis, and work done with other data science tools like RapidMiner, Tableau, RStudio, and PowerBI. It's organized into several folders to keep the different aspects of the project separate and manageable.

Getting Started

Prerequisites

• Python: It is highly recommended to use Anaconda to manage your Python environment. You can download it from anaconda.com.

• Anaconda Environment: A shared Anaconda environment is provided in the Setup folder as environment.yml. You can recreate the environment using:

  conda env create -f Setup/environment.yml
  conda activate DataScienceStudy  # Or whatever name is in your environment.yml

• Anaconda Environment: A shared Anaconda environment is provided in the Setup folder as DataScienceStudy.yml. You can recreate the environment using:

  conda env create -f Setup/DataScienceStudy.yml
  conda activate DataScienceStudy  # Or whatever name is in your .yml file is

• Jupyter Notebook: Install Jupyter Notebook (if not already included in your environment):

  conda install jupyter

• Other Software: The Setup folder contains documentation on how to install and configure other software used in this project (Git, VS Code, Anaconda, etc.).

Instructions

1. Clone the repository:

   git clone [https://github.com/thaopham98/DataScience.git](https://github.com/thaopham98/DataScience.git)
   cd DataScience

2. Create and activate a virtual environment (Anaconda - recommended): (See above)

3. Explore the project folders: The repository is organized as follows:

Project Folders

• data: This folder contains the datasets used in the analysis. The datasets are typically in CSV, TSV, or other common data formats. Describe the type of data stored here (e.g., "Customer data," "Sales data," etc.)

• python_analysis: This folder contains Jupyter Notebooks with examples of data analysis using the datasets provided in the data folder. Each notebook focuses on a specific analysis or project. Notebooks in this folder should be considered finished and reviewed. =======

• python_analysis: This folder contains Jupyter Notebooks with examples of data science and machine learning using the datasets provided in the data folder. Each notebook focuses on a specific analysis or project. Notebooks in this folder should be considered finished and reviewed.

  • Load and preprocess the data.
  • Perform exploratory data analysis (EDA).
  • Apply machine learning models (if applicable).
  • Visualize the results.
  • Interpretations for visual and statistical results.

• draft: This folder contains Jupyter Notebooks that are still in progress. These notebooks may be incomplete or contain experimental code.

• RapidMiner: This folder contains documentation (PDF files) outlining the steps taken to create models in RapidMiner.

• Tableau: This folder contains Tableau workbooks (.twbx or .twb) and supporting documentation (e.g., .docx files with images of visualizations).

• RStudio: This folder contains R scripts (.R files) and any related data or output from data analysis performed in R.

  • My First R Markdown

• PowerBI: This folder contains Power BI files (.pbix) and any related documentation.

Usage Examples (Within python_analysis)

Provide a brief example for each notebook in the python_analysis folder. For example:

• analysis_notebook_1.ipynb: This notebook performs customer segmentation using k-means clustering.

Data (Within data)

The data folder contains the datasets used for analysis. Provide more details about the data, such as its source, format, and any preprocessing steps performed.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

Authors

• Thao Pham (thaopham98)