arya2004 / data-science

Data Science Unitersity Course

Data Science University Course

Course Overview

This Data Science course provides a comprehensive introduction to the field, covering various data processing, analysis, and visualization techniques using R within Jupyter Notebooks. The course is designed for university students and includes practical projects and datasets to enhance learning.

Repository Structure

datasets/

This directory contains the datasets used throughout the course. Each file is related to specific exercises and projects covered in the course.

• germination_csv.csv: Data related to seed germination studies.
• hair_eye_color_csv.csv: Dataset containing information on hair and eye color.
• Hypothesis_csv1.csv: Data used for hypothesis testing.
• knn1_csv.csv: Dataset for K-Nearest Neighbors (KNN) algorithm.
• pollutant_csv.csv: Dataset on pollutant levels.
• Toy_sales_csv.csv: Sales data for toy products.
• travelled_abroad_csv.csv: Data on individuals who have traveled abroad.
• wbc_csv.csv: White blood cell count data.

projects/

This directory contains Jupyter notebooks for various projects and exercises. These notebooks utilize R for data analysis and visualization.

• 00.ipynb: Introduction and course overview notebook.
• 01.ipynb: Initial data exploration and basic data analysis.
• 02.color.ipynb: Analysis related to the hair and eye color dataset.
• 02.germination.ipynb: Analysis of the germination dataset.
• 02.ipynb: Additional exercises and projects.
• 02.pollutant.ipynb: Analysis of pollutant data.
• 03.ipynb: Advanced data analysis techniques.
• 03.boxplot.ipynb: Creating and analyzing box plots.
• 03.scatterplot.ipynb: Creating and analyzing scatter plots.
• 04.ipynb: Further advanced analysis and visualization techniques.
• 05.ipynb: Continuation of advanced topics.
• 06.ipynb: Specific project work.
• 07.ipynb: Additional project exercises.
• 08.ipynb: Further project exercises.
• Lab1.ipynb: Laboratory exercises.

Other files

• .gitignore: Specifies files to be ignored by Git.
• LICENSE: License information for the course materials.
• README.md: This readme file.

Getting Started

1. Clone the Repository: Clone this repository to your local machine using:

   git clone https://github.com/arya2004/data-science.git

2. Install Dependencies: Ensure you have Jupyter and the necessary R kernel installed. You can install them using:

   # Install Jupyter
   pip install jupyter
   
   # Install R and the IRKernel
   R
   install.packages('IRkernel')
   IRkernel::installspec(user = FALSE)

3. Open Jupyter Notebooks: Navigate to the projects directory and open the desired .ipynb file using Jupyter Notebook:

   jupyter notebook 00.ipynb

Course Workflow

1. Start with 00.ipynb: Begin with the introductory notebook to get an overview of the course.
2. Follow the Notebooks in Sequence: Proceed through the notebooks in numerical order. Each notebook builds on the concepts and skills from the previous ones.
3. Use the Datasets: Apply the datasets in the datasets/ directory to complete the exercises and projects in each notebook.

License

This project is licensed under the terms of the MIT license.

Contact

For any questions or concerns, please open an issue on GitHub or contact the course instructor.