This repository contains a collection of data science lab experiments designed to provide hands-on experience with various data science techniques and tools. It covers essential topics and methods in data science, including data manipulation, analysis, and visualization.
The laboratory experiments in this section include:
- Numpy: Fundamental package for numerical computations, including array operations and mathematical functions.
- Pandas: Library for data manipulation and analysis, providing data structures like Series and DataFrames for handling structured data.
- Plotting & Visualization: Techniques for visualizing data distributions and relationships using libraries like Matplotlib and Seaborn.
- Exploratory Data Analysis (EDA): Methods for analyzing datasets to summarize their main characteristics, often using visual methods.
- Hypothesis Testing: Statistical methods to validate assumptions or claims about a dataset.
- Chi-Square Tests: A statistical test to determine if there is a significant association between categorical variables.
In this section, you'll find mini projects that apply data science skills learned throughout the experiments:
- Data Cleaning: Projects focusing on preparing raw data for analysis by removing inaccuracies and inconsistencies.
- Exploratory Data Analysis (EDA): Projects that use visual and quantitative methods to understand data distributions and relationships.
- Machine Learning: Applying algorithms to build predictive models based on the data, including supervised and unsupervised learning techniques.
This repository utilizes various technologies, including but not limited to:
- Python
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Scikit-learn
- Clone the repository:
git clone https://github.com/iamtahasc/Data-Science-Lab-Experiments.git - Navigate to the project directory:
cd Data-Science-Lab-Experiments - Install the required dependencies:
pip install -r requirements.txt - Follow the instructions in each subdirectory for specific experiments and mini projects.
Feel free to explore, experiment, and contribute to this repository!