https://github.com/paladique/intro-to-datascience-workshop

A workshop that walks through data science foundations in a 30 - 1 hour format, focused on participants with prior programming experience but new to data science
https://github.com/paladique/intro-to-datascience-workshop

Last synced: 18 days ago
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A workshop that walks through data science foundations in a 30 - 1 hour format, focused on participants with prior programming experience but new to data science

• Host: GitHub
• URL: https://github.com/paladique/intro-to-datascience-workshop
• Owner: paladique
• License: mit
• Created: 2024-05-08T17:26:43.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-05-17T03:47:37.000Z (6 months ago)
• Last Synced: 2024-10-18T15:18:37.403Z (27 days ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 3.81 MB
• Stars: 2
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Introduction to Data Science

A mini-workshop that walks through data science foundations in a 30 - 1 hour format, focused on participants with prior programming experience but new to data science

## Scenario

You are a data scientist for a cat furniture store, and you're helping the sales and production teams understand the last quarter's sales.

## Contents

The notebook covers the following topics:

1. Introduction to Data Science: An overview of what data science is and its applications.

2. Data Exploration: Techniques for exploring and understanding datasets.

3. Data Preprocessing: Methods for cleaning and preparing data for analysis.

4. Data Visualization: Tools and techniques for visualizing data.

5. Machine Learning: Introduction to machine learning algorithms and their applications.

6. Model Evaluation: Methods for evaluating the performance of machine learning models.

## Usage

To make the most of this notebook, it is recommended to follow along with the code examples and exercises provided. Each section is accompanied by detailed explanations and code snippets that demonstrate the concepts being discussed. Feel free to modify and experiment with the code to deepen your understanding.

## Getting Started

To get started, open this notebook in a GitHub Codespace. Follow the instructions within the notebook to progress.