Jupyter-Notebook

It consists of almost all the Commands of the Jupyter-Notebook

Data Science Tools and Ecosystem

In this repository, you will find a Jupyter Notebook summarizing the Data Science Tools and Ecosystem. The notebook covers popular programming languages, libraries, and tools commonly used in data science.

Table of Contents

• Introduction
• Data Science Languages
• Data Science Libraries
• Data Science Tools
• Arithmetic Expressions Examples
• Objectives
• Author

Introduction

This Jupyter Notebook explores the fundamental components of the Data Science ecosystem. It highlights programming languages, libraries, and development tools frequently employed by Data Scientists.

Data Science Languages

Some of the popular languages that Data Scientists use are:

1. Python
2. R
3. Julia

Data Science Libraries

Some of the commonly used libraries used by Data Scientists include:

1. NumPy
2. Pandas
3. Matplotlib

Data Science Tools

Here are three open-source tools used in data science:

• Jupyter Notebook
• Anaconda
• Visual Studio Code with Python extension

Arithmetic Expressions Examples

Below are a few examples of evaluating arithmetic expressions in Python.

In a code cell, you can perform operations like multiplying and adding integers:

# This is a simple arithmetic expression to multiply and then add integers.
result = (3 * 4) + 5
print(result)  # Output: 17

Objectives:

List popular languages for Data Science
Identify commonly used libraries in Data Science
Introduce open-source tools for Data Science

Author: Divya Kumari