Exercise 2

Tools for Data Science

Exercise 3

Introduction

In this notebook, we will explore various tools, libraries, and languages used in Data Science.

Exercise 4

Data Science Languages

• Python
• R
• SQL
• Julia
• Scala

Exercise 5

Data Science Libraries

• NumPy
• Pandas
• Matplotlib
• Scikit-learn
• TensorFlow
• Keras

Exercise 6

Table of Data Science Tools

Tool	Category
Jupyter	Notebook
RStudio	IDE
Apache Hadoop	Big Data Platform
IBM Watson	AI Platform

Exercise 7

Arithmetic Expression Examples

Below are examples of simple arithmetic expressions such as addition and multiplication.

Exercise 8

Multiply and Add Numbers

result = (3 * 4) + 5 result

Exercise 9

Convert Minutes to Hours

minutes = 120 hours = minutes / 60 hours

Exercise 10

Objectives

• List popular languages used in data science
• List commonly used libraries
• Create arithmetic expressions
• Share the notebook via GitHub

Exercise 11

Author

Muhammad Abdullah Arif

Share Your Notebook via GitHub Download the notebook from JupyterLite:

File > Save As > Download .ipynb

Go to GitHub and:

Create a new public repository (e.g., data-science-tools-assignment)

Upload your .ipynb file

Copy the public link to your uploaded notebook (must be viewable without logging in). Take Screenshots Open your notebook in GitHub.

Take a screenshot of the first page of your notebook.

Recommended: Use Windows Alt + PrtSc or Snipping Tool to capture.