DATA SCIENCE USING JUPYTER NOTBOOK #Notebook #Introduction IN this project we will learn how to use jupyter notebook for Data Science #objective
Objective: Demonstrating the Efficacy of Jupyter Notebook for Data Science This project aims to showcase the capabilities of Jupyter Notebook as a comprehensive tool for data science workflows. By incorporating code, visualizations, and narrative text within a single environment , we will demonstrate how Jupyter Notebook facilitates exploratory data analysis, model building, and result interpretation.
Key areas of focus will include: Interactive coding: Executing Python code and observing immediate outputs. Data visualization: Creating various plots and charts to uncover patterns and trends. Narrative integration: Explaining the code and results through descriptive text. Reproducibility: Sharing the notebook to enable others to replicate the analysis. Through this project, we will highlight the efficiency and effectiveness of Jupyter Notebook in supporting the entire data science lifecycle.
#list of the language use for data science 1.python 2.R 3.mysql 4.c++ 5.java 6.julli
#data science libraires python 1.panda 2.scikit 3.numpy 4.Matplotlib. R 1.Ggplot2 2.Shiny 3.Caret 4.Dplyr
#Data Science Tools -Apache Spark -Jupyter Notebook -TensorFlow -Seaborn -Matplotlib -PyTorch -Apache Hadoop
#Arthmatic expression consists of arithmetic terms, such as x, x2, xy, or 3xy2, combined by arithmetic operations, such as addition, subtraction, multiplication, and division.
#MULTIPICATION x = 3 y = 2 print( x*y) output 6
#code to convert minutes in hours def minutes_to_hours(minutes): """Converts minutes to hours.
Args: minutes: The number of minutes to convert.
Returns: The equivalent number of hours as a float. """
return minutes / 60
minutes = 150 hours = minutes_to_hours(minutes) print(f"{minutes} minutes is equal to {hours} hours")
output : 150 minutes is equal to 2.5 hours
#AUTHOR shivani sharma