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Data-Cleaning-Process
Cleaning our data is the third step in data wrangling. It is where we fix the quality and tidiness issues that our identified in the assess step. In this training, we'll clean all of the issues we identified in using Python and pandas. This Jupyter Notebooks will be structured as follows: we'll learn about the data cleaning process: defining, coding, and testing we'll address the missing data first (and learn why it is usually important to address these completeness issues first) we'll tackle the tidiness issues next (and learn why this is usually the next logical step) And finally, we'll clean up the quality issues This training will consist primarily of Jupyter Notebooks we will leverage the most common cleaning functions and methods in the pandas library to clean the nineteen quality issues and four tidiness issues identified .Business-Analytics-Malaria-Project
This project is aimed at analysis using data visualization in Tableau. , As A large part of working with data is being able to interpret data visualizations and explain our insights to others.Data_Wrangling_Project
Introduction Real-world data rarely comes clean. Using Python and its libraries, we will gather data from a variety of sources and in a variety of formats, assess its quality and tidiness, then clean it. This is called data wrangling. we will document our wrangling efforts in a Jupyter Notebook, plus showcase them through analyses and visualizations using Python (and its libraries) and/or SQL.Explore-Weather-Trends
In this project, we will analyze local and global temperature data and compare the temperature trends where we live to overall global temperature trends. Instructions the goal will be to create a visualization and prepare a write up describing the similarities and differences between global temperature trends and temperature trends in the closest big city to where we liveLove Open Source and this site? Check out how you can help us