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.

Project-Data-Visualization

This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, we will use Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, we will produce a short presentation that illustrates interesting properties, trends, and relationships that we discovered in your selected dataset. The primary method of conveying your findings will be through transforming our exploratory visualizations from the first part into polished, explanatory visualizations

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 live