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Explore-Weather-Trends

Udacity- Data Analyst Nanodegree

Overview

This project will analyze the local and global temperature data and compare the temperature trends to the closest city i live to overall global temperature trends.

Data extraction

  • Extract the required data from the database using SQL. Export the temperature data for the world as well as for the closest big city to where I live.The list of cities and countries are in the city_list table.

    • Write a SQL query to extract the city level data. Export to CSV.
    • Write a SQL query to extract the global data. Export to CSV.

  • Open up the CSV files using Excel or Google sheets.

Moving Averages

Calulate the moving averages for the Global data and City data. i calculated the Moving Averages in 10,15,20 years for both thr temperature data to analyze which one gives a smoother line.

Data Visualization

  • Create a line chart that compares your city’s temperatures with the global temperatures. Make sure to plot the moving average rather than the yearly averages in order to smooth out the lines, making trends more observable.

Observation

  • Observe the similarities and differences between the world averages and the city’s averages, as well as overall trends.
    • Is your city hotter or cooler on average compared to the global average? Has the difference been consistent over time?
    • “How do the changes in your city’s temperatures over time compare to the changes in the global average?”
    • What does the overall trend look like? Is the world getting hotter or cooler? Has the trend been consistent over the last few hundred years?

Source

Credit to Udacity for the data. The Licensing for the data and other descriptive information at Udacity Website in Udacity Data Analyst Degree in Project I: Explore Weather Trends.