- Processes remote sensing weather data
- For all the cities in the database, latitute and longitude coordinates are extracted
- Using Django REST api, a call is made to the METAR API to fetch the METAR code for each city
- Statistical analysis skills:
- Data Gathering: Developed a python script for a backup mechanism to scrape data from public weather data warehouses
- Data Cleaning: The scraped data contains a lot of missing information, from error-prone HTML to unescaped spaces. Several data cleaning methods were developed using Regexp
- Categorization: After decoding the METAR code, the remote sensing weather data was categorized into "good", "marginal" and "bad" weather using variables such as temperature, pressure and visibility
- Statistical Summary: Utilized descriptive statistics such as mean, mode, interquartile range, variance, standard deviation and range to calculate additional metrics
- Time Series Analysis: Considered historical data to identify patterns within the usual weather cycle
