How do you how make sense of a thousand columns and a million rows? You start by cleaning it. In this data cleaning project I used Texas cosmetology licensure data Texas data sources and geographical/socioeconomic data from the U.S. Cenus to create a database, clean it, and condense to a manageable size in preparation for analysis. See my code, the walkthrough narrative, and screenshots on GitHub.

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In this project I cleaned housing data using SQL Server. I standardised discrepant data, divided joined data into more manageable columns, removed duplicates, and populated missing data based on context clues Follow the story and let me walk you through my process on GitHub.

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In this project I used SQL Server to extract and analyse COVID-19 vaccination, case, and death data using SQL. I compared deaths to population size to predict likelihood of dying by COVID in each country. I also calculated the total percentage of population infected by COVID-19 in Ireland and analysed global infection rates and vaccination by country, continent, and population size. The data source is updated daily at https://ourworldindata.org/covid-deaths.

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