Joseph Noone’s Post

This SQL project was tough, but patience is definitely a virtue. Another project from Alex the Analyst, this dataset is from Nashville Housing (I can't recall what year), but it was really interesting nonetheless! #microsoftsqlserver #dataanalytics #inspiringdatanalyst

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🚀 Exciting Data Cleaning Project Update! 🚀 Just completed a thorough data cleaning process for the NashvilleHousing dataset! 🧹💻 Project link:-https://lnkd.in/gk7Jszvx Here's a glimpse of the SQL queries I used to ensure the data is accurate and standardized: 1. Standardized Sale Date Format: Altered the SaleDate column to ensure a consistent date format. 2. Populated Property Address Data: Filled in missing PropertyAddress values by performing a self-join on ParcelID, resolving NULLs with existing data. 3. Broke Down Address into Individual Columns: Separated PropertyAddress into Address and City, creating new columns for better analysis. 4. Separated Owner Address Components: Utilized the PARSENAME function to split OwnerAddress into Address, City, and State components. 5. Changed 'Y' and 'N' to 'Yes' and 'No': Updated the 'Sold as Vacant' field for clarity, replacing 'Y' with 'Yes' and 'N' with 'No'. 6. Removed Duplicates: Deleted duplicate entries based on specified criteria using ROW_NUMBER(). 7. Deleted Unused Columns: Removed unnecessary columns like OwnerAddress, TaxDistrict, and PropertyAddress. This process ensures our data is accurate, standardized, and ready for analysis! 💼📊 #DataCleaning #SQL #DataAnalysis #NashvilleHousing #DataQuality #LinkedInUpdate

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 Can you write a query to fetch the number of employees working in project ‘ABC’ from the employee data table? ANS :- Knowledgeable applicants will understand that they should use their query’s aggregate function count() and the where clause to fetch the required results. The count() function returns the rows that match the developer’s criterion: SELECT Count(*) FROM EmployeeData WHERE Project = ‘ABC’; #SQL,#POWER BI,#DATA

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I did something amazing this year! 🌟 In my journey as a data analyst, I tackled the Nashville Housing Dataset and cleaned it using SQL queries. Let me walk you through the steps I took to ensure the data was accurate and ready for analysis. 1️⃣ Converting String Date to Date datatype was the first task on my list. This ensured consistency and efficiency in handling date-related queries. 2️⃣ Updating missing values in the Address Column was crucial for the dataset's completeness. By filling in these gaps, we improved the overall quality of the information. 3️⃣ Breaking out PropertyAddress and Owner Address into individual columns made the data more organized and easier to work with. This separation allowed for better analysis and visualization of the property and owner details. 4️⃣ Changing 'Y' and 'N' to 'Yes' and 'No' in the 'SoldAsVacant' field provided clarity and improved the dataset's readability. 5️⃣ Removing duplicates and redundant columns streamlined the dataset, eliminating unnecessary information and reducing clutter. This Nashville Housing Dataset, with its 56447 records, contains valuable insights into property details and ownership information. By cleaning this data effectively, we set a solid foundation for further analysis and decision-making. 🔍 Have you ever cleaned a dataset using SQL queries? What challenges did you face, and how did you overcome them? Let's discuss! #DataCleaning #SQLQueries #NashvilleHousingDataCleaning Visual Idea: An image showing before and after snapshots of the dataset cleaning process, highlighting the transformation from messy data to clean, structured information. GitHub Link: https://lnkd.in/d6SkEHZC