Rojin-Data Analyst Expert

Rojin-Data Analyst Expert


Visit all my SQL projects 


SQL (Structured Query Language) is a cornerstone of my data analysis and database management expertise. Here’s a detailed look at how I utilize SQL in my work:

  • Data Extraction and Manipulation: Crafting complex queries to extract, join, and manipulate data from relational databases such as MySQL, PostgreSQL, and Microsoft SQL Server. This ensures precise and efficient data retrieval.

  • Database Management: Creating and managing database schemas, tables, views, and indexes to optimize data storage and access. Ensuring that databases are well-structured and performance-optimized.

  • Stored Procedures and Functions: Developing stored procedures and functions to automate repetitive tasks and complex calculations, enhancing efficiency and reducing manual effort.

  • Data Analysis and Reporting: Generating detailed reports and dashboards by leveraging SQL to provide actionable insights that drive business decisions. Using various aggregation and filtering techniques to summarize and analyze large datasets.

  • Data Integration and ETL Processes: Designing and implementing ETL (Extract, Transform, Load) processes to integrate data from multiple sources. Ensuring data quality and consistency throughout the data pipeline.

  • Performance Tuning: Optimizing query performance through indexing, partitioning, and query optimization techniques. Ensuring that SQL queries run efficiently even with large datasets.

  • Data Security and Compliance: Implementing data security measures, including user permissions and roles, to ensure data privacy and compliance with regulations.

  • Advanced SQL Techniques: Utilizing advanced SQL techniques such as window functions, CTEs (Common Table Expressions), and subqueries to perform complex data operations and analysis.

  • Collaboration and Documentation: Working closely with cross-functional teams to understand data requirements and deliver SQL-based solutions. Documenting SQL code and data models to ensure clarity and maintainability for future use.

These capabilities enable me to turn vast amounts of data into actionable insights, drive business strategies, and ensure efficient database management. My SQL projects demonstrate my proficiency in solving complex data challenges and delivering high-impact results.

COVID-19 Data Exploration


Project Overview

This project utilizes a dataset sourced from COVID-19 statistics, comprising various columns like total cases, new cases, total deaths, population, and vaccination data. The goal was to analyze the impact of COVID-19 across different continents and countries, providing insights into infection rates, death rates, and vaccination progress.


Objective

To analyze the COVID-19 data to understand infection and death rates over time, identify patterns across different locations, and evaluate the effectiveness of vaccination campaigns.


Methods

  • Data Validation and Cleaning: Ensured data accuracy by validating and cleaning 15,000 rows and 8 columns, resulting in 13,926 rows after removing rows with missing values and correcting inconsistencies.

  • Exploratory Data Analysis (EDA): Applied EDA techniques to gain insights, visualize findings, and identify key relationships between features.

  • Trend Analysis: Analyzed infection and death rates over time for each location.


Results

  1. Data Validation:

    • Ensured continent and location columns were not null.

    • Consolidated and cleaned data for accurate analysis.

  2. Infection and Death Rate Analysis:

    • Total Cases vs. Total Deaths: Analyzed the death percentage relative to total cases.

    • Cases vs. Population: Calculated the percentage of the population infected by COVID-19.

  3. Countries with Highest Infection and Death Rates:

    • Identified countries with the highest infection rates relative to their population.

    • Analyzed death counts per population across different continents.

  4. Global Trends:

    • Aggregated global case and death counts over time.

    • Analyzed the effectiveness of vaccination campaigns using rolling sums and cumulative counts.

Insights

  • Countries with higher infection rates required more aggressive measures.

  • Vaccination campaigns significantly impacted reducing infection and death rates.

  • Continuous monitoring and timely data updates are crucial for managing public health responses.


Conclusion

The analysis validates the dataset, highlights key trends in infection and death rates, and evaluates the progress of vaccination campaigns. These insights are critical for public health decision-making and resource allocation.

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Cleaning Data in SQL Queries


Project Overview

This project involves cleaning and organizing a dataset from Nashville housing data using SQL. The data includes various columns such as sale date, property address, owner address, and more. The objective was to standardize data formats, fill in missing data, and prepare the dataset for analysis.


Objective

To clean and organize the Nashville housing dataset, ensuring consistency and accuracy in the data. This involves standardizing date formats, populating missing property addresses, breaking out address components, and removing duplicates.


Methods

  • Data Standardization: Standardized date formats and ensured consistency across the dataset.

  • Data Population: Filled in missing property addresses by joining tables on parcel IDs.

  • Data Splitting: Split addresses into individual columns for more detailed analysis.

  • Categorical Data Cleaning: Converted 'Y' and 'N' values to 'Yes' and 'No'.

  • Duplicate Removal: Identified and removed duplicate records.

  • Column Management: Removed unused columns to streamline the dataset.

  • Data Import: Used OPENROWSET and BULK INSERT for efficient data import.

Results

  1. Data Standardization:

    • Standardized the SaleDate column to a consistent date format.

    • Created new columns for converted sale dates.

  2. Data Population:

    • Populated missing PropertyAddress values using a self-join on ParcelID.

    • Ensured all properties have addresses for accurate location tracking.

  3. Address Components:

    • Split PropertyAddress into PropertySplitAddress and PropertySplitCity.

    • Split OwnerAddress into OwnerSplitAddress, OwnerSplitCity, and OwnerSplitState.

  4. Categorical Data Cleaning:

    • Converted 'Y' and 'N' values in SoldAsVacant to 'Yes' and 'No' for better readability and analysis.

  5. Duplicate Removal:

    • Used a Common Table Expression (CTE) to identify and remove duplicate records based on ParcelID, PropertyAddress, SalePrice, SaleDate, and LegalReference.

  6. Column Management:

    • Removed unused columns (OwnerAddress, TaxDistrict, PropertyAddress, SaleDate) to clean up the dataset.

  7. Data Import:

    • Configured server settings for efficient data import using BULK INSERT and OPENROWSET.

Conclusion

The project successfully cleaned and organized the Nashville housing dataset. Standardizing date formats, filling in missing property addresses, splitting address components, and removing duplicates improved the dataset's consistency and accuracy. This prepared the data for subsequent analysis and reporting.

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Rojin-Data Analyst Expert

Rojin-Data Analyst Expert