Utilized SQL to clean a large dataset containing housing information.To achieve this, I leveraged various types of SQL queries such as the CASE statement to create conditional expressions, the PARSENAME function to extract specific parts of a column, the UPDATE statement to modify existing data, the DELETE statement to remove unwanted data, and the ALTER TABLE statement to add or remove columns as needed, all of which helped me to transform a messy and unstructured housing dataset into a clean and well-organized one that was suitable for analysis and modeling.
Utilized SQL to explore and extract insights from a large dataset containing COVID-19 information by writing queries that employed various techniques such as the NOT NULL constraint to eliminate null values, the LIKE operator to search for specific patterns in the data, the ORDER BY clause to sort the data in a specific order, and the GROUP BY clause to group data based on specific columns and aggregate information. Additionally, I employed more advanced techniques such as the PARTITION BY clause to partition the data and perform calculations within each partition, the JOIN statement to combine multiple tables to extract more complex insights, the Common Table Expressions (CTE) to simplify complex queries, and temporary tables to store intermediate results for more efficient processing, all of which helped me to extract valuable insights from the COVID-19 dataset.
utilized Python and various libraries such as Pandas, NumPy, and Matplotlib on Google Colab to explore and analyze a large dataset containing gun violence information by writing queries that extracted valuable insights such as cases by year, fatalities, mental health issues, age group, and other relevant factors.Overall, this project showcased my ability to use Python and its associated libraries to perform data analysis and generate visualizations that help to better understand the prevalence and impact of gun violence base on the provided data.
utilized Power BI functions such as the Data Analysis Expressions (DAX) language to create measures and calculated columns, connect tables, and visualize the data using a wide range of tools such as cards, heat maps, bar charts, and slicers to effectively communicate the impact of the COVID-19 pandemic globally. The Global COVID-19 Dashboard on Power BI,provides insights on cases by country, total death by country, total recovered, active cases and other relevant metrics, thereby enabling policymakers and stakeholders to make data-driven decisions and formulate effective strategies to combat the pandemic.This project showcases my ability to leverage the full capabilities of Power BI to design and create compelling data visualizations that help to better understand complex global issues such as the COVID-19 pandemic.
Utilized SQL on BigQuery to explore and analyze a large dataset containing global and health population information by writing queries that leveraged various techniques such as the UNNEST function to split a column into multiple rows, the WHERE clause to filter the data based on specific conditions, the CAST function to convert data types, the PARTITION BY clause to perform calculations within each partition, and the Common Table Expressions (CTE) to simplify complex queries. Additionally, I employed other advanced techniques such as the COALESCE function to handle null values and the JOIN statement to combine multiple tables to extract more complex insights.
Analyzed a US Sales dataset and designed an Excel dashboard that highlights significant aspects of the sales team. The dashboard displays sales performance by region in a bar chart format with a slicer to highlight each region. A table with the top five clients is also visible, and a bar chart with a slicer shows which salesperson performed the best. Finally, a heat map was used to visualize sales performance across US.
This interactive dashboard created on PowerBI showcases the results of a survey conducted among data professionals.leveraged Power BI functions such as the Data Analysis Expressions (DAX) language to create measures and calculated columns, and visualize the data using a wide range of tools such as cards, donut charts, bar charts, and slicers to effectively communicate the results of a data professional survey. Through the creation of a data professional survey dashboard on Power BI, I was able to provide valuable insights on data professional's work experience, salary range, job satisfaction levels, and other relevant metrics, thereby enabling individuals and organizations to gain a better understanding of the data professional landscape. Overall, this project showcased my ability to leverage the full capabilities of Power BI to design and create compelling data visualizations that help to better understand the data professional industry, and provide insights for data-driven decision-making.