Pulled bike manufacturing data from an online course and transformed the raw data in Power BI's Power Query, created a relational data model, added calculated fields with DAX, then visualized the data with reports to track KPIs, compare regional performance, analyze product-level trends, and identify high-value customers
As a music lover, I was curious to better understand why I love the music I listen to so I extracted a playlist of songs from Spotify's API, cleaned the data in Excel, then visualized the information in Tableau - feel free to choose and analyze an artist!
In this paper, I take you through my process of utilizing R Studio to clean, analyze, and interpret one of the largest heart disease datasets available to help predict the likelihood of a person having high cholesterol; here's a link to the code I created in R Studio to perform the analysis
Leveraged cluster analysis in JMP and data visualization in Tableau to guide the placement of new schools in Bolivia's most impoverished areas, directly impacting children's lives and their communities -
(We presented to both English and Spanish speakers which is why we have both translations on the slides)
With a passion for health and fitness, I created a model in SAS Enterprise Miner that could predict the likelihood of patients having a heart disease
Created a Linear Programming Model within Excel which optimizes the number of macronutrients and calories under specified constraints; combined personal and scientific data to create variables which serve as an objective function to achieve a desired output
Cleaned Nashville Housing data in SQL by removing duplicates, replacing NULL values, and normalizing the information to make it more usable for future analysis and visualization
Analyzed movie data to find statistically significant correlations through Pandas to create and manipulate a data frame, NumPy for statistical analysis, and Matplotlib/Seaborn for data visualizations