About Me
Hi! I'm Kevin Cañas, and I'm a Data Analyst.My focus is to create reports and visualizations that allow data-driven businesses to thrive.I prioritize creating and maintaining data and publishing visualizations to create opportunities for action.
Skills
Excel | SQL | Salesforce CRM | Tableau
Data Management - 4+ Years
Report Development - 2+ Years
Stakeholder Engagement - 3+ Years
Global Project Management - 2+ Years
Featured Projects
Excel - penguin Body and Flipper Length
Exploratory analysis of the penguins_size dataset
tableau - Global Superstore
Shipping Analysis dashboard for Global Supestore
google sheets - NHL Player Stats
2021-2022 NHL Player Statistics dashboard
Certifications
Data Analyst Career Certificate | Issued June 2025
CompTIA Data+ | Issued October 2025
Excel - penguin Body and Flipper Length
In May 2025, I explored the Palmer Archipelago (Antarctica) penguin data penguin_size dataset. This project investigated the relationship between penguin flipper length and their mass.Questions of interest
1. What was the integrity of the dataset?
2. Which variables had the strongest correlation?
3. What was the spread of the dataset's strongest correlated variables?
4. Is this data suitable for further statistical analysis?Steps for Analysis
1. Pulled the data from Kaggle and cleaned the data, ensuring any blank values were removed.
2. Calculated summary statistics to verify the integrity of the dataset.
3. Completed analysis to ensure the lack of outliers and produced a correlation heatmap.
4. Created a scatterplot of the strongest, positively correlated variables and evaluated their spread.
5. Calculated the residuals, RMSE, and standard error for the two variables.Key Takeaways
1. The dataset's degree of integrity was high and contained no outliers.
2. flipper_length and body_mass had the strongest, positive correlation.
3. Their spread was slightly positively skewed with low error.
4. Although the sample size was small, the linear model was appropriate for the variables.Click here to view the Excel Workbook or here for the report.
tableau - Global Superstore
In August 2025, I explored the Global Superstore dataset. This project visualizes different shipping methods and their profitability at different granularities for data from Jan 2014 - Jan 2018.Questions of interest
1. Which shipping methods are the most profitable?
2. What is the distribution of delivery choices amongst our segment base?
3. What is the profitability spread of product categories in the catalogue?
4. What are the yearly profit and sales trends?Steps for Analysis
1. Pulled the data from Kaggle and loaded a cleaned CSV into Tableau Desktop.
2. Created calculated measures for Profit, Profit Margin, Delivery Time, and Average Delivery Time.
3. Generated KPI blocks for Total Sales, Avg. Delivery Time, and Profit Margin.
4. Determined the appropriate visualizations for the relationships of interest in the project questions.
5. Ensured connectivity between visualizations for filtration by Shipping Mode, Segment, and State to drill down on filtration.Key Takeaways
1. The Technology category of products generated the largest, positive profit between Jan 2014 - Jan 2018.
2. The Standard shipping mode is the most popular across segments.
3. Profit has increased from Jan 2014 - Jan 2018 and sales have increased, though at a significantly slower rate.
4. The dashboard can be further leveraged or layered for regional breakdowns, categorical breakdowns, and top sender breakdowns.Click here to view the interactive dashboard.
Google Sheets - NHL Player Stats
In June 2025, I explored the 2021-22 NHL Skater Statistics data set for non-goalie hockey players from the 2021-2022 NHL season from the Hockey Reference websiteProject Focus Points
1. Which players performed the best per player metric?
2. How do multiple players compare per category?
3. Summarize data based on position and team rather than name.Steps for Analysis
1. Pulled the data from Hockey Reference and ensured data integrity.
2. Created a wireframe mockup, shown in wireframe sheet.
3. Using the filter function, created unique player and team lists for dashboard dropdown buttons.
4. Created a dynamic visualization for the designated dropdown selections.
5. Created a multi-player table for the comparison of statistics, using INDEX and MATCH functions and protecting for ERRORs.
6. Displayed summary statistics and averages for a selected metric, selected by slicers.Potential Uses
1. Compare multiple players across 1 team to evaluate performance.
2. Evaluate which player statistics are underperforming to create practice plans and lineups.
3. Create marketing/franchising around high-performing players.Click here to view the interactive dashboard.