SQL/Tableau Developer

Sean Cancino

** ***** *****, ****** ** *****

[ ******@****.*** ® secant78.github.io/confuseanportfolio/

sean-cancino secant78 Ó 302-***-****

SKILLS

SAS

Python

Hadoop

Pandas

R

Microsoft Excel

Power BI

SQL

Java

Tableau

Javascript

AWS

Machine Learning

Latex

Git

EDUCATION

M.Sc. Statistics

University of Delaware August 2019 – June 2021 B.Sc. Neuroscience

University of Delaware August 2011 – June 2015 CERTIFICATIONS

SAS Base Certification

AWS Certified Developer

RELEVANT PROJECTS

Philadelphia Crime Data Visualization

University of Delaware Department of Statistics August – May 2020 Newark, DE

• Imported crime data from OpenDataPhilly’s public datasets in order to find insights on various aspects of Philadelphia’s crime incidents over the past 5 years.

• Used Power BI to create a dashboard containing visualizations such as the hours of the day crimes were most likely to occur, which locations were crimes most likely to occur, and what kinds of crimes were the most common. Election 2020 Demographics

University of Delaware Department of Statistics August – December 2020 Newark, DE

• Cleaned and formatted data sets from the Bureau of Labor Statistics, Kaggle, and the Census Bureau website in order to merge the data sets.

• Combined data sets containing election outcomes in different counties, labor force statistics, and population demograph- ics using proc sql inner join on the "county" variable in order to do analytics on the election outcomes.

• Generated multiple visualizations such as pie charts and bar plots using Excel pivot tables in order to examine relation- ships and trends between unemployment rate, race, and election outcomes in different counties in the US. Survival Analysis of NBA Player Career Length

University of Delaware Department of Statistics August – October 2020 Newark, DE

• Using data from Kaggle’s NBA Player’s Stats data set (https://www.kaggle.com/drgilermo/nba-players-stats), performed exploratory data analysis on the data set by creating histograms that showed the counts for each variable and scatter plots to determine the relationships between different variables.

• Cleaned the data set by removing variables that had a majority of missing values and changing variable names.

• Created survival curves that showed the how long an NBA player’s career was predicted to be based on factors such as how many points they scored each season, and how many games they played each season.

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