A Data Analyst, and US Army Veteran, Before making the change to Data analytics I've worked in various roles as a Pharmacy Technician ranging from retail to wholesale, inpatient, instructing, and sales. I've completed a four months fulltime 'Data Analytics Immersion Bootcamp' at Thinkful. My skillset ranges from Excel (Pivots, Formulas, Visualization, Analysis, Statistics) SQL, Python(Pandas, Numpy, Seaborn), data cleaning, data querying and Tableau. Currently a student at Thomas Edison State University for Mathematics and Minor in Computer Science as I continue to progress in my career.
⚡ Fun fact: I love biking, cars, drawing, writing, cooking, and I increase employee morale by 52.3% 😎
In this overview I will be updating my portfolio, projects and webpage.
Unique traits: Public speaking was a favorite course in college, instructor experience, being an instructor for 3 years helped me learn how to teach, explain information as simple as possible
Airborne Paratrooper: Airborne school has a 60% attrition rate, and only 15% of enlisted soldiers are Airborne qualified, which i've achieved.
Worked in prison more pressure, hostile environments, the unknown can happen at any time
Pharmacy technician; working with patients, phi and medication keeps me detailed oriented, accurate and effienent.
Security clearance; working with military intelligence and access and protect confidential information/data.
Currently: DIY my college degree learning and teaching myself is a skill that I've come to hone and improve upon while I pursue my degree with credit by exam.
Webpage, and Data Analyst Certifications...
Specifically working in python and Streamlit converting the projects up into an interactive web application.
Business Analysis - Revenue Growth Models
Completed exploratory data analysis to identify strategic scenarios to increase revenue, assessed by KPI
performance and solutions to increase profits while lowering operating costs for the next fiscal year. In process of this data, I cleaned the data, and separated profits down to location. The locations were focused areas based around airports and non airport locations.
A key, action that was implemented in this project, I removed all products that had a net negative return, increasing profits of the company by 270K. Before implementing any further strategies.
Business Analysis - Revenue Growth Models
In this project we have a multiple spread sheets from a rental car company “Lariat” the data ranged from locations, customer data, sales numbers, vehicle models, profit and loses, weather or not the vehicle got into an accident and it spread across multiple spreadsheets.
Going through the data, I found common variables that i can link them into one workable sheet. And later used pivot tables to come to some findings. For the data. Some of the data presented had some missing variables and mixed values, and nontraditional formatting.
The data has been cleaned and used to create a minimal dashboard.
I used index match, Vlookups for a majority of the data and sorting.
In the dashboard. The user can change the cells highlighted in yellow with values from 1 to 30% to see the implemented strategies and changes in the projected outputs.
At the end of the analysis, I've come to realize the data collected on the customers wasn't sufficient enough create better strategies.
Such as, customer IDs, to track how many repeat customers are apart of the income the company makes, and how we can capitilize on thes repeat customers. A further analysis I want to conduct: Some customers crash the vehicles, and how does this impact the company.
Statistical Analysis - Fuel Economy Annual Fuel Costs
In this scenario, I analyzed a Fuel Economy Data set provided by Fueleconomy.gov
After reviewing the data, it was previously cleaned so I was able to go straight into analyizing and creating pivot tables, filtering on providing information requiring the findings to the consultation firm.
In the analysis, the focus was on the annual fuel cost, in this case I focused on vehicle classes (Compact, Medium and Large)
I conducted T-Test to find the differences between the classes.
In addition from the results of the vehicle classes, I conducted a further analysis of the transmission type of the most cost effective vehicle class.
Statistical Analysis - Fuel Economy Annual Fuel Costs
Conduct statistical analysis on alternative fuel types, and annual fuel costs. Conduct electric vehicle effiency vs. hybrid vehicles Which company provides the most fuel effective vehicles.
Python Data Analysis - Currency Correlations
Unlike the stock market, where each stock has some correlation, it can be easily a result of causation as the markets are driven by emotion and speculation. In the Foreign exchange markets its based off a correlation as the prices of each currency is in relation to another. i.e. the USD value increases but in relation to another currency. In this analysis, we compare each pair, to another pair, which we find correlations of these pairs with in both positive and negatively.
Python Data Analysis - Currency Correlations
