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Jeff Luczak
Master of Science in Business Analytics
Arizona State University
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This page provides a summary of some of my proficiencies and projects I have worked on over the years.
For more, visit:
About Me
Growing up I always had enjoyment on building things with my bare hands and solving problems. While I thought I wanted to pursue a career in sports due to my love of team work and competition, that ultimately was not the correct path.
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In 2019 I decided to leave my career in sports and pursue a career in data. This was the best decision I have made. I get energized by being able to build solutions that provide value to my team members and the company as a whole. This gives me more of a sense of purpose.
Senior Business Analyst
November 2022 - Present
Managing multiple complex projects from inception to delivery
Collaborating with cross-functional teams to ensure project success
Leading Product Owner team on new project management standards
Creating Proof of Concepts through the use of Miro, Figma, Python, Tableau, and SQL to test scalability and avoid wasted effort of development teams
Business Analyst
January 2021 - November 2022
Translate business requirements into technical requirements for proper database management and reporting
Create ad hoc and customer facing reports for stakeholders to solve business problems
Ensure internal teams meet project deliverables by scoping required jobs to be done
2021 Hackathon Winner
August 2020 - December 2020
Drive operational decisions around energy management by building predictive models in Python
Collaborate with team members to meet deadlines
Social Media Analytics Intern
August 2020 - December 2020
Analyze KPIs through email marketing, social media, and website traffic to drive marketing decisions
Build visualizations/dashboards to drive insight to senior leadership and external partners
Work with multiple data sources and develop improvements to business processes
Projects
PUBG Finish Placement Prediction
Linear Regression in Python
PUBG is an online battle-royale video game where the goal is to be the last team standing.
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Given a large dataset of over 6 million rows of matches, my goal was to be able to predict each players win placement by building a predictive model off of several independent variables.
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My resulting model had an R^2 value of .76. This meant that my equation explained 76% of the variation in the data.
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Follow the URL to the left to see my work on this dataset by using Python.
Predicting Baseball Player Value
In the first class of my Master's program, I was tasked to answer a question using different visualizations in Tableau. We were given two datasets. One was regarding application purchase history and the other was a very large baseball dataset.
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Being a former baseball player, I was very excited to get my hands on this baseball data. The most difficult part of this project was coming up with a question to answer due to the vast information that was provided dating all the way back to the first year of baseball.
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After testing out different questions such as, "What makes a World Series Team?," I took a simpler approach. Being a Chicago Cubs fan due to living in Chicago until I was 21 years old, I decided to think about different debates I had while growing up. One of the biggest debates was about the massive contract the Cubs paid Alfonso Soriano in 2006.
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Alfonso Soriano in his first few years in the league was showing why he was one of the best players in the MLB. His ability to hit for power and steal bases was very rare at this time. This is what warranted the Chicago Cubs to pay him $136 million dollars over 8 years.
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Using SQL and Tableau, I analyzed how the top players in the league were compensated and how Alfonso's stats compared. My resulting analysis was that he did in fact deserve this contract. The reason this has been such a well talked about topic is the fact that Soriano injured his knees within the first few years of his contract and was never the same player.
Predicting Phoenix Murders
Being Phoenix residents, my colleague and I were wondering what the safest area was to purchase a home. Gathering data from the US Census Bureau we were looking to create a regression equation that we could use to predict murders in the city of Phoenix. This would in turn assist us in the home buying process.
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We used SQL to pre-process the data, Tableau to create heat maps, and Excel to create scatter plots which shows the correlation between each variable and the amount of murders. Once we determined the correlation between each variable, we decided whether to include that in our final regression equation.
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Our conclusion was that our regression equation only explained about 37% of the variation in murders happening in Phoenix which is not very strong. In order to better predict this crime, we would need more explanatory variables.
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Six Sigma Process Improvement
Our project was to evaluate a Design of Experiments (DOE) study of a Semiconductor assembly process to find the optimal adjustments of a new wire-bonder machine that would produce wire-bond strength above a lower specification limit of 7 grams. Using Minitab, I created x-bar, p, and r charts along with factorial analysis to see which parameters had the most effect on wire-bond strength. I also used Tableau to look at the DOE from another angle. They both showed similar results. DOUBLE CLICK EACH SLIDE TO SEE ALL INFORMATION.
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SQL Queries
Here is a quick snapshot of some queries I utilized for multiple projects in my Master's program. The purpose of this section is to showcase my ability to run advanced queries in SQL.
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Areas of Expertise:
Data Mining / Microsoft Azure
Customer Needs Assessment
SQL
Python
Microsoft Office Suite
Advanced Microsoft Excel applications / PrecisionTree / @RISK / StatTools
Business Intelligence Tools / Tableau
Customer Relationship Management
