Core Technologies: Python, MySQL, Spark, Tableau, Pandas, NumPy, Matplotlib, Seaborn, SciKitLearn, Anaconda, Jupyter Notebooks, Git/GitHub
Core Competencies: Data Storytelling, Applied Statistics, Machine Learning, Natural Language Processing, Classification, Regression, Clustering, Time Series Analysis, Anomaly Detection
Hey there! If you’re looking for someone who can do more than just clock in and out, then I’m the perfect match for your team. Hiring me means inviting a dynamic individual who thrives on making a difference, constantly seeking opportunities to learn and grow, and inspiring others to do the same.
I don’t settle for mediocrity; I believe in pushing the boundaries and challenging the status quo. Embracing new challenges excites me because I know that’s where real growth happens – both for myself and the team around me. My passion for personal and professional development means I’ll never stop pushing the envelope, constantly seeking ways to enhance our collective potential.
With a wide range of competencies at my disposal, I bring a diverse set of skills to the table. From strategic thinking and problem-solving to creative ideation and effective communication, I’m always eager to contribute and collaborate with my team members.
But what truly sets me apart is my ability to foster a positive and inclusive work environment. I believe that a thriving team is one that supports and empowers each other, where everyone’s unique talents are recognized and celebrated. Together, we can create an atmosphere where innovation flourishes, and accomplishments are shared by all.
If you’re ready to invest in a team player who’s driven by purpose, values growth, and cherishes the power of teamwork, then I’m ready to join your ranks. Let’s embark on this journey together and make a lasting impact in the workplace – the kind that leaves a ripple effect of success and fulfillment.
My Capstone Project: Pigskin Paydays
In this project, we aimed to predict the percentage of a team’s budget allocated to NFL quarterbacks based on their statistics. We gathered data from various sources, including player stats, contract history, and even player commentary, to gain insights into what factors influence a quarterback’s worth.
We started by exploring different hypotheses, like how leading the team to the playoffs or having more passing yards and touchdowns could impact a quarterback’s percentage cap. After combining and preparing the data, we delved into univariate and bivariate analyses to understand the relationships between the selected features and the target variable.
Through our modeling process, we tried out various regression models and found that the Ordinary Least Squares (OLS) model was the most effective, outperforming the baseline by 1.4 points with an RMSE score of 5.74 on the unseen test data. We also highlighted the importance of considering both measurable and intangible attributes like leadership skills to further refine our predictions.
Ultimately, our project provides valuable insights for teams and negotiators, helping them make informed decisions about player contracts and budget allocations. By continuing to improve and expand our model, we can extend its applicability to other positions beyond quarterbacks, making it an even more valuable tool for teams across the league.