Early-career candidate focused on learning, building skills, and creating portfolio work to move into a professional role.
A collection of personal projects, experiments, and practice work that demonstrate my current skills and areas I am developing.
Analyzed 8 years (2015–2022) of West Campus noise complaint records to identify year-over-year trends in complaint volume. Queried and aggregated raw complaint data using SQL, then cleaned and visualized results in R to surface trends in complaint timing and frequency.
Analyzed a 1,740 employee HR dataset to identify key drivers of attrition using exploratory data analysis and logistic regression in R. Found overtime status to be the strongest predictor of attrition, increasing the odds of leaving by approx. 4.4x; addressed class imbalance in the target variable to improve model evaluation beyond raw accuracy.
Early-career candidate focused on learning, building skills, and creating portfolio work to move into a professional role.
I'm Arya Adhikary. I am at the start of my career, with limited formal work experience and a growing interest in building practical skills and projects to prepare for professional roles.
Experience
Director of Marketing & General Member
Bright Stars of Texas, Austin, TX · August 2026 - Present
FIG Mentor
The University of Texas, Austin, TX · January 2026 - Present
Founder
Uplift With a Gift, Dallas, TX · April 2022 - Present
Orientation Counselor & Community Relations Committee at Camp Texas
Texas Exes, Austin, TX · January 2026 - August 2026
Education
The University of Texas at Austin, Austin, TX
Bachelor of Arts, Informatics & Psychology · Class of 2028
Skills
Analyzed 8 years (2015–2022) of West Campus noise complaint records to identify year-over-year trends in complaint volume. Queried and aggregated raw complaint data using SQL, then cleaned and visualized results in R to surface trends in complaint timing and frequency.
Analyzed a 1,740 employee HR dataset to identify key drivers of attrition using exploratory data analysis and logistic regression in R. Found overtime status to be the strongest predictor of attrition, increasing the odds of leaving by approx. 4.4x; addressed class imbalance in the target variable to improve model evaluation beyond raw accuracy.
Conducted exploratory data analysis and built a logistic regression model to predict passenger survival using the Kaggle Titanic dataset. Identified sex, passenger class, and age as statistically significant predictors, achieving 80% classification accuracy.