Hello World!

I’m Julia!

Current student at University of Indianapolis and Analytics Intern @Woodruff.

About Me

Student at the University of Indianapolis pursuing my master’s in Applied Data Analytics and looking for opportunities to learn more about the business world. Enthusiastic about solving problems and helping others to thrive. The courses taken so far involve analytical thinking and proficiency in different statistical software to perform analysis. The pursuit of an undergraduate degree in Mathematics with a minor in Business contributed to helping me develop my problem-solving skills, being able to analyze many different mathematics backgrounds.

Student-Athlete for five seasons (2017-2022), President of the Impact Club (2020-2021), and part of the Student-Athlete Advisory Committee(2020-2021).

Academic Background

Professional Background

Internship experience:

Analytics Intern | Woodruff (June 2022 - Present)
* Built Dashboards to visualize core business KPIs reducing the time spent on reports.
* Collaborated by providing analysis and data (e.g. client’s website re-design, and email campaigns).
* Aggregated unstructured data from 50+ sources to build the foundations for client reporting.
* Set metrics on GTM and GA4 to track website traffic as well as resolve technical issues.
IoT and Data Intelligence Intern | Logicalis (July 2021 - August 2021)
* Worked with another summer intern on research development on new technologies for intelligent building.
* Presented a final project for the Latin America executive board.
* Interacted with projects and technology companies (AWS, Azure, E-magic, Cisco, and others).

What I have been involved with:

Tutor | University of Indianapolis (September 2021 - Present)
Mathematics Tutor | Academic Resource Center (August 2019 - August 2021)
Manager Assistant | F3 Logistics (May 2020 - August 2020)
Teacher Assistant | Emmanuel College (August 2019 - May 2020)

Experience with R

  • So far R has been used for many projects in class as well as weekly assignments.
  • Experience by using for data cleaning and preparation, Visualizations, Machine Learning and AI.
    Projects:
    Predicting House Prices
    Using the House Market data, it was develop models to predict house prices based on 78 columns and 1600 rows of data. The model was created using Linear Regression, Bagging, Random Florest and XGBoost.
    Brokerage Data - Leverage Prediction
    Class Week 4

Experience with other analytic software

  • Languages: R, SQL, Python
  • Visualization Tools: Tableau, Data Studio, Adverity
  • Analytics Softwares: SAS, Google Analytics, Google Tag Manager, BigQuery