Jessica Claire
, , 609 Johnson Ave., 49204, Tulsa, OK 100 Montgomery St. 10th Floor
Home: (555) 432-1000 - Cell: - - : - -

Highly-motivated employee with desire to take on new challenges. Strong worth ethic, adaptability and exceptional interpersonal skills. Adept at working effectively unsupervised and quickly mastering new skills.

  • Programming Languages: Java, Python, R and RStudio, SPSS. Experience with Git and GitHub, Experience with Pycharm, Eclipse, Visual Studio Code
  • Analytical and Critical Thinking, Problem-Solving, Teamwork and Collaboration, Data Management
Education and Training
Expected in 2024 to to
Bachelor of Science: Computer Science And Applied Statistics Minor
University of St.Thomas - St.Paul,
  • Fall 2020 - Dean's List
  • 3.6 GPA
  • QPR GateKeeper Training for Suicide Prevention, Club Officer training, Zoom and classroom technology training, and Resident Life- Student Leader and Active shooting training
  • Additional Courses Taken: Calculus I, Calculus II, Introductory Statistics(Statistics I).
06/2023 to Current
Hardware and Software Support Intern Lake Health Urbana, OH,
  • Updated software versions with patches and new installations to close security loopholes and protect users. Assessed issues to determine appropriate troubleshooting methods for remediation. Installed wiring, cabling and devices to establish, repair and improve network operations.
09/2022 to Current
Student Technician Universal Health Services Donalsonville, GA,
  • Answered user inquiries to resolve computer software or hardware operation problems. Installed and performed minor repairs to hardware, software or peripheral equipment.
08/2022 to Current
Resident Advisor University Of St.Thomas City, STATE,
  • Led weekly meetings to address resident concerns and educate on changes to policies and procedures. Responded to crisis situations quickly to maintain calm and immediately determine level of assistance needed.
Related Courseworks

Python Project:

  • Introduction to Numeric Types; Turtle Graphics; Simple Loops; Functions, Introducing the Python Collections, Bigger Data; File I/O, Recursion, Random Numbers (Loops: While, For); Conditional statements
  • Objective: Learn to work collaboratively within a team; Implement the theories learnt from lecture to practical problems. Learn the basic syntax of python code. For example, how to write print statements in python.

Java (OOP) Projects:

  • Bubble, Quick, Selection, merge and Binary Sorts; Reversing Arrays; Loan Calculator; Efficiency Testing; Random Walk; Crap Game; Comparable Interfaces; Creating own sorting Algorithms.
  • Objective: Learning the basics of how the sorting methods work, and finding with method is the most efficient one to consider. Learnt the basics of Java and how it works, by going more detailed into the syntax of java.

Data Structures Java Projects:

  • Array Lists; Linked Lists(Big O, Growth Rates); Recursion and Linked Lists; Abstract Lists; Stacks; Queues; Priority Queues; Sorting Algorithms; Binary Search Trees and Sets; Hash Tables and Sets.
  • Objective: Learnt the basics of different data structures, implemented them in lab assignments, by implement some of methods in the files. Understood the theory behind these data structures and how to determine when to use which one. Learnt how to work independently on difficult projects and successfully complete them.

Computational Statistics and Data Analysis:

  • Exploratory Data Analysis; Exploratory Factor Analysis; Structural Equation Modeling
  • Objective: Learning how to analysis large data sets. Finding the most significant predictor in a model. Learnt to draw path diagrams and how to analyze it. Implemented the different types of analysis into a final project for the course. Learnt how to work in groups. Implements the theories done in class to do solve some of the problems related to Statistics.

Applied Regression Analysis:

  • Introduction to models; Simple linear regression;Inference for simple linear regression; Intro to multiple linear regression; ANOVA; Intro to logistic regression; Multiple logistic regression and data wrangling; Time series.
  • Objectives: Learnt how to construct a linear and multiple linear models using R programming language. Learnt how to interpret coefficients in a model. Learnt the basics of data wrangling, and practiced in a final project data set. Also covered how to analyze time series and how to present them in a simple way possible. Learnt how to use the Anova table to interpret results and come to a conclusion.

Additional Coursework in GitHub:

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Resume Overview

School Attended

  • University of St.Thomas

Job Titles Held:

  • Hardware and Software Support Intern
  • Student Technician
  • Resident Advisor


  • Bachelor of Science

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