Machine Learning Data Analyst

Nicolas I. Vasquez

San Marcos, Texas, ***** 915-***-**** *******.*******@*****.*** Bilingual (English & Spanish) SUMMARY OF QUALIFICATIONS

• Proficient in programming languages such as Python, C++, and RStudio, proficient in SQL and data visualization tools such as Excel, Python, Tableau and Cognos Analytics

• Ability to perform statistical analysis, including hypothesis testing, regression analysis, machine learning etc.

• Strong problem-solving, written, verbal communication and critical thinking skills

• Ability to work collaboratively and independently WORK EXPERIENCE

Graduate Assistant /Data Analyst Texas School Safety Center, San Marcos, Texas. February 2024 – December 2024

• Collected, cleaned, and analyzed data from over 300 training sessions to evaluate training effectiveness and participant engagement

• Conducted hypothesis testing and statistical analysis to uncover insights and support data-driven decisions

• Developed dashboards and visualizations in Excel to communicate trends, performance metrics, and key findings to stakeholders

• Prepared comprehensive reports and presented data insights, tailoring findings for stakeholders to support informed decision-making

• Authored research manuscripts, contributing to publications and providing evidence-based insights on school safety initiatives EDUCATION

Bachelor of Science in Applied Mathematics Texas State University, San Marcos, Texas. December 2022 Overall GPA:3.82 Honors: Magna Cum Laude, Phi Kappa Phi Honors Society Master of Science, Major in Mathematics (Statistics Concentration) Texas State University, San Marcos, Texas December 2024 CERTIFICATES

Google Data Analytics Professional Certificate & Google Advanced Data Analytics Professional Certificate Google/Coursera

• Prepare data for exploration

• Process data from dirty to clean using Excel and SQL

• Shared data through the art of visualization in Tableau

• Data analysis with R Programing

• Translate data into insights

• Regression analysis

• Basics of machine learning in Python

IBM Data Analyst Professional Certificate Coursera

• Data analytics in Excel, Python and SQL

• Data visualization in Excel, Python, and Cognos and creating dashboards

• Databases and SQL (unions, joins, subqueries, aggregate functions, etc.) The Data Science Course: Complete Data Science Bootcamp 2024 Udemy

• A comprehensive bootcamp in data science and machine learning, covering data wrangling, data visualization, predictive modeling, and deep learning

• Gained proficiency in Python libraries such as Pandas, NumPy, and Matplotlib, as well as machine learning with Scikit-Learn and TensorFlow for building neural networks

• Worked on real-world projects, performing data cleaning, exploratory data analysis, developing predictive and deep learning models ACTIVITIES & PROJECTS

Project: Portfolio SQL Exploration Covid Data with Tableau Dashboard March 2023

• Taking COVID data from Excel spreadsheets and exploring relationships between data in SQL and creating a Dashboard in Tableau Course Project: Multiple Regression Analysis, Using Life Expectancy Data April 2023

• Taking Life expectancy data from Excel and using RStudio to conduct multiple linear regression, analysis, and hypothesis testing Credit Card Fraud Detection Using Python and Machine Learning

• Cleaned and preprocessed credit card transaction data using Python to prepare it for analysis

• Built and trained a Random Forest model to detect fraudulent transactions

• Optimized the model by tuning hyperparameters with techniques like cross-validation and grid/random search

• Assessed model performance using key metrics, including accuracy, precision, recall, and F1-score

• Demonstrated expertise in Python for data preprocessing, feature engineering, and machine learning model evaluation Breast Cancer Cell Data Analysis Using R and Quantile Regression

• Analyzed breast cancer cell methylation data to identify hypermethylated CpG sites using quantile regression at levels ranging 75% to 85%

• Investigated the epigenetic silencing of tumor suppressor genes through hypermethylation patterns

• Visualized findings with R by generating heatmaps and other plots to show the distribution of hypermethylated CpG sites

• Leveraged R for data preprocessing, statistical modeling, and advanced visualization techniques

• Contributed to understanding the role of epigenetic modifications in cancer progression

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