Business Analytics Systems Analyst

Adrian William Vasquez

https://github.com/avasquez9999 ****************@*****.*** 657-***-**** https://www.linkedin.com/in/adrian-vasquez-a4993660/

Professional Summary

Highly motivated and detail-oriented Master of Science in Information Systems graduate with a strong background in Business Analytics, Data Visualization, Data Warehousing, Machine Learning, Database Management, and the SDLC. Ability to design and implement Information Systems, data pipelines, and predictive models, and derive insight from data to drive business insights. Excellent communication and problem-solving skills, with experience in teaching and tutoring Two 300-level Business Analytics/Statistics courses. Seeking a challenging role that leverages my skills to drive business growth. Please check out my GitHub Repository link in the header to see my projects.

Education

California State University, Fullerton Dec 2023

Master of Science in Information Systems

California State University Fullerton Jun 2022

Bachelor of Arts in Business Administration, Business Analytics

Skills

Technology: R, Python, Microsoft Excel, Tableau, Power BI, Jira, Visio, Drawl.io, Postgres SQL, SQL server, and Data Bricks.

Expertise: Systems Analysis and Design, Business Analytics, Database Management, Data Modeling, Data retrieval, Data transformation, Data Visualization, Data Cleaning, Machine Learning, Inferential-Descriptive-Predictive Statistics.

Professional Experience

California State University, Fullerton – College of Business and Economics Tutoring Center

Statistic/Business Analytics Tutor Jan 2023 – Dec 2023

Conducted tutoring sessions to help students with statistical concepts such as hypothesis testing, multiple linear regression, optimization, simulation, forecasting, data visualization, and descriptive statistics.

Provided individual and group tutoring to students in ISDS 361 A and ISDS 361B business analytics courses using Excel.

Created personalized study plans, broke down complex ideas into bite-sized chunks, and celebrated the students’ progress

along the way. Made meaningful connections while making a positive impact on my community and sharpening my skills.

California State University, Fullerton – College of Business and Economics, ISDS Department

Graduate Student Assistant Aug 2022- Jun 2023

Supported faculty in two 300-level Business analytics courses which cover advanced statistics and business intelligence.

Helped tutor students in data visualization, linear programming, simulation, multiple regression, and inferential statistics.

Designed practice tests tailored to specific course content to aid student understanding and boost academic success.

Recorded and edited video study material on course content, including multiple regression, forecasting, Monte Carlo Simulation, and linear programming.

Assisted a professor with helping 300+ students with questions about course concepts, through email, phone, text, and Zoom.

Served as an administrator on Canvas by troubleshooting problems such as handling discrepancies, posting assignments, adjusting deadlines, and adding assignments.

Supplemental Student Instructor Dec 2022-Jun 2023

Instructed two weekly classes for 20+ students in advanced statistical concepts such as multiple regression, data visualization, simulation, various time series forecasting methods, inferential statistics, and optimization.

Created effective lesson plans using various teaching methods to increase student comprehension and engagement.

Evaluated student outcomes, identified areas for improvement, and developed strategies to enhance student success.

Participated in department weekly meetings to evaluate sessions, identify areas for improvement, and develop strategies to enhance student success.

Monitored student progress and adjusted teaching methods and lesson plans accordingly to reduce drop and failure rates.

Coordinated with the college department head to develop a comprehensive curriculum for the ISDS course.

Project Rebound

Peer Navigator/Mentor Sep 2021- Dec 2023

Documented each interaction with Clients and maintained up-to-date client records while maintaining 100% confidentiality.

Conducted weekly needs assessments and connected clients with programs and resources to help attain educational goals.

Managed caseload and workload of seven clients independently to explain program offerings and requirements.

Collaborated in weekly meetings with a team of 7 to discuss client and program progress to identify areas of need.

Visited various juvenile prisons to advise and mentor youth on education and how to improve overall quality of life.

Facilitated group sessions of 5-12 people to educate others on the importance of obtaining an education.

Responded to questions regarding the program and opportunities to help adolescents transition back into society.

Campus Involvement and Leadership Experience

President and founder of Data Science and Machine Learning Club, CSUF Nov 2022- Dec 2023

Created and started a new club from one member to 120 members within a 4-month period including creating by-laws, and mission statements, hand-selected board members, and registered the club to be recognized an official university organization.

Led recruitment for new members and initiated funding for events through individualized marketing strategies.

Planned/organized 2 club meetings a month and put on 10 workshops covering topics of machine learning, Python, and AI.

Project Rebound, Member, CSUF Jan 2020- Dec 2023

Engineering and Computer Science Inter-Club Council, Board member, CSUF Nov 2022- Dec 2023

Relevant Projects

System Analysis and Design

Designed a Rideshare app for a moving company comparable to Uber for small and medium-sized moving jobs.

Created a system request and conducted technical and financial feasibility analysis through Microsoft Word.

Identified functional, and nonfunctional requirements for ride-sharing apps to develop functionality and use cases.

Developed use cases, context diagrams, and level 0, level 1, and level 2 data flow diagrams.

Created and developed entity relationship diagrams for database development.

IT Project Management

Performed the planning analysis and design for a system to help facilitate the transaction and usage of a product that transformed city light poles into an electric vehicle charging station; Also developed a plan to implement the system and created a deliverable.

Used project management ten knowledge areas to guide projects such as scope objective, project charter, SDLC, resource management, cost objective and budget, and time management: using Gantt charts, project scheduling tools, performed analysis, and justification.

Created project proposal with scope objective, cost estimate, critical assumption and constraints, project requirements and justification, and potential risks.

Created 10 features with 5 priority features, including the project's Work Breakdown Structure (WBS), and WBS dictionary.

Developed various data models and ERD diagrams to facilitate creating deliverables: an app to facilitate EV charging transactions.

Data Transformation

Designed and implemented ETL and ELT pipelines using PgAdmin, PostgreSQL, and R studio.

Project 1: extracted financial data from various sources. Loaded it into a temporary PostgreSQL database. Then I created a custom trading day calendar in Excel and loaded it into a table to help ensure that the trading data was complete. Then Formatted and transformed the data and stored it in a materialized view for easy exportation to R studio. From there built a Markowitz nonconvex optimization model to help identify the 10 best stocks by risk and reward

Project 2: Built a script that could scrap electricity load data from the NYISO website using R every few days and loaded the data into a Postgres database. Then cleaned and formatted the data so it could be used to create a forecasting model in R using TensorFlow API.

Independent Studies Forecasting Project

Independently learned proficiency in implementing and interpreting various forecasting models including Exponential Smoothing (ETS, Brown, Holt, Winters), Decomposition (STL, Classical, X-11, X12, X11 seats), ARIMA (Seasonal and Non-Seasonal) Time Series Regression Models (Regression with seasonality, dynamic regression w/ARIMA, Dynamic Harmonic Regression), Hierarchical forecasting (top-down and bottom-up), and Profit model.

Project: Build and evaluate various forecasting models on a 10-million-record dataset for a department store in Ecuador.

Challenges: How to build 1800 models to capture department variation and regional variation for 54 store locations. Had to learn how to account for complex daily seasonality using harmonics and Fourier terms to account for all different types of seasonality associated with fine-grain time series data. How to develop a forecasting model that would work with hierarchical data and could also account for various seasonality associated with daily sales data. How to further improve the model by incorporating regional holiday data to help capture holiday seasonality. How to forecast at various levels such as state, national, and location

Solution: Implemented various forecasting models and evaluated them based on various forecast errors.

Outcome: Created a Phrophet forecasting model that used piecewise regression, forriers, and decomposition which had a 4% MAPE.

Machine Learning Capstone project: Credit Card Approval.

Problem: Leveraged machine learning in R Studio to predict creditworthiness in a 1 million record dataset.

Challenges: Highly imbalanced data (99.5% creditworthy customers) hindered model performance, and data did not consist of a dependent variable so I had to perform feature engineering to create a dependent variable to build various predictive models.

Solution: Implemented KNN, Logistic Regression, and Classification trees with ensembles (Random Forest, XG-Boost) for creditworthiness prediction. Employed data cleaning, feature engineering, and various sampling techniques (under, over, synthetic) to address data imbalance. Engineered a dependent variable through feature engineering.

Outcome: Created a model with accuracy (95%) but models couldn't outperform the NIR baseline accuracy (99.5%), so I created a new model to predict "late fee w/out defaulting" that would aid in developing a new product for customers with lower credit scores.

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