Computer Engineer | Data Scientist

Hi, I'm Ricardo

About Me

As a data science and machine learning engineer with a computer engineering background, I have developed skills in data analysis and interpretation. I can apply various techniques, methods, algorithms, systems, and tools to extract insights from structured and unstructured data. I have experience in working with different types of data sources and formats, such as text, images, audio, video, and sensor data. I can also design and implement machine learning models and pipelines to solve complex problems and generate value from data.

  • Data Science
  • Python Programming Language
  • Statistic and Probability concepts
  • Data wrangling and cleaning skills
  • Data visualization tools and techniques
  • Machine learning and deep learning methods
  • Cloud computing platforms and services
  • Front-End Development
  • HTML, CSS and JavaScript
  • Responsive web design
  • Bootstrap and JQuery
  • React, Angular
  • Git and GitHub
  • Computer engineering
  • Coding in Python, Java, C++
  • Debugging and testing software
  • Computer Architecture and organization
  • Operating systems and networks
  • Software engineering and design
  • Mar 2022 - Jan 2023
    Data Science Fellow at Springboard
  • Aug 2019 - Aug 2020
    Machine Learning Intern at The Walt Disney Company
  • 2023
    Data Science Career Track, Springboard
  • 2022
    Deep Learning Nanodegree Program, Udacity
  • 2022
    TensorFlow Developer Professional Certificate, Coursera
  • 2021
    Data Science Career Path, CodeCademy
  • 2020
    Bachelor of Science in Computer Engineering, California State University, Fullerton

My Projects

Toxic Comments Classification

Implemented model that’s capable of detecting different types of toxicity in social media comments like twitter using Natural Language Processing. Used pandas, NumPy, and seaborn for data processing and visualization, and Scikit-Learn library to train the machine learning model. Classified online toxic comments into five different categories with 90% accuracy on a test dataset.

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Diabetic Retinopathy Detection Using Deep Learning

Built a Deep Learning model from thousands of images collected in rural areas using the Kaggle API to access the dataset. Used torchvision and OpenCV for image transformation and training of the model that will recognize the severity of diabetic retinopathy on images of the human eye. Predicted diabetic retinopathy with an accuracy of 70% given a clear eye image that helps to speed up disease detection.

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Prediction of Absenteeism at Work

Created a model that predict absenteeism at work using Python, SQL, machine learning methods, and Tableau for visualizations. Built a Logistic Regression model with a test accuracy greater than 80% by using cross-validation and evaluating different estimators’ performance. Developed model that predicts whether an employee can be expected to be missing for a specific number of hours in each workday.

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Contact Me

rcepedaraza@gmail.com