I'm a Data Engineer/Scientist who identifies business needs and develops valuable solutions to improve accuracy and process optimization. Drives business success with recommendations based on data findings. Previously as a teacher, a professional focused on maximizing student educational potential by applying diverse instructional strategies and classroom management techniques. Skillful in delivering lectures, facilitating discussions and planning to enhance learning. Using this, I bring a wealth of experience in data analysis and project modeling to guide research and demonstrations. Willingness to take on added responsibilities to meet team goals.
I am an enthusiastic analyst with a passion for creating results out of data and presenting the information in a clear, concise fashion.
❏ Programming Languages: Python, SQL Java Script, Java, HTML5, CSS ❏ Database & Management: SQL/NoSQL, PostgreSQL, R Python (Pandas, NumPy, Flask) MongoDB, Athea, Glue, Amazon S3, Pyspark Airflow, Kafka, Azure Databricks and Factory ❏ Big Data Analytics ❏ Data Mining & Warehousing ❏ Statistics ❏ Front-End Web Data Visualization + Geomapping ❏ Machine Learning ❏ GitHub/GitBash ❏ DataCamp/HackerRank Code practice,Competitions, Classes to enhance my background ❏ Leadership ❏ Microsoft SQL Server ❏ PostgreSQL ❏ Algorithms ❏ Hadoop ❏ Google API ❏ Jira
Data Analytics Certificate
24-week intensive program focused on gaining tech programming skills in Excel, VBA, Python, R, JavaScript, SQL Databases, Tableau, Big Data, and Machine Learning
STEM Certification
An interdisciplinary approach to integrating STEM into practice across the disciplines. Involving problem-based and project-based operations, technology,mathematics,science inquiries and backward design. Implemention and monitoring of outcomes outlined in presentations.
Master’s in Education
Endorsements: (Grade 7-12) English, Science, Social Studies, Math
Bachelors Degree
Dietetics and Chemistry
Three technical analysis and a proposal for further statistical study. Includes the following: Linear Regression to Predict MPG Summary Statistics on Suspension Coils T-Test on Suspension Coils Design a Study Comparing the MechaCar to the Competition
Live Demo View CodeA Colorado Board of Elections employee has given our company the following tasks to complete the election audit of a recent local congressional election. Election-Audit Summary provides a business proposal to the election commission on how this script can be used—with some modifications—for any election.
Live Demo View CodeUse BeautifulSoup, Splinter, and Pandas to scrape five different webpages related to Mars, and display the results on a webpage using MongoDB and Flask. Step 1 Scraping • Scraped the NASA Mars News Site and collected the latest News Title and Paragraph Text • JPL Mars Space Images - Featured Image • Use Splinter to navigated the site and found the image URL for the current Featured Mars Image and assigned the URL string to a variable called img_url Mars Facts • Visited the Mars Facts webpage and used Pandas to scrape the table containing facts about the planet including Diameter, Mass, etc. • Use Pandas to convert the data to a HTML table string Mars Hemispheres • Visited the USGS Astrogeology site to obtain high resolution images for each of Mar's hemispheres • Saved both the image URL string for the full resolution hemisphere image, and the Hemisphere title containing the hemisphere name. o Used a Python dictionary to store the data using the keys hemisphere_image and title • Appended the dictionary with the image URL string and the hemisphere title to a list Step 2 - MongoDB and Flask Application
Live Demo View CodeCreated an automated pipeline that takes in new data, performs the appropriate transformations, and loads the data into existing tables. Refactoring the code from this module proved necessary and the creation of one function that takes in the three files, Wikipedia data, Kaggle metadata, and the MovieLens, rating data and performs the ETL process by adding the data to a PostgreSQL database.
Live Demo View CodePython TensorFlow library in order to create a binary classifier that is capable of predicting whether applicants will be successful if funded by the nonprofit foundation called Alphabet Soup. We received a CSV containing more than 34,000 organizations that have received funding from Alphabet Soup. Within this dataset are a number of columns that capture metadata about each organization.This ML model will help ensure that the foundation’s money is being used effectively. With neural networks ML algorithms we are creating a robust deep learning neural network capable of interpreting large complex datasets. Important steps in neural networks ML algorithms are a) data cleaning and data preprocessing as well as decision b) what data is beneficial for the model accuracy.
Live Demo View Code