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\Namaste, World{

About Me:

My name is Rubén (/ˈruːbən/)

Contact me at https://linktr.ee/Ruhguevara

I have a strong foundation in data science, with three years of academic training in various topics, including machine learning, data analysis, statistics, data pipelines, ETL, among others. In addition, I have gained practical experience through my professional role as a data science intern at HPE, where I have worked on various projects assuming different data science roles, such as data engineer and data scientist.

Some Technologies/Tools I use or have experience with:

  • Python
  • R
  • Excel
  • LaTeX
  • Markdown
  • Git/GitHub
  • MATLAB
  • SQL
  • Power BI
  • Tableau
  • HTML
  • Java
  • dbt
  • Google Cloud Platform
  • Amazon Web Services

Some of my work in the community:

Ruhguevara GitHub stats

My recent projects:

Credit Card Fraud Detection (Most recent)

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Python ETL pipeline

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Exploratory Data Analysis automation script

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Financial Complaints Dashboard - Tableau

Data Science Applied to YouTube Metrics

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Portfolio Management With Python

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My hobbies are:

  • Videogames, I'm and advocate of videogames as an art form
  • Music (rock, blues, jazz, funk, classic, andina and metal)
  • Playing piano and guitar.
  • Cooking, specially Asian and Mexican food.
  • I also enjoy reading books, articles or anything interesting I find on internet.
  • Spending time with my best friend, my dog pakkun a.k.a "El Pepe" + 10 nicknames more.

🏅 Highlighted badges/certificates

This format was taken with permission from Melissa Silva c:

and without permission from Yanni Martínez xd

Modified by me, feel free to use it.

}

Rub2.7182n's Projects

algorithmic_trading icon algorithmic_trading

Technical Analysis Project for Microstructure and Trading Systems Course, using EMA, SRSI and ATR with Backtesting and Optimization

credit_card_fraud_detection icon credit_card_fraud_detection

The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

eda icon eda

Exploratory Data Analysis automation

etl-pipeline icon etl-pipeline

Python pipeline to efficiently Extract, Transform, and Load data into a Data Warehouse or Data Lake. The project objective is to help a fictional private equity firm process the sales data they need to make informed business decisions when buying real estate.

gcp_mlops icon gcp_mlops

MLOPs Model Deployment with Google Cloud Platform

mgp icon mgp

Mega Guided Projects

proyecto-spf icon proyecto-spf

Financial Processes Simulation Project. In this project, we analyzed a 40 MB dataset with 16 columns and about 40k rows. Containing YouTube statistics like views, comments, likes, dislikes, etc. Some statistic and quantitative methods used are: Monte Carlo Simulation, Variance Reduction, Q-Q Plot Test, EDA, Random Variables Generation, Kolmogorov-Smirnov Test, Kernel Density Estimation, Stratified Sampling, etc.

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