Position: Data Scientist / Data Analyst / Machine learning engineer
Category: Data Science
English level: intermediate
Years of experience: 0.5

- "Deep Learning" (DeepLearning.AI) Coursera specialization certificate, 2021
- "Apache Spark (TM) SQL for Data Analysts" (Databricks) Coursera course certificate, 2023
- "Statistics for Data Science with Python" (IBM Skills Network) Coursera course certificate, 2023
- "Inferential Statistics" (Duke University) Coursera course certificate, 2023
- "AWS Cloud Technical Essentials" (AWS) Coursera course certificate, 2023
- "Unsupervised Learning, Recommenders, Reinforcement Learning" (DeepLearning.AI) Coursera course certificate, 2023
- "IBM Data Science" (IBM) Coursera specialization certificate, 2023
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Other courses and specializations taken:
- Data Science with R (John Hopkins University)
- TensorFlow 2 for Deep Learning (Imperial College London)
- Data Visualization and Communication with Tableau (Duke University)
"House Prices - Advanced Regression Techniques" Kaggle competition
79 explanatory variables describes (almost) every aspect of residential homes in Ames, Iowa. The goal is to predict the final price of each home. Steps:
- EDA: Exploring the target variable, displaying a correlation matrix to find important features.
- Data Processing: Imputing missing values, deleting outliers, encoding categorical features, adding new features, skewed features transfomation.
- Modeling: cross validation, base models (Lasso, ENet, KRR, GBoost, XGBoost, LightGBM), turning, stacking.

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"Deep Q-Learning - Lunar Lander" Coursera project
The goal of the Lunar Lander environment (using OpenAI's Gym Library) is to land the lander safely on the landing pad.
The agent has four discrete actions available: do nothing, fire right engine, fire main engine, fire left engine. The agent's observation space consists of a state vector with 8 variables: its two coordinates, two linear velocities, angle, angular velocity, two booleans, that represent whether each leg is in contact with the ground or not. The total reward of an episode is the sum of the rewards for all the steps within that episode.
The project uses a Target Network (with soft update weights) and Experience Replay techniques to avoid instabilities.

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"Winning Space Race" capstone Coursera project. The goal  is to predict how successful rocket launches like SpaceX can be. Steps:
- Data collection using SpaceX REST API and Web Scraping
- Data Wrangling
- EDA with Data Visualization and SQL
- Interactive visual analytics using Folium and
   Plotly Dash
- Predictive analysis using KNN, Decision Tree, SVM,   
   Logistic Regression.

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"Flower Classification with TPUs" Kaggle competition
Identify the type of flowers (total 104 types) in a dataset of images (training set more than 68 ths images).
- Distribution strategy to train with TPU
- Load the data in .tfrec format
- Augmengation strategy (random erasing, random flip left and right)
- Learing rate shedule (linear increase + exponential decay)
- Base models (EfficientNet, DenseNet, Inception) with callbacks (learning rate, checkpoint)
- Ensemble models, find the best parameters to ensembling
- Evaluate predictions with confusion matrix