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Hello, I'm Diogo Ribeiro

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Continuous integration

I'm a veteran Data Scientist with over two decades of experience, hailing from the picturesque country of Portugal. My journey through computer science, economy, management, medicine, natural sciences, engineering, pure mathematics and applied mathematics has been a continuous source of fascination and inspiration. Welcome to my GitHub profile!

I've delved into the intricate domains of supply chain, logistics, sustainability, finance, and health, where data-driven decision-making optimizes operations and promotes environmental responsibility.

🔭 In research, I'm deeply immersed in applying machine learning and statistics to health. My work harnesses data to advance healthcare solutions and outcomes.

🔭 My research also extends to mathematics, focusing on differential equations and partial differential equations. These tools apply to diverse fields such as epidemiology, economics, and sociology, helping unravel complex phenomena.

🔭 I'm passionate about graph theory and its applications in social networks, where I uncover patterns and connections that provide valuable insights into human interactions.

📈 Beyond these areas, I'm intrigued by the potential of big data analytics in marketing, where customer behaviors and preferences can be decoded to enhance business strategies.

📊 I'm also fascinated by the emerging field of quantum computing and its potential to revolutionize problem-solving in cryptography and optimization.

📐 My research in statistics and probability involves developing models that accurately predict outcomes and assess risks. These models are crucial in fields like finance, insurance, and public policy, where they inform decision-making and strategy.

🌍 Sustainability is another key interest, particularly in developing algorithms that promote renewable energy use and reduce carbon footprints.

Thank you for visiting my profile, and I look forward to connecting and collaborating!

🔗  Connect with me

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Tools and skills 🎓

Area Tool
OS Linux macOS
Languages Python Node.js TypeScript R MATLAB C C++ Ruby Fortran Apache Spark
Databases PostgreSQL SQLite MongoDB DynamoDB MySQL Microsoft SQL Server Neo4j GraphQL BigQuery
Datalake Apache Iceberg Apache Hudi
Infrastructure Docker GitHub Actions AWS Datadog Prometheus Jenkins
Command Line Bash Git curl wget
Cloud Services Azure GCP AWS
Typesetting Tools LaTeX Markdown R Markdown
Web Development Frameworks React Django
Streaming Apache Flink Apache Kafka Amazon Kinesis Apache Kafka
DevOps Tools Jenkins AWS CloudFormation
Data Analysis and Visualization Tableau Power BI
Data Science Jupyter RStudio Anaconda Kaggle Databricks SageMaker DataRobot H2O.ai RapidMiner Alteryx KNIME Apache Spark TensorFlow PyTorch Apache Flink Apache Kafka Snowflake BigQuery Airflow Matplotlib Plotly D3.js Tableau Power BI
Machine Learning TensorFlow PyTorch Scikit-learn Keras XGBoost LightGBM H2O.ai DataRobot RapidMiner Alteryx KNIME SageMaker Google Cloud AI Azure Machine Learning
Data Engineering Apache Spark Apache Flink Apache Kafka AWS Glue Google Cloud Dataflow Azure Data Factory
Mathematics MATLAB Wolfram Mathematica SymPy R SageMath Julia GNU Octave Scilab
Statistics R MATLAB Excel SciPy NumPy Pandas Stan
Optimization AMPL Gurobi CPLEX JuliaOpt SciPy NumPy Pandas Stan
Models Linear Regression Logistic Regression ANOVA Time Series Analysis Survival Analysis Decision Trees Random Forests Gradient Boosting SVM KNN K-Means Clustering PCA Neural Networks CNN RNN Bayesian Networks Reinforcement Learning
Statistics Linear Regression Logistic Regression ANOVA Time Series Analysis Survival Analysis PCA Cluster Analysis Bayesian Analysis Hypothesis Testing Correlation Analysis Factor Analysis Multivariate Analysis Non-parametric Tests
Mathematics Linear Algebra Calculus Differential Equations Probability Theory Statistics Discrete Mathematics Number Theory Abstract Algebra Topology Combinatorics Graph Theory Mathematical Modeling Game Theory
Data Science Data Cleaning Data Wrangling Data Exploration Feature Engineering Model Selection Model Evaluation Model Deployment Model Monitoring Data Visualization Data Storytelling Data Governance Data Privacy Data Security
Data Engineering Data Ingestion Data Processing Data Storage Data Transformation Data Integration ETL Data Pipelines Data Quality Data Orchestration

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📕  Latest Blog Posts in Medium.com

Building a Data Team in an Evolving World - Wed, 05 Jun 2024

Unsupervised Anomaly Detection - Mon, 22 Apr 2024

A Comprehensive Guide to Structural Equation Modeling with Latent Variables - Tue, 26 Mar 2024

Comparing Imputation Techniques - Fri, 08 Mar 2024

Partial Least Squares: A Comprehensive Guide to Overcoming Data Challenges - Thu, 29 Feb 2024

Depths of Logistic Regression - Wed, 28 Feb 2024

Modeling the Supply Chain of Second-Hand Cars - Tue, 27 Feb 2024

DBSCAN for Clustering Analysis - Tue, 27 Feb 2024

Fair Value Portfolio Hedging for a Bank - Mon, 26 Feb 2024

Python Type Checking - Sun, 25 Feb 2024

📕  Latest Blog Posts in https://diogoribeiro7.github.io/

Read more

This page you are reading is a profile readme. Around July 2020, GitHub made this a public feature.

To make one, create a repo named after your username (matching case exactly) and create a README.md file in it. Then go to your GitHub profile and you'll see your README appear there ✨.

Diogo Ribeiro's Projects

a-beginner-guide-to-carry-out-extreme-value-analysis-with-codes-in-python icon a-beginner-guide-to-carry-out-extreme-value-analysis-with-codes-in-python

A beginner's guide to carry out extreme value analysis, which consists of basic steps, multiple distribution fitting, confidential intervals, IDF/DDF, and a simple application of IDF information for roof drainage design. The guide mainly focuses on extreme rainfall analysis. However, the basic steps are also suitable for other climatic or hydrologic variables such as temperature, wind speed or runoff.

actuary icon actuary

Python code examples to support the Python for Actuaries webinars sponsored by ACTEX Learning

ars icon ars

Python implementation of Adaptive Rejection Sampling

awesome-datascience icon awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

brexit-analysis icon brexit-analysis

Data analysis exploring spatially varying explanations behind the UK's vote to leave the EU.

c icon c

All Algorithms implemented in C

causalsandwich icon causalsandwich

Simple implementation for estimating causal effects with M-estimation and sandwich variance estimators

codility icon codility

Solutions to exercises and tests at http://codility.com/

computational-physics icon computational-physics

My standing problems among the exercises in "Computational Physics" by (Newman, 2013). Please help!

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