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About me

I am Daniele Calixto, a data scientist from Brazil.

Since I was a child, I've always been passionate about maths. That's why I studied engineering and specialized in technology and programming. Data Science was the way I found to deliver more value through maths and technology tools.

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Know my career path in RESUME.

See my projects in my PORTFOLIO.

See about what I'm writing in my BLOG.

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Highlights

Image by Carlos Muza

Data Scientist Experience

1+ year working as a data scientist in a startup whose product is a data platform, providing services to clients from different segments

Moedas Antigas

Finance Experience

Background in Finance with 3+ years of work experience, which brought business knowledge

Código

Technology Experience

3+ years of contact with technology and programming practice, studying and professionally

Computador

Natural Language Processing Experience

Experience in real case projects using NLP, including Sentiment Analysis

Computadores

Specialization

Finishing a 2 year MBA of Data Science and finished in 2022 full time and 1 year programming course

Escrever em notas post-it amarelas

Agile Experience

4+ years working in companies that adopt agile methodologies

My Learning Roadmap

My journey of learning data science began many years ago (even without knowing I would become one) with engineering school, which gave me a consistent mathematical foundation. This was followed by a programming course and an MBA in data science, where I improved my logic and learned the fundamentals. Professional experiences are the icing on the cake, where I put the theories I had learned into practice and had contact with new technologies.

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I drew up the roadmap below based on roadmaps drawn up by experienced data professionals.

Sources: polinacsv, harshit, geekdforgeeks

 

In this roadmap, you can see topics already studied and applied, topics being explored and topics to be studied. Especially in technology, study and updates are eternal, so it will be constantly updated. Feel free to leave suggestions below. 

LEGEND

 

Studied and applied

Studying

To be Studied

Last update: April 2, 2024

Study focus: Machine Learning

  • Linear Algebra

  • Single and Multivariable Calculus

  • Differential Equations

  • Discrete Mathematics

  • Numerical Analysis

  • Time Series Analysis

  • Graph Theory

  • Complexity Theory

  • Information Theory

Mathematics

  • Descriptive Statistics

  • Correlation

  • Random Samples

  • Sampling Distribution

  • Hypothesis Testing

  • Statistical Inference

  • Confidence Intervals

  • ANOVA

  • T-test

  • Chi-square

Probability and Statistics

  • API Requests

  • Web Scraping: Selenium, Beautiful Soup

Data Extraction

  • Python Basics

  • Python libs: NumPy, Pandas, Matplotlib, Seaborn

  • R Basics

  • R tools: dplyr, ggplot2. Tidyr, Shiny

  • Git

  • SQL

  • Testing and Debugging

Programming

  • Data Format and Types

  • Data Quality

  • Outliers

  • Cleaning Data

  • Complex Data Structures

Data Wrangling

  • ETL

  • Data Mesh

  • Data Warehouse

  • Data Pre Processing

  • Data Validation

Data Fundamentals

  • Feature Engineering

  • Sentiment Analysis

  • NLP

  • SE, MSE, RMSE

  • Accuracy, Precision, Recall, F1-Score

  • Confusion Matrix

  • Dimensionality Reduction

  • LabelEncoder e OnehotEncoder

  • SVM

  • XGBoost

  • Ensamble Models

  • Reinforcement Learning

Machine Learning

  • Data apps

  • Python libs: Streamlit

  • BI Tools: Tableau, Power BI

  • Acessibility and Inclusive Design

  • Color Theory

Dataviz

  • Neural Networks

  • Deep Reinforcement Learning

Deep Learning

  • CI/CD

  • Containerization

  • Version Control

  • Scalability

  • Experiment Tracking

  • Model Governance

  • Model Monitoring

  • Model Deployment

ML Ops

DS Roadmap Suggestions

Thanks for sharing!

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