<h1 align="center">
<img src="https://img.icons8.com/color/96/python--v1.png" width="70"/>
<img src="https://img.icons8.com/fluency/96/r-project.png" width="70"/>
<br/> Joaquim Timóteo | Software Engineer & Data Science Architect
</h1>
```python
class DataScienceStack:
def __init__(self):
self.ml_libraries = ['TensorFlow', 'PyTorch', 'Scikit-learn']
self.visualization = ['Matplotlib', 'Seaborn', 'Plotly']
self.data_tools = ['Pandas', 'NumPy', 'Spark']
def deploy_model(self):
return "ML Pipelines | API Integration | Cloud Deployment"# R STATISTICAL EXPERTISE
data_science_stack <- list(
statistical_models = c("GLM", "Time Series", "Bayesian"),
visualization = c("ggplot2", "Shiny", "Lattice"),
data_manipulation = c("dplyr", "tidyr", "data.table")
)
deploy_analysis <- function() {
return("Advanced Statistical Modeling | Report Generation | RMarkdown")
}graph LR
A[Data Collection] --> B(Pandas Preprocessing)
B --> C{Model Training}
C -->|Python| D[Scikit-learn]
C -->|Deep Learning| E[TensorFlow]
D --> F[Flask API]
E --> F
graph LR
A[Data Cleaning] --> B(dplyr Transformation)
B --> C{Analysis Type}
C -->|Statistical| D[GLM Models]
C -->|Reporting| E[RMarkdown]
D --> F[Shiny Dashboard]
E --> F
| Task | Python Solution | R Solution |
|---|---|---|
| Data Manipulation | pd.DataFrame.pivot() |
dplyr::pivot_wider() |
| Visualization | sns.heatmap() |
ggplot2::geom_tile() |
| Modeling | sklearn.ensemble |
caret::train() |
| Deployment | Flask REST API |
Shiny Web App |
from sklearn.ensemble import RandomForestClassifier
def train_model(X, y):
model = RandomForestClassifier(n_estimators=100)
model.fit(X, y)
return model
# Feature Engineering Pipeline
X_processed = Pipeline([
('imputer', SimpleImputer()),
('scaler', StandardScaler())
]).fit_transform(X)library(caret)
train_model <- function(data) {
control <- trainControl(method = "cv", number = 5)
model <- train(
Class ~ .,
data = data,
method = "glmnet",
trControl = control
)
return(model)
}
# Advanced Visualization
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
geom_smooth(method = "lm")Python:
# PyPI Package Template
$ poetry new ds_utils
$ python -m buildR:
# CRAN-ready Package
$ devtools::create("rDStools")
$ devtools::check()# Python Learning Path
resources = {
'books': ['Python for Data Analysis', 'Fluent Python'],
'courses': ['Advanced ML with Python', 'PySpark Essentials']
}# R Learning Path
resources <- list(
books = c("R for Data Science", "Advanced R"),
courses = c("Shiny Masterclass", "R Production Systems")
)# Python Style
def connect():
platforms = {
'GitHub': 'https://github.com/joaquimtimoteo/',
'LinkedIn': 'www.linkedin.com/in/joaquim-timóteo-619957227'
}
return platforms# R Style
connect <- function() {
list(
Kaggle = "https://kaggle.com/joaquimtimoteo",
RPubs = "https://rpubs.com/joaquimtimoteo"
)
}📌 Combined Skills Matrix
| Skill Category | Python Implementation | R Implementation |
|---|---|---|
| Data Wrangling | Pandas/Numpy | dplyr/tidyr |
| Visualization | Matplotlib/Plotly | ggplot2/Plotly |
| Modeling | Scikit-learn/TensorFlow | caret/Tidymodels |
| Reporting | Jupyter Notebooks | RMarkdown/Quarto |
| Deployment | Flask/Django | Shiny/plumber |