def expertise():
return {
"machine_learning": {
"deep": ["TensorFlow", "PyTorch"],
"traditional": ["scikit-learn", "XGBoost"]
},
"engineering": {
"data": ["Pandas", "NumPy", "Spark"],
"cloud": ["AWS", "GCP", "Azure"]
},
"visualization": ["Plotly", "Seaborn", "D3.js"]
} |
analytics_mastery <- list(
statistical = c("Bayesian", "Time Series"),
interactive = c("Shiny", "htmlwidgets"),
reports = c("RMarkdown", "Quarto"),
packages = c("tidyverse", "data.table")
) |
graph LR
D[Data] --> P{Analysis Path}
P -->|Statistical Modeling| R[R Ecosystem]
P -->|ML/Deep Learning| PY[Python Stack]
R --> V{Visualization}
PY --> V
V -->|Interactive| I[Web Dashboards]
V -->|Static| S[Reports/Notebooks]
I --> DL[Deployment]
S --> DL
classDef python fill:#3776AB,color:white;
classDef r fill:#276DC3,color:white;
classDef deploy fill:#232F3E,color:white;
class PY python;
class R r;
class DL deploy;
# Python ML Pipeline Architect
def build_robust_pipeline(data, target):
# Define preprocessing steps
numeric_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())
])
categorical_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='constant')),
('encoder', OneHotEncoder(handle_unknown='ignore'))
])
# Column transformer for mixed data types
preprocessor = ColumnTransformer(
transformers=[
('num', numeric_transformer, selector_num),
('cat', categorical_transformer, selector_cat)
])
# Create production-ready pipeline
model_pipeline = Pipeline(steps=[
('preprocessor', preprocessor),
('classifier', RandomForestClassifier(
n_estimators=100,
max_depth=None,
min_samples_split=2,
random_state=42
))
])
# Train with cross-validation
return model_pipeline.fit(data, target) |
# Advanced Bayesian Analysis in R
advanced_modeling <- function(data) {
# Bayesian hierarchical model with Stan
model <- brm(
formula = outcome ~ predictor1 + predictor2 +
(1 + predictor1 | group),
data = data,
family = gaussian(),
prior = c(
prior(normal(0, 10), class = "b"),
prior(cauchy(0, 2), class = "sd")
),
chains = 4,
iter = 2000,
warmup = 1000,
cores = 4
)
# Generate posterior predictions
predictions <- posterior_predict(
model,
newdata = prediction_data
)
# Return model and diagnostics
return(list(
model = model,
diagnostics = pp_check(model),
predictions = predictions
))
} |
| ML Ops | MLflow | Kubeflow | Docker | CI/CD |
| Distributed Computing | Dask | Spark | Ray | Kubernetes |
| Deep Learning | PyTorch | TensorFlow | JAX | ONNX |
| Data Engineering | Airflow | DBT | Snowflake | BigQuery |
graph TD
A[User Behavior Data] --> B[Feature Engineering]
B --> C[Collaborative Filtering]
B --> D[Content-Based Filtering]
C --> E[Hybrid Model]
D --> E
E --> F[A/B Testing]
F --> G[Production API]
style A fill:#f9f,stroke:#333,stroke-width:2px
style E fill:#bbf,stroke:#333,stroke-width:2px
style G fill:#bfb,stroke:#333,stroke-width:2px
|
graph TD
A[Historical Data] --> B[Trend Decomposition]
B --> C[Seasonal Analysis]
B --> D[ARIMA Modeling]
B --> E[Prophet]
B --> F[LSTM Networks]
C & D & E & F --> G[Ensemble Methods]
G --> H[Prediction API]
style A fill:#f9f,stroke:#333,stroke-width:2px
style G fill:#bbf,stroke:#333,stroke-width:2px
style H fill:#bfb,stroke:#333,stroke-width:2px
|
def collaborate():
domains = ["MLOps", "Generative AI", "Causal Inference", "Bayesian Modeling"]
interests = ["Open Source", "Mentorship", "Research", "Teaching"]
return "Always open to collaborating on innovative data science projects!"