The scikit-learn certification, Professional
The Professional Practitioner Certification targets working data scientists. Regularization, ensembles, feature engineering, nested cross-validation, and the judgement to pick a model and defend it to a stakeholder.
professional practitioner
Seven competencies of a working mid-level data scientist.
The Professional certification ensures certified professionals have both the conceptual understanding and practical skills of a mid-level data scientist.
Advanced ML Knowledge
Proficiency in a broad range of machine learning algorithms and the ability to select appropriate models for specific problems.
Programming Expertise
Strong coding skills in Python, with experience in optimizing code for performance and scalability.
Data Handling and Engineering
Ability to handle large datasets, including data extraction, transformation, and loading processes.
Feature Engineering
Experience in creating and selecting features to improve model performance.
Tuning and Optimization
Proficiency in hyperparameter tuning, model selection, and ensemble methods to improve model performance.
Critical Thinking
Approach complex problems systematically and evaluate multiple solutions, including diagnosing issues in a model pipeline.
Business Expertise
How ML projects align with business goals and how to translate technical results into actionable business insights.
A step beyond Associate. Recognize when a model is regularized correctly, when a CV strategy leaks, and how to communicate that.
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(
penalty="elasticnet",
l1_ratio=0.5,
solver="saga",
)from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.metrics import average_precision_score
clf = HistGradientBoostingClassifier()
clf.fit(X_tr, y_tr)
ap = average_precision_score(
y_te, clf.predict_proba(X_te)[:,1]
)from sklearn.metrics import PrecisionRecallDisplay
PrecisionRecallDisplay.from_estimator(
clf, X_te, y_te,
).plot()from sklearn.pipeline import FeatureUnion
from sklearn.preprocessing import (
PolynomialFeatures, SplineTransformer,
)
union = FeatureUnion([
("poly", PolynomialFeatures(2)),
("spline", SplineTransformer()),
])from sklearn.model_selection import (
GridSearchCV, GroupKFold, cross_val_score,
)
inner = GridSearchCV(pipe, grid, cv=5)
outer = cross_val_score(
inner, X, y, groups=g,
cv=GroupKFold(5),
)Three levels. You are on the second.
Three certifications, each matching a level and a typical data scientist career path.
Associate Practitioner
Junior data scientist
Fundamental ML, preprocessing, evaluation
Professional
Mid-level
Regularization, ensembles, feature engineering, nested CV
Get training with Skolar.
The Professional track on Skolar matches this exam: regularization, ensembles, feature unions, and nested validation, with notebooks and practice questions written by the scikit-learn team.
Logistics, plain.
Everything you need to plan your sitting, in six lines.
Questions we get a lot.
Do I need the Associate before I sit Professional?
Is there a coding portion?
What if I do not pass?
Is the credential verifiable?
Does it expire?
Certify the work you already do, with scikit-learn.
120 minutes. $349 USD. Multiple-choice plus a hands-on lab, a credential issued by the maintainers themselves.