Choose Your Level
Pick the difficulty that matches where you are. You can come back and try a harder level later.
Choose the Right ML Algorithm
Given 5 business problems, pick the right algorithm and explain why.
Build and Evaluate a Predictive Model
Train a model to predict customer churn and explain the confusion matrix.
Detect and Mitigate Model Bias
Audit a hiring algorithm for demographic bias and propose mitigation strategies.
Machine Learning Fundamentals & Predictive Modeling
Predictive analytics allows organizations to forecast future business trends. Data analysts demonstrate machine learning competence by training supervised models (Scikit-Learn), evaluating regression/classification algorithms, and assessing model performance.
1. Predictive Machine Learning Notebook
Jupyter Notebook training linear regression, decision tree, or random forest models on tabular data.
2. Feature Engineering & Selection Log
Document detailing feature encoding, numerical scaling (StandardScaler), and correlation selection.
3. Model Performance Evaluation Report
Evaluation report presenting confusion matrices, Precision, Recall, F1-Score, and ROC-AUC metrics.
Frequently Asked Questions (Machine Learning Fundamentals)
What is the difference between Overfitting and Underfitting?
Overfitting occurs when a model learns training data noise and fails to generalize to new data. Underfitting occurs when a model is too simple to capture underlying data patterns.
When should you prioritize Precision over Recall?
Prioritize Precision when false positives are costly (e.g. spam detection). Prioritize Recall when false negatives are dangerous (e.g. disease screening).
Why is a train-test split necessary?
Splitting data into separate training (e.g. 80%) and testing (e.g. 20%) sets allows you to evaluate how well the model generalizes to unseen data.
Explore Data Analytics Career Paths
Build proof of work across other topics or view full career roadmaps mapping technical skills to hiring expectations.