Equip high school students to master data science and AI-driven decision making with this 23-page resource aligned to Oklahoma OAS L1.ET.AI.01. Includes 1 comprehensive assessment (10 questions with detailed answer explanations) covering data pipelines, exploratory data analysis, feature engineering, statistical testing, model evaluation, decision trees, ensemble methods, and A/B testing—perfect for hands-on data science projects through complete Python workflows analyzing real datasets (Titanic, housing prices, iris classification) from data cleaning to model deployment.

Key Components

✔️ 15 Standards-Aligned Vocabulary Terms on Data Pipeline, Exploratory Data Analysis, Feature Selection, Correlation, Outlier, Data Normalization, Missing Data, Dimensionality Reduction, Cluster Analysis, A/B Testing, Decision Tree, Ensemble Methods, Cross Validation, Precision and Recall, and Interpretability
✔️ 11 Comprehensive Content Sections explaining data pipeline automation, exploratory analysis workflows, feature engineering transformations, statistical foundations (correlation vs. causation, hypothesis testing), preprocessing techniques, model evaluation methods, decision tree construction, ensemble methods (Random Forest, Gradient Boosting), data-driven decision frameworks, and interpretability requirements
✔️ 1 Rigorous Assessment (6 multiple choice + 4 true/false questions) with complete answer key and detailed explanations for each question
✔️ 1 Group Activity (Complete Data Analysis Project, 75-90 minutes with Python/pandas/scikit-learn) performing end-to-end workflow: exploratory analysis, data cleaning, feature engineering, model training (decision trees, Random Forest, logistic regression), cross-validation, and insight presentation
✔️ 1 Individual Activity (Statistical Analysis & Hypothesis Testing, 45-55 minutes) formulating hypotheses, conducting statistical tests (t-tests, chi-square, correlation), interpreting p-values and effect sizes, and distinguishing correlation from causation
✔️ Word Search Puzzle for data science terminology reinforcement

Core Topics

• Data Pipelines → Data Ingestion from Multiple Sources, Cleaning (Missing Values, Duplicates, Outliers), Transformation, Validation & Orchestration
• Exploratory Data Analysis → Summary Statistics, Distribution Visualizations (Histograms, Box Plots), Scatter Plots, Correlation Matrices & Pattern Discovery
• Feature Engineering → Domain-Driven Transformations, Polynomial Features, Binning, Time-Based Features, Aggregation, Text/Geospatial Features & Interaction Terms
• Feature Selection → Filter Methods (Correlation, Mutual Information), Wrapper Methods (Forward/Backward Selection), Embedded Methods (LASSO, Tree Importance) & Multicollinearity Handling
• Statistical Foundations → Hypothesis Testing (Null/Alternative), P-values, Confidence Intervals, Correlation vs. Causation, Confounding Variables & Experimental Design
• Data Preprocessing → Normalization/Standardization, Missing Data Imputation, Outlier Detection/Treatment, Encoding Categorical Variables & Data Validation
• Dimensionality Reduction → Principal Component Analysis (PCA), Curse of Dimensionality, Variance Preservation & Feature Space Compression
• Cluster Analysis → Unsupervised Learning, K-means, Customer Segmentation, Natural Pattern Discovery & Similarity Metrics
• Model Evaluation → Train-Test Splits, Cross-Validation (K-Fold, Stratified), Confusion Matrices, ROC Curves & Baseline Comparisons
• Decision Trees → Recursive Splitting, Information Gain, Gini Impurity, Pruning, Feature Importance & Interpretable Rules
• Ensemble Methods → Bagging, Random Forest, Boosting (Gradient Boosting), Stacking, Diversity Importance & Variance Reduction
• Classification Metrics → Accuracy, Precision, Recall, F1-Score, True/False Positives/Negatives & Class Imbalance Handling
• Regression Metrics → Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared & Residual Analysis
• A/B Testing → Randomized Experiments, Treatment/Control Groups, Statistical Significance Testing & Causal Inference
• Data-Driven Decision Making → Predictive Analytics, Prescriptive Optimization, Dashboard Design & Organizational Implementation

Technical Specs

📄 Pages: 23 | Format: Instant PDF Download

🎯 Oklahoma Standard: L1.ET.AI.01 - "Explore various architectures of artificial intelligence including neural networks, machine learning, and their applications in solving real-world problems"

📚 Series Position: Topic 7 in the complete L1.ET.AI.01 curriculum sequence (bridges foundational AI concepts with advanced techniques by teaching the data science workflow essential for all AI/ML projects)

What Makes This Resource Unique

Complete End-to-End Workflow: Group activity implements the entire data science lifecycle on real datasets (Titanic survival, housing prices, iris classification): loading data, exploratory analysis with visualizations, handling missing values and outliers, engineering predictive features, training multiple models (decision trees, Random Forest, regression), cross-validation tuning, test set evaluation, and presentation of actionable insights—building production-ready skills.

Statistical Rigor with Practical Application: Individual activity teaches hypothesis formulation and testing on real data: students choose research questions, state null/alternative hypotheses, select appropriate tests (t-tests, chi-square, correlation), compute p-values and effect sizes, create supporting visualizations, and critically distinguish correlation from causation—addressing the #1 statistical misconception in data analysis.

Professional Data Science Stack: Activities use industry-standard tools (Python, pandas for data manipulation, matplotlib/seaborn for visualization, scikit-learn for modeling, Jupyter notebooks for interactive analysis) with same workflows data scientists use professionally—preparing students for internships, competitions (Kaggle), and university data science programs.

Feature Engineering Mastery: Deep coverage of the "secret sauce" that often matters more than algorithm choice: domain-driven transformations, polynomial features for nonlinearity, time-based extraction, aggregation from transactions to customer-level, text feature creation, interaction terms—teaching students that thoughtful feature engineering frequently outperforms sophisticated algorithms on raw features.

Ensemble Methods Demystified: Explains why combining models works (wisdom of crowds, variance reduction through diversity), compares bagging (Random Forest) vs. boosting (Gradient Boosting) vs. stacking, and implements ensembles hands-on—covering the techniques dominating Kaggle competitions and production ML systems while maintaining interpretability through feature importance analysis.

Call-to-Action

Build data science expertise while covering OAS L1.ET.AI.01! Includes 5-6 days of no-prep content with complete Python workflows, statistical testing projects, and rigorous assessments.

Series Integration

Foundation for All AI/ML: Topic 7 provides the data science infrastructure underlying all AI applications—students who learned architectures (Topic 1), training (Topic 2), NLP (Topic 3), vision (Topic 4), ethics (Topic 5), and applications (Topic 6) now master the complete workflow: data pipelines feeding training, exploratory analysis informing feature engineering, statistical testing validating hypotheses, and ensemble methods powering production systems.

Bundle Available: Complete High School AI Curriculum: 9-Unit Bundle for OK L1.ET.AI.01 Bundle

Tags

#DataScience #MachineLearning #OklahomaStandards
#HighSchoolCS #L1ETAI01 #FeatureEngineering
#StatisticalAnalysis #EnsembleMethods #PythonDataScience
#STEMCurriculum #DataAnalytics #STEMCareers

About the Author

Matt Cole holds a Master's Degree in Information Technology and has spent over two decades working in healthcare IT, including project management roles. He served a full five-year term on the Pocola Public School Board, where he helped shape district vision, policies, and curriculum decisions. His ongoing professional learning and service in public education drive Sooner Standards' commitment to rigorous, future-focused resources for Oklahoma high school students.