AI & Data Science Mastery Program

Program onboarding, Gate 0 diagnostic, functions, vectors, matrices, descriptive statistics, and data types.

Step-by-step setup of Python environment, Git workflow, functions, data structures, and NumPy array operations.

Write modular Python code to compute vector/matrix operations and statistical summaries on a structured dataset.

Verify Gate 0 completion (baseline quiz, tool setup, domain problem selection) and review notebook standards.

Goal-driven agents, ReAct planning, deterministic tool schemas, short/long-term memory, multi-agent handoffs, and human-in-the-loop safety.

Coding stateful agent workflows with API/SQL tool execution, retry guardrails, loop caps, and step-level observability.

Build and evaluate a bounded agentic system that decomposes complex tasks, invokes tools safely, and logs audit traces.

Verify Gate 3 exit evidence: working GenAI/agentic prototype with quantitative quality, reliability, and risk evaluation.

Integrating AI with legacy systems, privacy/compliance controls, model registries, drift monitoring, SLA/cost modeling, and adoption roadmaps.

Containerizing AI services, instrumenting latency/token/drift metrics, and authoring an enterprise AI risk & governance register.

Produce the enterprise deployment blueprint, risk/compliance matrix, and ROI justification for your Capstone system.

Final technical audit of capstone repositories, reproducibility checklists, and executive presentation dry-run.

Structuring technical-to-executive narratives, defending architectural tradeoffs, and reviewing the 5-dimension Mastery Rubric.

End-to-end smoke testing of data pipeline, model/agent service, evaluation benchmarks, and README documentation.

Complete all 6 Capstone pillars: Problem & Value Brief, Reproducible Data Pipeline, Model/AI System, Evaluation & Risk Report, Enterprise Deployment Plan, and Live Demo.

Formal capstone defense before faculty and evaluators, 5-dimension rubric scoring, and graduation certification.

Probability rules, normal/binomial distributions, sampling variability, confidence intervals, p-values, and regression intuition.

Dataframe indexing, relational merges, missing-value imputation strategies, outlier detection, and Matplotlib/Seaborn charts.

Transform a noisy real-world dataset into a documented, cleaned table with statistical hypothesis tests and visual charts.

Critique statistical claims, check for spurious correlations, and refine chart annotations.

End-to-end workflow: question, data, analysis, baseline model, interpretation, train/test split, and data leakage traps.

Feature preparation, train/validation splits, fitting baseline linear/logistic models, and interpreting core metrics.

Build a complete notebook from problem statement and EDA to baseline model evaluation and stakeholder summary.

Evaluate Python fluency, statistical reasoning, data cleaning, and problem framing for Gate 1 exit clearance.

Survey AI search, planning, constraint satisfaction, logic, decision trees, ensembles, and classification/regression tradeoffs.

Scikit-learn ColumnTransformer pipelines, hyperparameter tuning, precision-recall curves, and confusion matrix diagnostics.

Benchmark multiple supervised algorithms on a business dataset and document failure modes across data slices.

Live cohort presentation of supervised modeling benchmarks and error analysis findings.

Unsupervised learning (K-Means, hierarchical, PCA), time-series forecasting fundamentals, recommendation logic, and KPI trees.

Implementing behavioural clustering, silhouette evaluation, demand forecasting, and interactive KPI dashboard views.

Translate unsupervised segments and predictive forecasts into an executive dashboard and actionable business memo.

Peer critique of BI dashboards and alignment between statistical outputs and operational decisions.

Data ingestion, storage, star/snowflake schemas, warehousing, data quality assertions, security, and scalability practices.

Building reproducible SQL + Python ETL pipelines, automated schema/null checks, and lineage documentation.

Deliver an end-to-end data pipeline feeding an analytical model with automated data quality gates.

Verify Gate 2 exit evidence: evaluated baseline model, documented tradeoffs, and reproducible data pipeline.

Perceptrons, activation functions, gradient descent optimization, dropout/batchnorm, CNNs, RNNs, and representation learning.

Writing custom PyTorch Datasets, training loops, learning rate schedules, and tracking loss/accuracy curves.

Train a deep neural model on vision or tabular/sequence data and run controlled regularization/architecture ablations.

Review training curves, convergence bottlenecks, and when deep learning outperforms classical ML.

Text preprocessing, subword tokenization, vector semantics, NER, sentiment, classification, and transformer attention.

Implementing modern NLP pipelines with pretrained embeddings, fine-tuning classifiers, and slicing errors on edge cases.

Deliver an evaluated NLP pipeline on noisy domain text with quantitative metrics and failure-mode taxonomy.

Live cohort demonstration of Deep Learning & NLP prototypes and capstone proposal lock-in.

System prompting, chunking and hybrid retrieval, JSON schema enforcement, hallucination mitigation, latency, and cost tradeoffs.

Building a retrieval-augmented generation service with vector search, citation verification, and golden test sets.

Ship a working GenAI application with prompt templates, retrieval grounding, guardrails, and faithfulness scores.

Stress-test peer GenAI apps against adversarial inputs, context overflow, and ungrounded claims.