52 Week - Certified Applied AI and Data Science Program

This course contains the use of artificial intelligence.

52-Week Certified Applied AI and Data Science Program is a comprehensive, year-long learning journey designed to take students from complete beginner to confident applied AI practitioner. Through daily lessons, hands-on labs, weekly reviews, practical projects, and a portfolio-ready capstone, you will develop the technical and business skills required to build modern data and artificial intelligence solutions.

The program begins with Python programming, development environments, Jupyter notebooks, Git, reproducibility, and collaborative workflows. You will then learn NumPy, pandas, data cleaning, feature creation, visualization, descriptive statistics, probability, statistical inference, hypothesis testing, and A/B testing.

You will develop practical data engineering and analytics skills by collecting information from datasets and APIs, writing SQL queries, understanding relational databases, designing data warehouses, and creating ingestion, transformation, validation, storage, and monitoring pipelines.

The machine learning portion introduces supervised learning, regression, classification, cross-validation, feature engineering, model selection, and performance evaluation. You will work with logistic regression, decision trees, random forests, gradient boosting, k-nearest neighbors, and Naive Bayes. You will also explore unsupervised learning, clustering, dimensionality reduction, recommender systems, anomaly detection, and model interpretation.

Specialized modules cover time-series forecasting, neural networks, deep learning, computer vision, natural language processing, embeddings, attention, BERT, GPT, and transformer workflows. These topics provide a strong foundation for understanding modern AI applications.

The program then moves into generative AI, prompt engineering, retrieval-augmented generation, vector databases, hallucination reduction, and agentic AI. You will learn how agents use tools, call functions, plan tasks, maintain memory, and complete multi-step workflows.

Production readiness is addressed through MLOps, experiment tracking, model packaging, versioning, deployment, APIs, batch and real-time inference, monitoring, alerting, containers, workflow orchestration, scalable processing, GPUs, and cloud cost awareness.

You will also study AI security, data privacy, prompt injection, fairness, explainability, responsible AI, governance frameworks, risk registers, controls, auditability, and compliance. Enterprise strategy modules connect technical implementation with use-case prioritization, stakeholder alignment, operating models, solution architecture, and business value.

During the final eight weeks, you will design, build, test, document, deploy, and present a complete capstone solution. You will also create a GitHub portfolio, case study, resume narrative, LinkedIn story, and career roadmap.

By graduation, you will have a broad and practical foundation in data science, machine learning, deep learning, generative AI, AI agents, MLOps, governance, and enterprise AI delivery.