Pre-Program. Pre-Program Foundations. Develop foundational skills in computational thinking, Python programming, GitHub version control, AI fundamentals, Excel for business analytics, Linux, quantitative methods, and SQL. Build the technical base needed to confidently engage with program coursework, regardless of prior experience.

TERMS. Term 1. Quantitative Modeling for Data Analytics. Learn to apply statistical analysis in Python, including descriptive statistics, causal inference, sampling methods, hypothesis testing, linear regression, and probability theory. Build data visualizations in R and analyze sustainability-focused case studies to support data-driven decision-making.. Python for Data Analytics. Develop practical Python skills for business analytics by writing reliable code, manipulating data with Pandas, and creating visualizations. Apply introductory machine learning workflows using scikit-learn and experiment with LLMs and coding assistants. Collaborate in a team project to transform real-world data into actionable insights.. Term 2. Data Visualization & Storytelling. Learn to design and deliver impactful data stories using Tableau. Apply Business Intelligence techniques across the full lifecycle, from data modeling to interactive dashboard creation, and communicate insights that drive strategic decisions.. AI and Machine Learning. Build and apply AI and machine learning models for business analytics. Progress from linear models and classification to ensemble methods, unsupervised learning, and neural networks. Apply language models to real-world business challenges and compete in hands-on, Kaggle-style projects.. SQL & Data Architectures. Learn to design, query, and manage relational and non-relational databases. Apply data modeling techniques and evaluate modern data architectures to support analytics and business decision-making.. Business Strategy in the AI World. Develop strategic thinking in the context of AI-driven disruption. Analyze industries and business models, apply innovation frameworks, and evaluate digital transformation strategies. Engage in discussions and case studies to assess the ethical implications of AI in business.. Bootcamp. Power BI Bootcamp (PL-300). Learn to build end-to-end business intelligence solutions using Power BI. Prepare and transform data, design data models, create dashboards, and develop DAX measures. Build professional reports and prepare for the PL-300 certification.. Term 3. Agentic & Generative AI. Learn to design and deploy agentic and generative AI solutions for business. Apply prompt engineering, retrieval-augmented generation (RAG), and multi-agent systems. Build and evaluate AI-driven prototypes while addressing governance and ethical considerations.. Reinforcement & Deep Learning. Develop advanced deep learning and reinforcement learning models. Apply techniques such as transformers, fine-tuning, GANs, and diffusion models, and implement RL methods like Q-learning and PPO. Evaluate and deploy AI systems in real-world business contexts.. Data Intensive Architecture. Learn to design scalable, data-intensive systems. Apply architectural patterns and frameworks to process and analyze large-scale data with high reliability and performance in real-world business environments.. Capstone Challenge BAAI. Apply the full range of technical, analytical, and strategic skills to solve real business problems. Work in teams as a consultancy to deliver high-impact solutions and present recommendations to industry experts and faculty.. Bootcamp. Google Cloud Bootcamp. Prepares students for the Google Cloud Computing Foundations Certificate through four courses with hands-on labs. Covers cloud fundamentals, infrastructure management, networking and security, and GCP data/ML/AI services. No prior cloud experience required.. ELECTIVES . Advanced Fintech Applications. Apply advanced analytics and Python skills to financial technologies. Analyze markets, build algorithmic trading models, and explore blockchain, digital payments, and AI applications in finance.. Analytics, Problem Solving & Decision Making. Develop structured problem-solving skills and apply data analytics techniques such as forecasting, risk analysis, and machine learning. Use Lean Startup principles to make data-driven strategic decisions.. Marketing Fundamentals: Digital & Analytics. Learn to design and execute marketing strategies across digital and traditional channels. Analyze consumer behavior, measure campaign performance, and apply analytics to optimize engagement and growth.