AI & DATA SCIENCE MASTERY PROGRAM
A single, unified 12-week, 72-hour engineering pathway from quantitative and Python foundations to production-minded AI systems — integrating 12 courses (ADS-101 to ADS-112), 4 progressive phases, and 5 competency gates.
AI & Data Science Mastery Program
By the end of the program, students will be able to frame data and AI problems, build reliable data pipelines and predictive models, develop generative and agentic applications, and communicate, evaluate, and deploy solutions responsibly in real-world settings.
Includes all 12 courses (ADS-101 to ADS-112), Dhaka campus desk with external monitor, fiber Wi-Fi, 48h rubric reviews, and Capstone Studio defense.
12 INTEGRATED COURSES (ADS-101 TO ADS-112)
Every student enrolled in the AI & Data Science Mastery Program completes all 12 component courses across 4 progressive levels.
Mathematics and Statistics for Data Science
Build the quantitative foundation: functions, vectors, matrices, probability, distributions, sampling, estimation, hypothesis testing, correlation, regression intuition, and uncertainty.
Python Programming for Data Science
Learn Python syntax, functions, data structures, modules, debugging, notebooks, NumPy, pandas, visualization, and clean coding through data exercises.
Introduction to Machine Learning and Data Science
Frame problems and move from raw structured/unstructured data to insight using the end-to-end workflow: question, data, analysis, model, interpretation, and communication.
Fundamentals of Artificial Intelligence
Survey AI problem-solving and reasoning, search, planning, constraints, graphical models, logic, machine learning, NLP, vision, and Python-based AI problem solving.
Machine Learning and Business Intelligence
Apply supervised and unsupervised learning, preprocessing, evaluation, predictive analytics, visualization, forecasting, classification, clustering, and recommendations to business decisions.
Data Engineering
Design ingestion, storage, transformation, integration, warehousing, data quality, security, scalability, and performance practices for dependable analytical assets.
Deep Learning
Understand and implement neural networks, optimization, CNNs, RNNs, representation learning, and generative model concepts for vision and language applications.
Natural Language Processing
Process text with tokenization, embeddings, syntax and semantics, named entities, classification, sentiment, language models, and practical NLP evaluation.
Generative AI-Driven Intelligent Apps Development
Build intelligent applications using generative models, retrieval/context, structured outputs, prompting, evaluation, safety, UX, and deployment patterns.
Agentic AI
Design goal-driven agents with reasoning, planning, tool use, memory, orchestration, collaboration, evaluation, and controlled deployment.
Enterprise AI
Plan, govern, integrate, secure, scale, and operationalize AI across business functions, legacy systems, workflows, and organizational change.
AI & Data Science Capstone Studio
Integrate the pathway into a portfolio-grade solution: problem framing, data pipeline, model or AI application, evaluation, governance, deployment plan, and stakeholder presentation.