Programme Content
Module 1a: Foundations of Business Analytics & Data Science
Business problem framing, KPIs, Excel modelling, basic statistics, MySQL, distributions, hypothesis testing, A/B testing.
Module 1b: Data Visualization & Case Studies
Visualization Best Practices & Perception, Choosing the Right Chart, Introduction to Tableau and Power BI, Building Interactive Dashboards, Data Storytelling Frameworks, Advanced Visualization Techniques.
Module 2: Data Warehouse for AI
Data is the foundation for Artificial Intelligence models. Generative AI models rely on large amounts of high-quality data to learn patterns, relationships, and structures. Understanding various data stores helps students appreciate how data is stored, managed, and retrieved. This data is usually stored in a variety of data stores viz. relational databases, NoSQL databases, data lakes. Knowledge of these data stores will enable students process various data formats
Module 3: Python Crash Course for GenAI
Learn basic Python required for GenAI- Data Types, Flow Structures, Data Structures, Functions, Libraries, Python in Data Science.
Module 4: Machine Learning Basics
Supervised and unsupervised learning, time series analysis, churn prediction, forecasting. Examples include sentiment analysis for customer reviews.
Module 5: Neural Networks & Deep Learning
Understand the transition from traditional machine learning to neural networks and deep learning and their common applications.
Module 6: Introduction to LLM & Prompt Engineering
You can use LLM for content creation, computer programming, problem solving, dataset generation, and learning. In this model, you will learning how to effectively leverage LLM to improve your productivity. You will be learning basic prompting strategies such as setting the context, role prompting, and tuning model settings. You will also learn advanced prompting methods such as chain-of-thought (COT) prompting.
Module 7: Building your own AI agents
Gain a comprehensive understanding of AI agents from simple rule-based systems to modern autonomous agents and learn how they perceive, reason, learn, and act to solve real-world problems.
