Business problem framing, KPIs, Excel modelling, basic statistics, MySQL, distributions, hypothesis testing, A/B testing.

Visualization Best Practices & Perception, Choosing the Right Chart, Introduction to Tableau and Power BI, Building Interactive Dashboards, Data Storytelling Frameworks, Advanced Visualization Techniques.

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

Learn basic Python required for GenAI- Data Types, Flow Structures, Data Structures, Functions, Libraries, Python in Data Science.

Supervised and unsupervised learning, time series analysis, churn prediction, forecasting. Examples include sentiment analysis for customer reviews.

Understand the transition from traditional machine learning to neural networks and deep learning and their common applications.

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.

Supervised and unsupervised learning, time series analysis, churn prediction, forecasting.