Become a Certified
Gen AI Data Analyst

This entry-level program provides a foundational overview of Artificial Intelligence (AI), covering its main types, role in data analytics, and the basics of deep learning. It also highlights the differences between machine learning and deep learning, and explores key applications of AI across various industries.

This program introduces essential statistical concepts for data analysis, including descriptive statistics, probability, and data distributions. It covers the basics of inferential statistics, highlights the difference between correlation and causation, and explores common sampling methods. The program also emphasizes fundamental data cleaning techniques to ensure accurate and reliable analysis.

In this module, we provide a foundational understanding of machine learning, covering key approaches such as supervised, unsupervised, and reinforcement learning. It introduces common machine learning algorithms and explains the bias-variance tradeoff, a core concept for building accurate and reliable models.

This program covers the essential steps of preparing data for machine learning. It begins with data acquisition and explores common data formats and structures. Participants will learn key preprocessing techniques, methods for handling missing data, and the fundamentals of feature engineering. The program also introduces data splitting strategies to effectively train and evaluate machine learning models.

This module introduces the fundamentals of Generative AI, exploring various types of generative models and their real-world applications. It covers the basics of training these models and discusses common challenges faced in the development and deployment of Generative AI systems.

In this module we focus on Exploratory Data Analysis (EDA) and effective data visualization. It introduces core visualization techniques and popular Python libraries used for creating insightful visuals. The program also covers advanced visualization methods, guiding participants in generating meaningful insights and communicating them through compelling data storytelling.

Here we provide a practical introduction to the machine learning model training process. It covers data splitting strategies, loss functions, and optimization techniques essential for effective model training. Participants will also learn about key evaluation metrics, cross-validation methods, and how to address common challenges such as overfitting and underfitting to build robust models.

This module explores techniques to improve machine learning model performance. It covers hyperparameter tuning, regularization, and feature selection methods to enhance model accuracy and generalization. Participants will also learn ensemble learning techniques and how to effectively compare and select the best-performing models.