In this module, learners will learn to manage data using probability distribution functions. Learners will start by applying the Bernoulli distribution to model categorical data, explore the Poisson distribution for forecasting, and utilize the Exponential and Normal distributions for regression modeling.

In the third module of this course, Learners will learn to apply the Central Limit Theorem in scenarios where data may be improperly distributed. Identify and analyze sample data, using both parametric and non-parametric methods to handle various test cases for hypothesis testing and decision-making.

In the fourth module, learners will explore implementing Exploratory Data Analysis (EDA) on large, complex datasets by conducting both univariate and multivariate analysis. They will also learn how to clean and process data, as well as perform feature engineering to prepare the data for analysis.

In this module, learners will learn how to use machine learning models to extract insights from data. They will apply regression and classification algorithms and then optimize the results produced by these models.

This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz on Probability, Statistical Modeling, and Machine Learning.