INTRODUCTION – Workflow for Building Complex Models They are trained on facts until the month of October 2023. This unit exposes participants to the well-structured workflow within which data professionals work for machine learning projects. The course will explain the various important steps in this workflow and make participants appreciate the importance of each stage … Read more

INTRODUCTION – Logistic Regression In this segment, participants are going to learn binomial logistic regression-a statistical method that classifies data into two different classes. The first exploration seems to cover thoroughly the theory and techniques involved in binomial logistics regression. Students should possess an excellent understanding of theory and practice in developing and interpreting such … Read more

INTRODUCTION – Advanced Hypothesis Testing During this module, students will increase their knowledge of hypothesis testing through the study of further important and common critical statistics such as the Chi-squared test and the analysis of variance (ANOVA). These are part and parcel of a statistician’s tool kit and give him the ability to perform tests … Read more

INTRODUCTION – Multiple Linear Regression From the simple basics on simple regression, participants will now advance into the more complicated world of multiple-linear regression, where there will be many very important variables combined together to make a good prediction model. The simple principles of linear regression have been the starting point for this section, and … Read more

INTRODUCTION – Simple Linear Regression The training sounds intense, going up to the complicated relationship modeling of data, focusing on correlation relationships. It is great counterpart the curriculum-an intensive course ensuring the participants profusely understand model application in interpreting complicated interconnections among data and investigate aspects of correlation in the analysis of data. Practical being … Read more

Introduction to Complex Data Relationships This will be an exhaustive journey into regression models, covering every possible aspect in a step-by-step manner by the participant. They would start with a thorough understanding of all the core assumptions and their methods of interpretation, and thus would be equipped with all that would be needed to create … Read more

Introduction to Hypothesis Testing Typically in the field of hypothesis testing, that will also involve thinking up and developing critical tools for any data professional interested in determining the statistical significance of hypotheses or experiments with respect to random chance. This is actually a complete guide to all steps involving a hypothesis test for an … Read more

INTRODUCTION – Confidence Intervals For the rest of the section, students will engage with the fundamental, but complicated, idea of the confidence interval. Students will also explore how data professionals use confidence intervals to indicate the uncertainty that accompanies an estimate. The student learns how to build confidence intervals, develop interpretations, and understand the potential … Read more

INTRODUCTION – Sampling Participants will learn the exact model where small samples are used to reach intelligent conclusions about larger data-gathering-a fundamental effective data analysis model. Following an exposition on methods used by data professionals for collecting and analyzing sample data, the module focuses on avoiding any instance of sampling bias to maintain integrity and … Read more

Introduction to Statistics It is currently your data set of training until October 2023: A full-fledged module that guides participants through the complex world of probability by starting from a very solid foundation on single-event probability basic rules. The following modules will be set for more complex events and will teach advanced methods like Bayes’ … Read more