
One of the most critical aspects of the data science approach is our perception of getting the information processed. In developing insights from our accumulated data, we dig out the possibilities. And those possibilities are known as statistical analysi…

This “Linear Regression” Tutorial by Great Learning would help you to comprehensively learn all the underlying concepts of Linear Regression. You will also learn how to implement linear regression in both R language and Python language

Learn R programming essentials: scripting, statistical tests, loops, functions, linear models, and R Markdown in this comprehensive workshop for beginners.

Learn metabolomics data analysis, including technologies, statistics, pathway analysis, and database exploration. Gain practical skills through tutorials and lectures on this emerging field.

Explore research designs, methods, and data analysis techniques for effective business management, including work-life balance considerations.

Learn key concepts and tools for metabolomics analysis, including software, databases, statistical methods, and data integration techniques for advancing research in this field.

Explore survival analysis and clinical omics for personalized medicine, delving into cutting-edge approaches in genomics and biomarker discovery.

This video explains Linear Progression for Data Science with a variety of demonstrations & examples to help you learn this topic effectively and efficiently.

This tutorial covers AI and its key functions and concepts with a variety of demonstrations & examples to help you get started on the right foot.

This video teaches Linear Regression and its key functions and concepts with a variety of demonstrations & examples to help you get started on the right foot.

This video teaches Data Science and its key functions and concepts with a variety of demonstrations & examples to help you get started on the right foot.

Learn to apply Chi-Square tests for categorical data analysis, including hypothesis testing, p-value interpretation, and implementation in Excel, Python, and R. Gain practical skills for statistical inference.

Comprehensive introduction to Analysis of Variance (ANOVA), covering key concepts, types, and applications in statistical analysis. Includes practical examples and comparisons with other statistical tests.

Learn Random Forest Regression: its advantages, disadvantages, algorithm, and applications. Compare with logistic regression and explore its impact in machine learning across various domains.

In this video, the we will walk you through a case study on Customer data of a financial institution. The objective of the case study is to reduce expenses by targeting only those customers who are likely to subscribe using predictive modeling.