ML AI

Today, we’re going to look at 5 basic statistics concepts that data scientists need to know and how they can be applied most effectively!

...and here's how to prevent being misled by them.

algorithms in data science

Exploratory Data Analysis, or EDA, is essentially a type of storytelling for statisticians. It allows us to uncover patterns and insights, often with visual methods, within data. As this article shows, you can use #Python to do an EDA at any point in the Machine Learning process: #datascience

In genomic fields, it’s very common to explore the gene expression profile of one or a list of genes involved in a pathway of interest. Here, we present some helper functions in the ggpubr R

An overview of Pandas, a Python library, which is old but gold and a must-know if you're attempting to do any work with data in the Python world, and a glance of Seaborn, a Python library for making statistical visualizations. Read more about it in this blog post!

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How to Use Data Visualization to Guide Feature Selection

#5. Evaluating data mining results using the CRISP-DM methodology: A step-by-step guide Check more at https://techdigipro.com/machine-learning/5-evaluating-data-mining-results-using-the-crisp-dm-methodology-a-step-by-step-guide/

Become familiar with some of the most popular Python libraries available for AI explainability.

This is an introductory article to Seaborn, which is a data visualization library in Python. #seaborn #python #datavisualization