# LET'S DO SOME DATA WORK.

Welcome to Just into Data, a place for data science made simpleR!

Enjoy data science articles on various topics such as Machine Learning, AI, Statistical Modeling, Python Programming.

### Introducing Statistics for Data Science: Tutorial with Python Examples

Learn Critical Statistics concepts

This is a tutorial for statistics for data science. Learn essential statistical concepts as a data analyst or data scientist with this practical guide.

### Learn Python Pandas for Data Science: Quick Tutorial

Examples for all primary operations of DataFrames

This is a quick tutorial to learn Python pandas for data science, machine learning. Learn how to better manipulate and analyze data with this guide.

### Python NumPy Tutorial: Practical Basics for Data Science

Learn NumPy arrays with Examples

This is a beginner-friendly tutorial of Python NumPy (arrays) basics for data science.

Learn this essential library with examples.

### Logistic Regression for Machine Learning: complete Tutorial

Understand this popular Supervised Classification Algorithm step-by-step

This is a complete tutorial for logistic regression in machine learning. Learn the popular supervised classification predictive algorithm step-by-step.

### Linear Regression in Machine Learning: Practical Python Tutorial

With examples using Python scikit-learn

This is a complete tutorial to linear regression algorithm in machine learning.

Learn how to implement simple and multiple linear regression in Python, step-by-step.

### Machine Learning for Beginners: Overview of Algorithm Types

Start learning Machine Learning from here

In this beginners’ tutorial, we’ll explain the machine learning algorithm types.

Following this guide, you can break into machine learning by understanding:

– What is machine learning, in simple words.

– What are supervised, unsupervised, and reinforcement learning.

– 10 commonly used machine learning algorithms.

In the end, you’ll gain an overview of machine learning (ML) and when to use these algorithms when practicing ML.