In this guide you will learn to implement polynomial functions that are suitable for non-linear points. You will be working in R and should already have a basic knowledge on regression to follow along.

When you have feature points aligned in almost a straight line, you can use simple linear regression or multiple linear regression (in the case of multiple feature points). But how will you fit a function on a feature(s) whose points are non-linear? In this guide you will learn to implement polynomial functions that are suitable for non-linear points. You will be working in R and should already have a basic knowledge on regression to follow along.

A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.

My journey into the vast world of data has been a fun and enthralling ride. I have been glued to my courses, waiting to finish one so I can proceed to the next.

This video on Data Manipulation in R will help you learn how to transform and summarize your data using different packages and functions. You will use the dplyr package to select, filter, arrange, and mutate data. You will use the tidyr library to create tidy data. You will look at functions such as gather, spread, separate, and unite. Let's begin!

Learn the essential concepts in data science and understand the important packages in R for data science. You will look at some of the widely used data science algorithms such as Linear regression, logistic regression, decision trees, random forest, including time-series analysis. Finally, you will get an idea about the Salary structure, Skills, Jobs, and resume of a data scientist.

Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.