Data Science using R


Data science is an emerging field that has gained popularity over the past few years. It involves the collection, analysis, and interpretation of large amounts of data. We will start by learning about the different types of data sets available and then move on to explore some of the most common statistical methods used in data analysis. This course will introduce you to the basics of data science using R, which is an open source programming language for statistical computing and graphics. It allows you to perform various tasks in statistics, machine learning, and visualization. By the end of this course, you will be able to perform basic data analysis tasks such as creating graphs, performing descriptive statistics, and building predictive models.

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Introduction to Data Science using R

In this course, we will cover the following topics:
• What is data science?
• Types of data science
• Uses of data science
• How to use R
• Introduction to R

Types of Data Science using R

Data science is an interdisciplinary field that combines statistics, computer programming, mathematics, and other quantitative disciplines with domain knowledge. It involves the collection, curation, analysis, and visualization of large amounts of data. Data science has become increasingly popular as more businesses collect and analyze data to make better decisions.

Uses of Data Science using R

There are several ways to use data science using R. You can use it to explore data, build models, visualize results, and perform statistical analyses.

Data Cleaning

In order to make sense of large amounts of data, we need to clean it up first. This includes removing any errors, duplicates, and other anomalies.


We will use the iris dataset to demonstrate how to visualize data using ggplot2.

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